Volume 12 Issue 2

A Review on Effects of Different Nanofluids on Trapezoidal Corrugated Channel

Authors- M.Tech Scholar Abhishek Ramteke, Professor Dr. Sanjay Kumar Singh

Abstract- Heating/cooling processes using different techniques are essential in many industrial applications such as production and electronics processes, and consequently, all machines require reducing or increasing heat transfer. Despite the high pressure drop caused by turbulent flow in complicated channels, the use of corrugation in heat exchangers has received a lot of attention during the last two decades. Therefore, corrugated surface technique is the promising method to intensify the thermal performance and compactness of heat transfer instruments in some cooling system applications, especially in compact plate heat exchangers. Corrugated walls depend on manipulating the fluid direction of the main flow, which helps induce recirculation flow vortexes that enhance heat transfer. On the other hand, the use of traditional fluids in this technique has been limited due to their somewhat lackluster thermal properties. Therefore, the poor thermophysical properties of these liquids are considered as the major obstacle to improving heat transfer processes. The introduction of nanoparticles into conventional liquids offers a viable solution to improving the thermal properties of these liquids.

Securing Smart Homes: Challenges and Solutions in Iot Cybersecurity

Authors- Nandhini PK, Professor Dr. Febin Prakash

Abstract- The emphasis these days is increasingly on utilizing intelligent loT systems coupled with smart houses tomodernize and ensure the level of living. These contain a few security holes that could open them up to outside assaults. People are more comfortable using smart devices and switching from a mechanical to an automated way of life when these problems are anticipated. Nowadays, there is interest in testing and identifying ways to stop different kinds of assaults. A variety of programs are available to check if a home system is secure. This study examines smart home applications with an emphasis on low-power wireless technology security issues[1].In order to solve problems and provide new services, sensors and connected computers send data via the Internet (IoT). For instance, IoT is used in smart houses. Smart locks may be installed; temperature monitoring, smoke detection, and automatic lighting control are all possible with smart home technology. Additional security and privacy issues it raises include the possibility of user data being accessed by surveillance devices or phony fire alarms. Numerous attacks of different kinds can target smart homes. IoT is emphasized in this survey. The design, objects, and standards of the IoT are discussed.. Researchers look at IoT- based smart home challenges and provide answers in this article.[2] In everyday life for humans exhibits the use of (IoT). From wearable wrist watches to autopilot automobiles, the need for IoT is expanding at an incredible pace. Considerable progress in smart home technology has also been made to enhance living standards .However, security and privacy of IoT devices in smart home networks have become a major concern due to data transmission between smart devices and the Internet. An adversary may break the security of smart homes using a variety of tactics, including malware, boot phase assaults, sniffer attacks, and node capture operations. The security breach hinders the provision of smart home services and negatively affects the privacy of the renters.[3].

DOI: /10.61463/ijset.vol.12.issue2.124

In Smart Cities, Smart Grid

Authors- Epah Juaris, Professor Dr. Febin Prakash

Abstract- Thanks in large part to electrical and mechanical revolutions, the need to build a robust and inexpensive framework has never been more important than it is today. We are creating a biological system that connects the real and virtual worlds by implementing a clever foundation. Now that these astute cities are here, we can enable our clients—or clients—or even territories—to use data and analytics to make decisions. We’ll mention as the present standard astute cities. As it were, clever framework developments can help construct future smart cities. Since the terms “unused ordinary,” “another typical,” and “post-coronavirus world” have become widely used, the state’s “shrewd framework” will be the next unused ordinary of our cities. Our economies and society needed to be reformed at the same time. Customers’ demand for electricity has grown significantly as a result of the development of swift and efficient technologies. The global population’s increasing tendency toward urbanization has put more pressure on public and commercial sectors to enhance the global distribution of electrical power. An important part of smart grid technology is power distribution and generation. Smart grid technology also lowers costs and increases accessibility for the typical user in the distribution and allocation of electrical power. With the demise of the traditional electrical grid, a new and efficient means of transferring electricity has surfaced. It is now quite easy to collect client data for effective resource allocation by using information technology tools and applications. Measurement of an electrical power grid is made easier by the notion of the phasor measuring unit, sometimes called the advanced metering infrastructure. The computational intelligence that smart power promises can only be realized by combining components such as an analysis of the entire electrical spectrum, from electrical generation to substations to distribution, as well as consumer and feedback loops. The energy industry can easily and swiftly enter a new age when all of these actions are taken into consideration. The intelligent grid is run via a bidirectional automation system. Common potentials include the smart grid system’s advanced distributed inexhaustible or continuous energy resource creation and storage. Modern cities lack two crucial elements that smart cities may offer: economic development and a high standard of living. Enhanced liveability, sustainability, and efficiency are the objectives of smart cities. To put it another way, a smart city can oversee upkeep tasks, assist in resource optimization while keeping an eye on security issues, and monitor and integrate the operation of all vital infrastructure, such as roads, tunnels, aviation, waterways, railroads, communication, power supply, etc. This study does more than just offer a comprehensive review of the most current advancements in smart grid research and development. Furthermore, we will enumerate the attributes of electrical power distribution companies and the smart grid in this essay. Key qualities that are common across systems and the necessity for change will also be covered. Conversely, we will all be able to witness firsthand how smart grid technologies may be used in smart cities—the cities of the future. A thorough introduction to the recently developed idea of smart cities is given in this article. Researchers can utilize it to become acquainted with the variety of research opportunities that exist in this application domain. For the most part, the smart city remains a notion, and scholars and practitioners are still at odds over a precise and uniform definition. In a word, a smart city is a place where information, digital, and telecommunication technologies are used to improve the operations of the city for the benefit of its residents. This results in traditional networks and services being made more flexible, efficient, and sustainable. Smart cities have improved speed, friendliness, safety, and environmental friendliness [1].

Machine Learning-Based Rice Fungal Disease Management

Authors- M.Tech. Scholar Richa Sharma, Assistant Professor Aarti

Abstract-Rice is one of the most important staple crops in the world, feeding billions of people every day. However, rice cultivation is threatened by various fungal diseases that cause significant losses in yield and quality. In recent years, machine learning (ML) techniques have been applied to develop decision support systems (DSS) for crop disease management. In this study, we present the development of an ML-based DSS for rice fungal disease management. The system was trained using a dataset of images and associated metadata of rice plants infected with various fungal diseases. The DSS was designed to provide real-time diagnosis and management recommendations based on the input image of a diseased rice plant. The accuracy of the DSS was evaluated using a test dataset of images of rice plants with known fungal infections. The results show that the ML-based DSS can accurately diagnose and provide management recommendations for various rice fungal diseases. The system has the potential to be used as a tool for rice farmers and agricultural extension workers to manage fungal diseases in rice crops.

DOI: /10.61463/ijset.vol.12.issue2.512

Motor Torque Control and Speed Control by Using Artificial Intelligence Controller

Authors- Yogesh Jamre, Assistant Professor Raghunandan Singh Baghel

Abstract-An increasing number of applications in high performing electrical drive systems use nowadays, squirrel-cage induction motors. This paper describes a simplified method for the speed control of a three phase AC drive using Proportional-Integral controller. The simulation results show that the step response of the model is very fast, steady and able to work in four quadrants, and robustness and high performance is achieved.

Application of Numerical Simulation in Optimization of Material Structure for Manufacturing Drone

Authors- Thanh Cuong Nguyen, Truong Thanh Nguyen, Viet Tuan Luu, Thanh Chung Le, Tat Dat Dong

Abstract-Today, with the development of modern science and technology, flying devices are gradually optimized during use without requiring too much intervention from the pilot. In particular, the emergence of small unmanned aerial vehicles (UAV) is thriving to serve both military and civil purposes. However, to achieve the desired flight characteristics according to mission requirements, optimization in design and manufacturing is essential. Therefore, the article delves into the research and application of numerical simulation software to optimize the structure of composite materials in manufacturing drones. On that basis, find a suitable structure to ensure maximum durability for the shell, as well as uniform stress distribution on the surface of the aircraft shell frame.

DOI: /10.61463/ijset.vol.12.issue2.116

Voyiqbot- A System for Intelligent Travel Recommendations and Bookings

Authors- Manoj V Bhagwath, Sahil Rathee, Navneet Kumar Rai, Akshat Pandey, Jain Dipika Pravin, Asst. Prof. Dr.Febin Prakash

Abstract-Many individuals embark on journeys to new destinations for a variety of reasons, taking into account factors such as the optimal travel route, safety, and accommodation comfort. This innovative system aims to assist users in discovering their perfect getaway by considering their travel timing and various influencing factors. It provides comprehensive support, helping users not only in identifying their next destination but also in seamlessly booking tickets and accommodations for their upcoming trip. This streamlined approach proves particularly beneficial for travelers, eliminating the need to navigate multiple systems daily and allowing for efficient task execution with the assistance of a robotic interface. The system takes into consideration the travel time, traveler interests, available transportation modes, and current travel trends. The objective is to develop a versatile system that not only aids in destination discovery but also facilitates the entire booking process. By simplifying the currently cumbersome organization process, the system aims to foster increased interest in travel among a wider audience. This system intends to empower tourists in planning their trips based on criteria such as budget, preferred tourist spots, or other personalized preferences. To achieve this goal, a comprehensive review of similar systems like TripAdvisor, Priceline, and Expedia Inc was conducted to analyze functionalities, strengths, and weaknesses. The overall process involves three key components: an input process that accepts user search criteria, a system process that utilizes a web crawler to match input with relevant websites, and an output process that displays information aligning with the user’s preferences. In essence, the system employs a chatbot to recommend destinations, suggest accommodations, and facilitate the booking of various modes of transportation, including buses, trains, and flights, thereby enhancing the overall travel planning experience for tourists.

DOI: /10.61463/ijset.vol.12.issue2.117

Improvement the Heat Transfer Rate of Ac Evaporator by Optimizing Materials

Authors- Vishal sironiya, Assistant Professor Khemraj Beragi

Abstract-Air Conditioning is referred to the treatment of air so as to all together control its temperature, moisture content, cleanliness, odor and circulation, as required by occupants, a process, or products in the space. The subject of refrigeration and air conditioning has evolved out of human need for food and comfort, and its history dates back to centuries. The history of refrigeration is very fascinating since every aspect of it, the availability of refrigerants, the prime movers and the developments in compressors and the methods of refrigeration all are a part of it. In the present work the Experimental Investigation for Air Conditioning Condenser to Increase the Heat Transfer Rate by Varying the Tube Arrangement.

A Method for Merkel Tree Oriented Key Management in Cloud Computing

Authors- Pankaj Kumar, Mr. Ankit Navgeet Joshi, Dr. Harsh Lohiya

Abstract-Many businesses store and manage data in the cloud for various purposes. The transmission of client data in the cloud is less secure because of the round-robin loop that checks the integrity of the data. To improve cloud data security, this study proposes an improved Merkle Hash Tree method for a successful recognition model in a multi-owner cloud. To hash a large amount of data, a Merkle Hash Tree uses leaf hubs with hash tags and non-leaf hubs with child hash data databases. Merkle’s Hash Tree provides efficient data planning and, through good design, truly defines the history of the data. The defined method allows you to maintain open access security and a secure cloud storage infrastructure. Data is sent from the owner to the cloud and processed with a private key. The data is stored in the cloud server using the improved Merkle hash tree method. Data records provided by data owners are audited by external auditors and customers are audited when changes are effected using a multi-ownership recognition approach. Authorities that review data usage have succeeded in increasing data usage while addressing safety and security issues. Data authentication plays a critical role in the ability to evaluate advanced content and protect against tampering by internal or external opponents.

Investigating the Efficiency of Sawdust Ash Based Alkaline Flooding for Enhancing Heavy Crude Recovery

Authors- Philip Ogwu, Michael Edwin Asuk, Ikechi Igwe, Emeline Temple

Abstract-The recovery of heavy crude oil presents a difficult challenge in the petroleum industry due to its high viscosity and poor flow characteristics. This work evaluated the performance of locally-formulated alkaline in reducing the viscosity of heavy crude, thus facilitating its extraction. A sufficient amount of sawdust was gathered and burnt and the ash recovered. The recovered sawdust ash (SDA) was dissolved in water to formulate the alkaline solution. An enhanced oil recovery experiment was conducted using the locally formulated alkaline and carbon dioxide as the driving gas. The setup involved CO2 cylinder connected to a 10-litre tank, functioning as a reservoir for crude oil. The experimental results provided valuable insights into the feasibility of employing locally-based alkaline flooding for heavy crude recovery. The viscosity of the crude oil was evaluated as the volume of the alkaline solution (SDA) and NaOH were increased. Increase in the volume of both SDA and NaOH applied to the crude oil resulted to a consistent and effective reduction in viscosity of the heavy crude, lowering the viscosity from 0.69cst at 0ml to 0.36cst for sawdust and 0.340cst for NaOH at 25ml. While the scope is limited to laboratory experiment, the findings have the potential to inform and improve field applications in heavy crude reservoirs, offering a sustainable and cost-efficient approach to enhanced oil recovery. This research contributes to the knowledge base on heavy crude oil recovery methods, emphasizing the practical application of locally-available resources for enhanced oil production while minimizing the negative environmental impact.

Software Development Life Cycle

Authors- Harsh Agarwal, Dr. V. Srinath

Abstract-Software Development Life Cycle [SDLC] is a process that consists of a series of planned activities to develop or alter the Software Products. There are various SDLC models widely used for developing software. SDLC models give a theoretical direction for development of the software to the developer. SDLC models are very important for developing the software in a systematic manner such that it will be delivered within the time deadline and should also have proper quality. Employing proper SDLC allows the project managers to regulate the whole development strategy of the software. Each SDLC has its advantages and disadvantages according to which we decide which model should be implemented under which conditions. For this we need to compare SDLC models. This paper will describe how software development is done, what are the phases of software development and what are the requirements of software development and what are the different difficulties faced during the software development and How we can enhance the development cycle.

Implementing a Vote kick System in Video Games Through Blockchain Technology

Authors- Ankush Singh

Abstract-The video game industry has grown exponentially over the years, with the advent of new technologies and platforms. However, as the industry has grown, so have the issues that plague multiplayer gaming, such as cheating, hacking, and trolling. To address these problems, many games have implemented vote kick systems, which allow players to vote to remove another player from the game. However, these systems can be easily abused, leading to unfair or even malicious use. By leveraging block chain technology, we aim to create a more secure and decentralized system that can effectively prevent the misuse of the vote kick system. The transparency and immutability of block chain can ensure that all votes are recorded and stored securely, making it difficult for any individual to manipulate the voting process. We plan to do this by implementing a simple demo using the C# Language, Unity 3D game engine and Desonity SDK for DeSo Block chain and develop a vote kick system to showcase the benefits of a block chain-based vote kick system. As the industry continues to evolve, it will be interesting to see how block chain and other emerging technologies will shape the future of video games.

Design and Fabrication of Robot for Assistance in Rescue Operations during Flood

Authors- Alkesh Anil, Brahmajith P V, Aleena Achenkunju, Nayan S Kurup

Abstract-Floods are one of the most common natural disasters that can sometime become extremely difficult to control. Rising sea levels, climate change and dependency on river basins or coastlines will increase frequency and probability of flooding in many regions of economic activity worldwide in coming decades. Existing rescue operations risks life’s of rescuers and doesn’t provide quick response. In the face of increasing frequency and intensity of flood-related disasters, the need for effective and efficient rescue operations has become paramount. In this project, we propose the design and development of a flood rescue robot equipped with a gas sensor, PIR (Passive Infrared) sensor, and ultrasonic sensor to enhance the efficiency and safety of rescue missions. This is a microcontroller based rescue robot for detecting people who are affected by natural calamities. The advantage of this system is it can be use in water with our smart phone control. It is capable of sensing living bodies, gas levels, distance measuring, presence of obstacles under water and live video streaming. Robot can carry emergency medicine as well as food for the affected people, it can also guide rescue team towards affected area with proper safety warnings. The collected sensor data is processed and analyzed, enabling the robot to make informed decisions during rescue missions, such as adjusting its trajectory or prioritizing areas with higher gas concentration or human presence.

DOI: /10.61463/ijset.vol.12.issue2.120

Arising New Trends, Technologies and Applications in IOT

Authors- Gayatri Makrand Ghodke, Assistant Professor Dr. Swati Pramod Mane

Abstract-The Internet of Things (IOT) is a network of physical devices, vehicles, and home appliances embedded with electronics, software, sensors, actuators, and connectivity. It connects the physical world to computer-based systems, transforming our lives into smarter ones. IOT devices collect data from the environment, reducing human effort and improving security. It’s essential for home automation and business, with applications like smart lighting and smart locks. New IOT trends include AI, blockchain, 5G, digital health, edge computing, and more. AI enhances data integrity, blockchain facilitates transparent information sharing, 5G offers faster internet, digital health enhances patient-doctor relationships, and edge computing provides high speed, reliability, and real-time services. IOT applications include smart lightning, smart locks, smart security cameras, smart traffic signals, and smart cities.

AIoT Device for Optic impairment and Minor Cognitive Decline

Authors- Nicholas Ponraj M, Mohnish M, Mohammed Harris J, Priyadharshini V

Abstract-Alzheimer’s disease poses significant challenges for patients, caregivers, and healthcare professionals, necessitating innovative solutions to streamline diagnosis, ensure patient safety, and provide adequate support. This project presents a comprehensive system that integrates deep learning techniques, image processing, and wearable technology to address these critical needs. The core of the system revolves around a user-friendly web application developed using Python and Streamlit, enabling rapid and accurate diagnosis of Alzheimer’s disease stages (Very Mild Dementia, Mild Dementia, and Non-Dementia) through the analysis of MRI scanned images. Leveraging Convolutional Neural Networks (CNNs) implemented with TensorFlow Keras, the application provides healthcare professionals with an efficient tool for diagnosis, mitigating mental fatigue and expediting the diagnostic process. In addition to diagnosis, the system incorporates innovative safety and support features. Utilizing image processing techniques with Python and OpenCV, the system recognizes family members and caregivers interacting with the patient, fostering a sense of security and familiarity, particularly when the patient is alone. Furthermore, the integration of sign-to-speech and real-time object detection enhances communication and awareness for patients with Alzheimer’s. To address safety concerns for patients venturing outside, a wearable device powered by Arduino Nano, GSM, and OLED capabilities is introduced.

A Review on Analysis and Design of a Multi-Storey Building by Using Staad Pro

Authors- Research Scholar Amay Kumar Ranjan, Assistant Professor Dipali Jaiswal

Abstract-A review of the analysis and design of a multi-storey building with STAAD Pro is carried out. Planning is done by using AutoCAD and load calculations were done manually and then the structure was analysed using STAAD Pro. The dead load, imposed load and wind load with load combination are calculated and applied to the structure. Overall, the concepts and procedures of designing the essential components of a multistory building are described. STAAD Pro software also gives a detailed value of shear force, bending moment and torsion of each element of the structure which is within IS code limits.

Improvement WSN Protocol Performance in Modified Genetic Algorithm

Authors- Madhuri Singh Chouhan, Professor Amit Thakur

Abstract-Wireless Sensor Network (WSN) is the deployed randomly and on the far places to sense information. The security, quality of service and energy consumption is the major issues of WSN. To minimize the consumption of higher amount of energy in these networks, clustering is applied. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for the cluster head selection in the network. The cluster head is selected based on the energy consumption and distance to base station. A node is selected as Cluster Head (CH) if it has the highest amount of energy and the least distance from base station. In this research work, genetic algorithm is applied with the LEACH protocol for the cluster head selection. The proposed work is implemented in Matlab. The results of existing LEACH protocol is compared with proposed LEACH protocol in terms of certain parameters. The comparative analysis and achieved outcomes show that the proposed approach performs well in terms of energy consumption as it consumes lesser amount of energy.

Social Media Sentiment Analysis Using Machine Learning and Optimization Techniques

Authors- Neel Doshi

Abstract-Lately, there are emergence and advent of information Inter-non-public interplay internet websites, micro blogs, wikis, similarly to internet packages and information, e.g. tweets and net-postings explicit perspectives and opinions on different topics, issues and occasions in many applications, further to, special domains that includes business, economic system, politics, sociology, and and so forth., which are resulted from supplying massive opportunities for reading and reading human perspectives and sentiment. The goal of sentiment analysis is to categories a speaker’s or Arranging facts into wonderful, negative or impartial categories. Sentiment evaluation method determining the views of a person from the textual content regarding that topic i.e. how one feels about it. It is probably used to classify the text conent. numerous researchers have used a giant type of strategies to train the classifiers for the Twitter dataset with diverse consequences. The studies uses a hybrid approach of the use of Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre- processing can be accomplished by appearing various approaches i.e. Tokenization; elimination of stop-words and emoticons; stemming. Then their function vectors are being made by way of the calculation of TF-IDF and optimized with (PSO) and (ACO) before appearing the binary text categorization. Naïve Bayes and help Vector system can be those gadget mastering technicalities used for the binary category of tweets.

Review Analysis of Improved Topologies of Isolated DC-DC Converters for Electric Vehicle Onboard Chargers

Authors- T.Rajeswari, D.Arjuna, B.Vasu, R.Ram Kishore, S.Bharath

Abstract-As electric vehicles (EVs) gain prominence in the automotive industry, the development of efficient and reliable onboard charging systems becomes increasingly crucial. Isolated DC-DC converters play a pivotal role in these systems, converting high-voltage battery power to levels suitable for charging auxiliary systems. This review paper explores recent advancements in isolated DC-DC converter topologies specifically tailored for EV onboard chargers. It discusses the principles, advantages, and limitations of existing topologies before delving into the design and performance evaluation of innovative topologies aimed at improving efficiency, power density, and reliability. Furthermore, the paper identifies key challenges and future research directions in the pursuit of optimized isolated DC-DC converters for EV applications.

Bluetooth Enabled Walking Cane Integrated with Hearing Assistance

Authors- Parkavi S, Imthiyas B, Ajay S, Jayasudharsan K

Abstract-The innovative assistive technology known as the Bluetooth-enabled walking cane is made to enhance the quality of life for those who are visually impaired. With ease, this cutting-edge cane incorporates cutting-edge technologies including physiological sensors, hearing aids, and a high-definition camera. The cane enhances navigation safety with GPS by leveraging Bluetooth technology to wirelessly connect to hearing aids or ear buds and provide real-time audio cues and directions. By integrating physiological sensors like the SpO2 and pulse sensors, users may monitor vital signs and receive notifications for any anomalies, providing significant health monitoring capabilities. Additionally, the walking cane that provides users with visual information to help them identify things and impediments in their way is being fixed by the ultrasonic sensor This creative walking cane is a major improvement in helping people with vision impairments in their daily lives since it combines accessibility with modern technology.

Review Analysis of Advances and Future Prospects in Electric Vehicle Innovations

Authors- Dr.G.Jaya Krishna, K.Krishna Vamshi, V.Srinivas, P.Chenna Reddy, Nilesh Sahani

Abstract-The transition towards electric vehicles (EVs) represents a pivotal shift in the automotive industry towards sustainable transportation. This review paper offers an extensive survey of recent advancements and future possibilities in EV innovations. It covers a wide range of topics, including battery technologies, charging infrastructure, vehicle design, autonomous features, and sustainable materials. By synthesizing the latest research findings and industry developments, this paper aims to provide insights into the current state of EV technology and potential avenues for future growth and innovation. Additionally, it explores the challenges and opportunities associated with the integration of BLDC motors in the rapidly evolving landscape of electric mobility.

Review of Li-Ion Battery with Battery Management System in Electric Vehicles

Authors- Dr.S.Sivaganesan, D.Prabhakar, B.Praveen kumar, G.Aditya, G.Vinay kumar

Abstract-As electric vehicles (EVs) continue to gain traction as a sustainable transportation solution, the performance and reliability of their energy storage systems, particularly lithium-ion (Li-ion) batteries, are of paramount importance. This review paper provides a comprehensive analysis of Li-ion batteries with integrated Battery Management Systems (BMS) in EVs. It explores the principles of Li-ion battery technology, the functions of BMS, and their combined impact on EV performance, safety, and longevity. Additionally, the paper discusses recent advancements, challenges, and future research directions in the optimization of Li-ion batteries and BMS for EV applications.

Compressed Air Energy Storage for Rubber Industrial Applications

Authors- D.G.A.D. Dissanayake, Dr. K.A.C. Udayakumar

Abstract-This report presents a solution to the power interruption during the period of power transfer from grid supply to the diesel generator during power interruption from the grid. The method is proposed to use in one of the leading gloves manufacturing factories in Sri Lanka which incurs loss of production and hence financial losses during power transfer period. This report proposes to use compressed air energy storage system to provide uninterrupted power supply during the power transfer period. The report provides feasible method to provide uninterrupted supply during power transfer period. These methods utilize compressed air energy storage system as source of energy during the power transfer. The method has been proposed as techno-economical feasible solution to the problem. The elements of the proposed system their features and operation are given in this report. The process with the compressed air energy storage system has been simulated using MATLAB software and the results were presented. As a part of the project, prototype model has been designed and fabricated. All the elements of the prototype design are described in detail. The results of the operation of the prototype model have been presented and both simulation results and the results from the experiments have shown the proposed methods provide a solution to the existing problem of the factory.

Image to Text Extraction

Authors- Sahil Salunkhe, Sahil Bora, Ubaid Karbhari, Vinay Vaisisth

Abstract-Image Text Extraction is a critical interdisciplinary field at the confluence of computer vision and natural language processing. It focuses on extracting and analyzing textual information from images, finding applications in document digitization, content retrieval, and accessibility. Key stages involve image preprocessing, text localization, and text recognition. Challenges include multilingual text, low-quality images, handwritten text, and layout preservation. Researchers continually refine algorithms to enhance accuracy and robustness. Finally, the words are digitized using Optical Character Recognition (OCR).

Evaluation of Fiber-Optic Cable Performance

Authors- R.Divyavarshini, S.Sanjay kumar, F.John Teni Jio, Assistant Professor A.B.Evanjalin

Abstract-This paper discusses the fundamental communication concept as well as the many fibre types used in optical fibre communication systems. The effectiveness of the system will be determined by a few fundamental ideas that have been clarified. A number of parameters have been studied using two wavelengths (1310 and 1550 nm) in order to assess the performance of the digital fibre optic communication system, including the attenuation that results from increasing the length of fibre for single mode (SM) and multimode (MM) fibres, the bending losses, the splice losses, and the analysis of Q-factor and bit error rate (BER). Three different transmission channels—copper wires, radio frequency (RF), and optical fibers—have been contrasted. The channel for transferring the information was faster over optical fibre.

Low-Cost Robotics Using Machine Learning

Authors- D.Allwin Sanjay, Irulandi, J.Jerin, Assistant professor V.Jino Shiny

Abstract-The rise of artificial intelligence (AI) and robotics over the last decade has created a variety of career opportunities in a variety of industries, including robotics. Manufacturing and healthcare are two industries. As a result, skills like AI and robotics will be crucial in the coming years. The goal of this project is to introduce an AI platform for low-cost robotics using Julia. The graphical interface makes it easier to design and implement machine learning functionalities such as object/pattern recognition and classification. The application provides three different types of learning methods: machine learning, deep learning, and transfer learning. The end devices are currently Arduino-based and Raspberry Pi processors. Communication can be either wired or wireless.

DOI: /10.61463/ijset.vol.12.issue2.129

Research and Calculation of Twisting Phenomenon that Destroys Wings on SU-27 Aircraft

Authors- Van Huy Khuat, Truong Thanh Nguyen, Trong Son Phan, Le Phan, Van Quyen Dinh

Abstract-This article presents the results of calculating the torsional limit to failure on the SU-27 aircraft wing. Calculations are performed using diagrammatic analysis and formulas on Matlab software. Present the results of calculating the torsional speed that destroys the wing of the Su-27 aircraft and compare it with the experimental results on the documents on the use of the SU-27 aircraft of the Air Force Officer School. With the help of this method, it is possible to obtain the limit speed value in different flight conditions and then apply it during flight practice at units using this type of aircraft.

The Role of IOT in EV’S

Authors- Ankith M, Rohan V, Shashank m

Abstract-The global push for net-zero emissions and decarbonization has driven the transition toward cleaner energy alternatives. Among the major contributors to carbon emissions, the fossil-fuel-dominated global transportation system accounts for 37% of recent emissions. To combat this, the adoption of electric vehicles (EVs) has surged worldwide, aiming to reduce carbon footprints and enhance transportation efficiency. However, despite this growth, challenges persist in the form of limitations in EV charging infrastructure. These bottlenecks hinder seamless integration into existing transport systems. To address this, systematic approaches are necessary. In particular, the Internet of Things (IoT) plays a critical role in overcoming EV public charging challenges.

Review Analysis of a Survey on Women Safety Device Using GPS and GSM

Authors- Professor Mrs.G.P.Merline, K. Daya, K.Bharadwaj, S. Akash, B. Rahul

Abstract-This paper provides a thorough review of existing technologies designed to enhance women’s safety through the integration of Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies. The growing concern for women’s safety has led to the development of numerous devices aimed at providing real-time tracking, communication, and emergency assistance. This survey explores the key features, limitations, and advancements in such devices, offering valuable insights for researchers, developers, and policymakers in the field of personal safety technology.

WSN Energy Efficient Routing Protocol Implementation based on AI

Authors- Meharban Singh Parmar, Professor Amit Thakur

Abstract-Wireless sensor network (WSN) has emerged as a useful supplement to the modern wireless communication networks. Optimal selection of paths for data transfer results in saving of energy consumption resulting in increase of network lifetime of Wireless Sensor Networks. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Routing protocols in WSNs might differ depending on the application and network architecture as there is still no consensus on a fixed communication stack for WSN. Newer Routing protocols are required to cater to the need of ubiquitous and pervasive computing. In this paper, WSN Routing Protocols has been classified in four ways i.e., routing paths establishment, network structure, protocol operation and initiator of communications. Further, routing protocols have been categorized on the basis of their homogeneity and heterogeneity of sensor nodes followed by the criteria of clustered and non – clustered among both. Data aggregation, support for query and scalability of the network of these routing protocols have also been.

Intrusion Detection System using Blockchain-Enable Technology

Authors- Professor Jyotsna Kadam, Professor Sneha Tirth, Professor Sai Takwale, Dr.Geetika Narang

Abstract-Intrusion detection is a critical aspect of cybersecurity.Intrusion detection system has vital role in detecting malicious cyber attacks.Distributed system provides powerful security through features of novel IDS. It involves monitoring networks and systems for signs of unauthorized or malicious activities. Intrusion detection systems using blockchain reduces traditional vulnerabilities as well as novel threats and data loss. IDS use techniques like anomaly detection and signature-based methods to identify potential threats. When suspicious behavior is detected, alerts are generated, allowing security teams to respond swiftly. IDS plays a vital role in safeguarding digital assets and maintaining the security of information systems.

Computational Fluid Dynamics Analysis of Flow Characteristics in Convergent and Divergent Sections

Authors- Joseph B. Bassey, Dominic D. Ekpo, Victor U. Gentle

Abstract-In this paper fluid flow characteristic in reduction section of a pipe system is analyzed. Two reduction sections type, known as the concentric and eccentric reducers are considered. The basic flow characteristics considered are pressure and velocity. According to Bernoulli’s theorem, these parameters (pressure and velocity) vary with respect to one another as fluid flows through reduction section in a pipe system. This variability arises because the Bernoulli’s theorem is predicated on the continuity law. The behavior of fluid in these sections is analyzed using a CFD approach. Here, the fluid flow characteristic in the two reducer types is visualized using ANSYS Fluent. The results obtained show that the flow behavior through these two sections differs slightly and the tapered angle of the reducers has significant effect on the flow behavior despite the equivalent dimension of the inlet and outlet diameters and length.

Addressing Student Grievances: A Comprehensive Analysis of Grievance Management Systems

Authors- Harshil Sheth, Siddhraj Solanki, Aayush Parmar, Jay Dave, Asst. Prof . Hiren Raithatha

Abstract-This paper delves into the intricate realm of addressing student grievances within educational institutions, emphasizing the significance of effective management strategies. It examines a diverse array of grievances that students may encounter, ranging from academic disputes and interpersonal conflicts to institutional policies and procedural challenges. By analyzing the multifaceted nature of these grievances, the study aims to provide comprehensive insights and actionable recommendations for educational institutions. The research emphasizes the direct correlation between addressing student grievances and enhancing student well-being, academic success, and institutional excellence. Through a detailed examination of case studies and best practices, the paper offers practical guidance on how schools and colleges can cultivate a positive and supportive learning environment by addressing student concerns proactively.

Intention to Stay Work, Factors to Participate in Work at Some Beauty SPA in HO Chi Minh City

Authors- Nguyễn Văn Nhân, Hồng Thị Thu Trang

Abstract-This research was conducted at small and medium-sized cosmetic spas (in this article collectively referred to as small and medium-sized enterprises) in Ho Chi Minh City, Vietnam, to provide more detailed information about the factors Work engagement affects employee engagement. with the job and intend to stay and work at the enterprise. With the basic theory of Mitchell et al. (2001), the author conducted a direct survey of employees at the enterprise and obtained a valid survey with n=300 tables. The software used is SPSS 20.0 and AMOS 20.0 to process data. As a result, the factors of Job Embeddedness all have a direct impact on the level of engagement with Work, of which the strongest order is Sacrificial Organization (SO) with beta = 0.384. The second is Organizational Fit (FO) with beta = 0.369. Third is Organizational Linkage (OL) with beta=0.335. Sacrifice when leaving the organization directly impacts the intention to stay with the organization, with a regression coefficient of 0.126, this level of influence is quite low and almost insignificant. The standardized regression coefficient between job engagement and intention to stay with the organization is 0.597. The solutions proposed by the author group are intended to help business leaders have a deeper insight into the current state of the business and make appropriate decisions for operations, in order to improve and enhance cohesion of employee with business.

DOI: /10.61463/ijset.vol.12.issue2.121

Working and Challenges in Glucometer

Authors- Sushmita Bist, Ayush Raiyani

Abstract-The increasing prevalence of diabetes globally emphasizes the critical need for effective strategies in glycemic control to reduce complications and healthcare costs. This paper delves into the current landscape of diabetes management, with a particular focus on the advancements and challenges surrounding glucometer technology. The paper emphasizes the need for continuous research and innovation in glucometer technology to enhance accuracy, reliability, and user acceptance. It aims to contribute to optimizing glycemic control and improving outcomes in diabetic care through a comprehensive analysis of glucometer advancements and challenges.

A Efficiency Enhancement of Solar Based HVAC System

Authors- Ashish Pandey, Assistant Professor Khemraj Beragi

Abstract-Solar energy converts the renewable energy to increase growth. The development of energy in to generated worldwide. It in the most easy to construct the process of solar air Conditioning systems. The different energy are involved into the solar air conditioning to the decreasing current Sources Then using high oil and concern environmental effects have been Controlling. The most comfortable Process of the solar energy system. It we implemented nowadays, increase in progress. we are air Conditioning systems are using in. every building, malls, Colleges industries, flats, etc. solar power air conditioning system is the hot issue to study building energy Consumer. To increasing the efficiency of air Conditioning intensity of the cooling system. The transient thermal efficiency we can using the storage system to maintain the temperature to indoor, The solar system must be using the different.

Cloud Gaming Application Using Artificial Intelligence

Authors- Harshit Agarwal, Professor Dr. Febin Prakash

Abstract-Cloud gaming is an emerging field offering convenient access to high-quality gaming experiences. However, minimizing latency remains a challenge, particularly across diverse network conditions. This paper proposes a novel approach utilizing artificial intelligence (AI) to address these challenges. Our system employs AI algorithms to optimize various aspects of cloud gaming, including video encoding, network routing, resource allocation, and user interaction prediction. Machine learning models dynamically adjust streaming parameters based on real-time network conditions and user preferences, reducing latency and improving visual quality. Additionally, reinforcement learning optimizes server-side resource allocation, enhancing scalability and cost-effectiveness. The integration of AI-driven predictive analytics enables personalized gaming experiences by anticipating user actions and adapting gameplay dynamics accordingly. By analyzing player behavior patterns and preferences, the system tailors game content, difficulty levels, and in-game assistance. Experimental results demonstrate the efficacy of our AI-enhanced cloud gaming system in delivering superior performance compared to traditional approaches. Through comprehensive evaluations, the system exhibits significant improvements in latency reduction, band width efficiency, resource utilization, and user satisfaction metrics.

DOI: /10.61463/ijset.vol.12.issue2.122

Near Field Communication (NFC) in Computer Networks

Authors- Nikhil N Kumar, Shreyas U V, Suprit Babu N, Dr. Jayasheela C S

Abstract-Near Field Communication (NFC) has emerged as a powerful technology within computer networks, enabling short-range wireless data exchange between devices. This paper explores NFC’s technical foundation, its diverse applications across various sectors, and the security and privacy considerations associated with its use. The paper delves into the operating frequency, data rates, and underlying principles of inductive coupling that facilitate communication between NFC devices. It explains the roles of active and passive devices, communication protocols, and the importance of Secure Elements in safeguarding sensitive information. Furthermore, the paper explores the extensive range of NFC applications, including mobile payments, identity and access control, data exchange, smart object interaction, marketing and advertising, entertainment, and even medical applications. Each application highlights the potential of NFC to bridge the gap between the physical and digital worlds. Security and privacy concerns surrounding NFC are also addressed, discussing potential vulnerabilities like eavesdropping and data skimming. The paper emphasizes the security measures in place, such as encryption and mutual authentication, to mitigate these risks. Additionally, it explores the importance of user awareness and responsible data practices. Looking towards the future, the paper examines emerging trends like the Internet of Things (IoT) and smart cities, where NFC is poised to play a crucial role. Advancements in NFC-v technology and higher data rates promise to further enhance capabilities and drive wider adoption. However, challenges related to device compatibility, security concerns, and evolving privacy regulations need to be addressed.

Remote Desktop Applications in Education

Authors- Soumya Singh, Shreya Srivastava, Sidharth Premdas

Abstract-This research paper investigates the role and impact of remote desktop applications in the realm of education. Focusing on the intersection of technology and learning environments, the study explores how these applications facilitate virtual classrooms, remote collaboration, and educational resource accessibility. The paper begins by examining the historical context of remote desktop applications in education, tracing their evolution from supplementary tools to indispensable components of modern online learning ecosystems. It analyses the pivotal role played by these applications in bridging geographical gaps and providing students and educators with flexible, remote access to educational resources. Security and privacy concerns in the educational context are scrutinized, emphasizing the need for robust measures to protect sensitive student data and ensure a secure online learning environment. The research investigates authentication protocols, data encryption, and compliance with educational privacy standards. User experience takes centre stage, with an in-depth exploration of how remote desktop applications influence the learning process. The study assesses the effectiveness of these tools in fostering student engagement, collaboration, and interaction within virtual classrooms. Attention is given to considerations such as interface design, ease of use, and adaptability to various educational settings. Comparative evaluations of remote desktop applications tailored for education form a critical component of the paper. The analysis delves into features specifically designed for educators, administrative capabilities, and scalability to accommodate varying class sizes and academic disciplines. Additionally, the paper explores innovative uses of remote desktop applications in education, including virtual laboratories, collaborative project work, and access to specialized software. The potential integration of emerging technologies like augmented reality for immersive learning experiences is also considered. In conclusion, this research paper aims to provide a comprehensive understanding of the multifaceted impact of remote desktop applications on education. By addressing historical evolution, security considerations, user experience, and emerging trends, the study offers valuable insights for educators, administrators, and developers seeking to optimize the use of remote desktop applications in the dynamic landscape of contemporary education.

Enhancing Road Safety with Advanced Sensor Technology and Mobile Integration: Utilizing Gyroscope and Airbag Sensors for Accurate Accident Detection in the Car Accident Alert System

Authors- V Varun Reddy, Mohammed Abrar, Shreyas P, Suprit Babu

Abstract-The Car Accident Alert System (CAAS) is a groundbreaking initiative in the pursuit of zero traffic fatalities. It leverages advanced technology to establish a secure environment for drivers, envisioning a future where every drive is safer than the previous one. This system integrates Arduino components, sensors, and a mobile app to detect automobile accidents and promptly alert emergency services and family members. The essential elements of the CAAS include a gyroscope and car airbag sensors, which function in tandem to detect any sudden changes in the vehicle’s orientation and impact, indicative of a potential accident. In the event of an accident, the system sends an alarm signal to the Arduino Bluetooth module, which then transmits a distress message to a pre-connected mobile phone. The mobile app created for the CAAS is vital in facilitating Bluetooth data transfer, tracking the vehicle’s current GPS location, and sending messages to emergency services and family members, while also managing false detections. This app offers real-time updates and enables users to respond swiftly to emergencies, significantly reducing response times and potentially saving lives. The CAAS app prioritizes user-friendly design, ensuring a smooth and frictionless experience for drivers, maximizing critical response times.

DOI: /10.61463/ijset.vol.12.issue2.123

Microbial Dynamics of Lactic Acid Bacteria in Human Breast Milk

Authors- Assistant professor Dr. Shradhdha Mansukhlal Gondaliya

Abstract-Lactic acid bacteria (LAB) are integral to human health, particularly in modulating gastrointestinal function and immune responses. Human breast milk, recognized as a vital source of LAB for newborns, contains a diverse array of nutrients and bioactive compounds essential for infant growth and development. This study aimed to isolate and characterize LAB from breast milk samples obtained from lactating donors of varying ages and lactation periods. LAB were isolated using selective media, and primary screening was conducted based on colony morphology, gram characteristics, catalase activity, and motility. Results demonstrated significant variability in LAB distribution among donors, with higher counts observed during early postpartum periods. While mean colony-forming unit (CFU) counts decreased with advancing lactation periods, no direct correlation was found between donor age and LAB counts. The findings underscore the importance of understanding breast milk micro biota dynamics for infant health and development, necessitating further research in this field.

Bud Yield Detection and Quality

Authors- Padma Priyanka, Vedant Kumbhare, Zuveb Kamdoli, Pranay Wagh, Dr. Arvind Jagtap

Abstract-The abstract presents a concise overview of the study on bud yield detection, highlighting its objectives, methodology, findings, and potential implications. Bud yield detection is crucial in optimizing agricultural practices and ensuring efficient crop management. This study aims to develop a reliable and efficient method for accurately assessing bud yield in various plant species. The methodology involves the utilization of advanced technologies such as computer vision and machine learning algorithms. High-resolution images of plants are captured at different growth stages, focusing on bud development. These images are then processed to extract relevant features and characteristics of buds. Machine learning models are trained using these features to predict bud yield based on the observed patterns and relationships. The findings of this study reveal a strong correlation between the extracted features and the actual bud yield of the plants. The developed machine learning models demonstrate a high degree of accuracy in predicting bud yield, providing a valuable tool for farmers and researchers to assess and manage crop production. By accurately estimating bud yield early in the growth cycle, farmers can make informed decisions about resource allocation, irrigation, fertilization, and harvesting schedules. The implications of this research are significant for sustainable agriculture and food security. The ability to predict bud yield with precision contributes to reducing wastage, optimizing resource usage, and increasing overall crop productivity. Additionally, this study paves the way for the integration of technology-driven approaches into traditional farming practices, bridging the gap between modern innovation and age-old cultivation methods.

DOI: /10.61463/ijset.vol.12.issue2.130

Password Based Circuit Breaker

Authors- Mr. S. Radha Krishna Reddy, B. Srikar, T. Naveen Kumar, V. Kiran, CH. Vishal, P. Vishnu Vardhan

Abstract-When operated manually we see fatal electrical accidents to the line man are increasing during the electric line repair due to the lack of communication and coordination between the maintenance staff and the electric substation staff. In order to avoid such accidents, the breaker can be so designed such that only authorized person can operate it with a password. This ensures security of the worker because no one can turn on the line without his permission. The system is full controlled by the 8-bit micro controller of 8051 family. A keypad is used to enter the password and a relay to open or close circuit breaker, which is indicated by a lamp.

A Comprehensive Review on Life Cycle Optimization of Residential Air Conditioner Replacement Using AI

Authors- Heeru Baghel, Assistant Professor Khemraj Beragi

Abstract-The aim of this paper is to present a general bibliographic review about recent scientific papers focused on the design and operation of air conditioning systems in green and smart buildings. The suggested review is developed using tools offered by the Scopus academic research directory, with a defined search criteria. Furthermore, the VOS viewer science biblio metric analysis software has been used. Nowadays, increasing energy efficiency and decreasing carbon footprint of current and future buildings, both green and smart is considered more important. The most frequent fields of research in scientific contributions related to the cooling of green buildings are: sustainable development, energy efficiency and the construction industry; while in smart buildings they are: energy efficiency, smart grids, energy management.

Developing an Optimal Model for Predicting the Severity of Wheat Stem Rust (Case study of Arsi and Bale Zone)

Authors- Tewodrose Altaye

Abstract-This research utilized three types of artificial neural network (ANN) methodologies, namely Back propagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such as mean maximum temperature, mean minimum temperature, mean rainfall, mean average temperature, mean relative humidity, and different wheat varieties. The statistical analysis revealed that GRNN demonstrated effective predictive capability and required less training time compared to the other models. Additionally, the results indicated that total seasonal rainfall positively influenced the development of wheat stem rust.

Novel Approach to Load Analysis of Multistory Building with its Bending

Authors- Research Scholar Aman Singh Bais, Professor Rajesh Chouhan

Abstract-A multi-storey is a building that has multiple floors above the ground. It can be a residential or commercial building. In this project the analysis and design of multi-storey building. In general, the analysis of multi-storey is elaborate and rigorous because those are statically indeterminate structures. Shears and moments due to different loading conditions are determined by many methods such as portal method, moment distribution method and matrix method. The present project deals with the analysis of a building. The dead load & live loads are applied and the design for beams, columns, the footing is obtained manually.

Enhancement of Load Bearing Capacity in Diagrid Multistory Building with Observed Torsion

Authors- Research Scholar Yawar Khan, Professor Sachin Sironiya

Abstract-Up to the present day, various developments have been arisen to improve the performance behavior of buildings. Diagrid system is an innovative structural system for high rise structures with its lattice like aesthetics and high-efficiency structural performance. Literally, the word “diagrid” is made of two words “diagonal” and “grid”. In this system, all exterior vertical columns are eliminated in contrast to conventional structural systems such as braced frames and framed-tube systems. The diagrid frame is subdivided into repetitive modules along the height and forms a diamond-shaped structure.

Cyber Security for Critical Infrastructures of a Nation

Authors- Pradnya Kashikar, Shivanand B Padiyar

Abstract-Cybersecurity is the backbone of implementing secure network; controlling access, usage and data; monitoring any unauthorized intrusion or suspicious activity to safeguard confidentiality, integrity and availability of critical data from unauthorized intrusion.

DOI: /10.61463/ijset.vol.12.issue2.126

The Influence of Emerging Technologies on the Socio-Economic Background and Behavioral Patterns of Indian Youth

Authors- Assistant Professor Dr. Pritichhaya Tamboli

Abstract-Emerging technologies have a transforming and challenging effect on the young of India. There is a tremendous opportunity for innovation, economic progress, and enhanced quality of life as the country rapidly digitises. But there are still obstacles in the way of this advancement, such as the need for ongoing skill development, resolving employment inequities, and guaranteeing fair access to opportunities. Collaboration among educational institutions, governments, and industry stakeholders is crucial in cultivating a youth population that is tech-savvy, adaptive, and morally aware. India can enable its youth to flourish in the ever-changing landscape created by emerging technology and make a substantial contribution to the country’s development by carefully navigating these hurdles.

Basic Fundamentals of the Mathematics

Authors- Assistant Professor V Shilpa

Abstract-In this paper it was clearly explained that general fundamentals of the mathematics. Mathematics is the science and study of quality, structure, space, and change. Mathematicians seek out patterns, formulate new conjectures, and establish truth by rigorous deduction from appropriately chosen axioms and definitions. Mathematics provides an effective way of building mental discipline and encourages logical reasoning and mental rigor. In addition, mathematical knowledge plays a crucial role in understanding the contents of other school subjects such as science, social studies, and even music and art.

Intrusion Detection and Prevention Using CNN-LSTM

Authors- T Sai Harshitha, V. Sreenidhi, Sk. Parveen, P Tejaswini, Assistant Professor Yerininti Venkata Narayana

Abstract-The rapidly evolving landscape of digital interactions, the persistent threat of intrusion attacks poses significant challenges to the security and stability of computer networks. The effects of such attacks are multifaceted, ranging from service disruptions and data breaches to the compromise of sensitive information and financial losses. The project, titled “Detection and Prevention of Intrusion Using CNN-LSTM” introduces an advanced method for network security. By combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, the model can effectively detect and prevent intrusions. The study involves training the model with diverse datasets containing both normal and malicious network behaviors. The CNN part focuses on spotting spatial patterns, essential for identifying specific types of attacks, while the LSTM part captures time-related patterns in network traffic. The integrated model works in real-time, keeping a constant eye on network activities and proactively blocking suspicious actions. Deep learning models like this can adapt to new cyber threats over time. The project focuses on three attacks namely: DoS (Denial of Service), Malware, Portscanning. The research includes experiments on popular datasets, and its practical implementation involves taking actions such as implementing quarantine system and blocking suspicious ports based on the model’s predictions.

DOI: /10.61463/ijset.vol.12.issue2.125

Modeling, Implementation and Performance Analysis of a Grid-Connected Photovoltaic/Wind Hybrid Power System

Authors- Y.Rambabu, T.Ramya, R.Pinaaki, P.Lahari Keerthi, B.Rajesh

Abstract-This study looks into the design, control strategy, and dynamic modeling of a grid-connected hybrid power system that combines wind and photo voltaics (PV). To improve system performance, the hybrid power system consists of a wind farm and a PV station that are interconnected via the main AC-bus. In order to collect the most power possible from a hybrid power system as climatic conditions vary, the Maximum Power Point Tracking (MPPT) technology is used in both PV stations and wind farms. Software like Matlab/Simulink has been used to model and simulate the hybrid power system. The efficiency of the hybrid power system’s MPPT technology and control approach is assessed under various environmental circumstances, including changes in wind speed and solar irradiation. Furthermore, the control technique effectively keeps the grid voltage constant despite changes in the outside environment and the electricity that the hybrid power system injects.

Blockchain-Powered Patient Health Care System

Authors- N. Kavitha, K. Vineetha, K. Naga Lakshmi, L. Divya Lakshmi devee, Associate professor Dr. K. Kranthi Kumar

Abstract-The purpose of this study is to look into how blockchain technology could completely change the way electronic health records (EHRs) are protected. EHRs store important medical information about patients and need to be kept safely and easily available while still being managed. Even though health care has perks, worries about data protection are still holding it back. By using both public and private ledgers, smart contracts, and context-based access control, the suggested blockchain-based system provides a unique answer. This design not only makes sure that patient data is stored safely and reliably, but it also makes sure that different healthcare systems can talk to each other. In addition, it adds a reliable and effective way to handle complicated medical treatments. The study not only looks at ways to make data safer, but also at how blockchain could be used in healthcare, such as making it easier to share health data anonymously and safely for research reasons. The writers’ structure focuses on being accessible, interoperable, and auditable. This makes the way for a stronger and more open healthcare environment. Using smart contracts, this method should make keeping medical records more efficient while still protecting privacy and safety.

DOI: /10.61463/ijset.vol.12.issue2.127

A Review on Security Enhanced WSN DSR Protocol to Prevent Black Hole Attacks on Manets

Authors- Jitendra Sharma, Professor Amit Thakur

Abstract-Wireless Sensor Network (WSN) is a network which has several small sensor devices responsible for communicating and sensing data wirelessly with each other. WSN have a various field of applications such as military, healthcare monitoring, traffic monitoring, industrial monitoring, undersea monitoring, agriculture monitoring etc. These networks are implemented in unprotected environments, thus vulnerable to various kind of security threats results in breaching network security. Blackhole attack is one of such kind of attack. Different techniques have been studied which detects and prevents blackhole attack. Various advantages and disadvantages of different techniques are also discussed which helps in finding best approach for blackhole attack. This synopsis focuses on analysis of various detection and prevention methods of blackhole attack in WSN.

Review on Analysis Attacks and Defense Metrics of Routing Mechanism in WSN Mobile AD HOC Networks

Authors- Bhupesh Paliwal, Professor Amit Thakur

Abstract-The main objective of the synopsis is to present different types of Security attacks, their effects and defense mechanisms in Wireless Sensor Network which is vulnerable to security attacks and threats due to its characteristics and limitations. Security attacks are identified and classified from different perspectives e.g. based on network layer in which the attack occurs, specifically network layer wise security features and the network security basics, based on attacker location, based on transmission of information, based on different protocol stack layers etc. and the different security measures that can be applied to defend against different attacks. This survey synopsis focuses on various aspects of different security attacks, their effects and defense mechanisms corresponding to each attack etc. So this synopsis helps researchers to have a very strong idea about the security issues, existing attacks and they can also use the ideas and concepts to build more secure wireless sensor network system in future. A direction can be obtained to develop new security mechanisms to protect new possible attacks along with existing ones.

Online Voting Using Blockchain

Authors- Yogesh Chakravarthy A, Prasannaram R, Navaneeswaran D

Abstract-In the paper that follows, an online voting system enabled by blockchain is proposed. An infinite range of applica- tions that profit from distributed economies are made possible by blockchain technologies. An Android application with improved security features, including authorization and authentication, is the model that is being suggested. A unique identity key is used to incorporate authentication, and a fingerprint is used to authorise. Another method of voter verification is one-time passwords. This project’s security is implemented utilising SHA-256, blockchain, and the 128-bit AES encryption technique. The vote is cast as a transaction, and a blockchain is constructed to record the number of votes cast. By doing this, integrity and atomicity are preserved.

DOI: /10.61463/ijset.vol.12.issue2.130

Computational Analysis of a Corrugated Dragonfly Airfoil at Low Reynolds Number

Authors- Mr.S Siva Jothi, L. Jerclin, V.Arunjetlin, M. Lalitharani

Abstract-The chord Reynolds numbers of micro air vehicles are usually in the range of 104 to 105.The laminar flow separation is a common phenomenon occurring in a flow over a body. The corrugated dragonfly airfoil has the potential ability to sustain an attached flow at low Reynolds number, thereby suppressing laminar flow separation or large laminar bubbles. In this project an optimized corrugated dragonfly airfoil is designed from the survey of standard airfoils. The flow properties of the corrugated dragonfly airfoil are measured at different angles of attack such as 00,50,100 and 150 for the Reynolds number of 5*104 in which the MAVs usually operates. The aerodynamic performance of corrugated dragonfly airfoil is compared with a traditional NACA 0012 airfoil at the same Re and also with a corrugated dragonfly airfoil at a different Re of 34000. The corrugated airfoil is meshed using GAMBIT and the computational fluid flow analysis is carried out using FLUENT on the corrugated dragonfly airfoil at low Reynolds number of 5*104. The flow behavior around the airfoil is analyzed and simulations are carried out to predict the behavior of unsteady flow structures around the airfoil at different angles of attack.

DOI: /10.61463/ijset.vol.12.issue2.128

Building Information Modeling

Authors- Dipanshu kolhe, Lekhraj Sinotiya, Bhumika Arya, khushboo Baghel, Sonam Kadve, Asst. Prof. Brajesh Pandey

Abstract-Building Information Modeling (BIM) has emerged as a transformative technology in the architecture, engineering, and construction (AEC) industry, offering powerful tools for enhancing the visual quality of building designs. This abstract presents a literature review focused on exploring the use of BIM for visual quality enhancement in building projects. By synthesizing existing research, industry reports, and case studies, this review aims to elucidate key methodologies, techniques, and best practices employed in leveraging BIM for visual enhancement purpose.

Flow Plan: Revolutionizing Project Management with AI-Powered Planning and Collaboration

Authors- Digvijay Patil, Aditya Dhade, Sharyu Malshette, Samrudhi More, Shweta Dangi

Abstract-Flow Plan is a revolutionary web-based application designed to streamline project management processes through innovative features and advanced technologies. This versatile tool offers comprehensive functionalities for project planning, note-taking, and documentation, empowering project teams to collaborate effectively and efficiently. Leveraging the power of artificial intelligence (AI), Flow Plan provides auto-generation capability, producing detailed software project life cycle plans encompassing five stages. Users input project details such as problem statement, cost estimates, availability, and time span, allowing Flow Plan to generate tailored plans that meet specific project requirements. The application utilizes a node-based visualization approach, organizing project plans in a clear and structured format. Users have the flexibility to modify plans according to their needs, ensuring adaptability to evolving project dynamics. Flow Plan includes a secure login interface, enabling users to add team members and share plans or notes for collaborative work. Users can seamlessly switch between documentation views and node view, enhancing accessibility and understanding. With its color-compatible interface and integration of modern technologies such as [1]React JS,[2] Express JS, [3]Firebase, and [4]Chat GPT API, Flow Plan sets a new standard in project management tools, promising enhanced efficiency, collaboration, and success for project teams.

Designing and Developing of E-Commerce Website

Authors- Krishna Soni, Assistant Professor Riddhi.A.Mehta

Abstract-In the contemporary digital landscape, the proliferation of e-commerce websites has revolutionized the way businesses operate and consumers engage in transactions. This research paper explores the intricate process of designing and developing an e-commerce website, emphasizing a comprehensive approach to address the multifaceted challenges and opportunities inherent in this domain. The study delves into various aspects including user experience (UX) design, backend infrastructure, security considerations, and performance optimization. Firstly, the paper elucidates the importance of user-centric design principles in crafting intuitive interfaces that enhance user engagement and satisfaction. It discusses methodologies such as user research, wire framing, and prototyping to create seamless navigation pathways, efficient product discovery mechanisms, and frictionless checkout experience. . Secondly, the research highlights the pivotal role of backend infrastructure in supporting the functionalities of an e-commerce website. It evaluates different backend frameworks and databases, emphasizing scalability, reliability, and data integrity as key criteria for selection. The integration of essential components such as user authentication, product catalog, management, order processing, and payment gateways is examined in detail.. Furthermore, the paper underscores the paramount significance of security measures to safeguard sensitive user data, prevent fraud, and ensure compliance with regulatory standards. It explores encryption techniques, secure authentication protocols, and vulnerability assessments as indispensable practices to fortify the resilience of e-commerce platforms against cyber threats. Moreover, the research addresses the imperative of optimizing website performance to deliver seamless user experiences and mitigate bounce rates. It explores strategies such as content delivery networks (CDNs), caching mechanisms, and image optimization techniques to expedite page load times, minimize latency, and enhance overall responsiveness .In conclusion, this research paper provides a holistic framework for the design and development of e-commerce websites, synthesizing insights from user experience design, backend infrastructure deployment, security implementation, and performance optimization. By embracing this comprehensive approach, businesses can effectively navigate the complexities of the digital marketplace and cultivate enduring relationships with, their online customers. Generals terms: Predictive Analytics, Statistical Modeling, Financial Risk Management, Data Analysis, Credit Scoring.

Advanced Password Management System

Authors- Vignesh Shivaram M, Mahesan P, Aanandha Varsini M

Abstract-The Advanced Password Management System is a groundbreaking initiative that integrates blockchain technology into password management, aiming to transcend the limitations of traditional systems and create a new era of heightened security, user empowerment, and transparency. Blockchain, known for its decentralized architecture, immutability, and cryptographic security, serves as the cornerstone of this innovative project. By addressing vulnerabilities associated with centralized systems, reducing the risk of a single point of failure, and bolstering the integrity of digital identities and authentication processes, the system offers users greater control over their digital identities, introducing transparency through immutable audit trails, and leveraging smart contracts for efficient authentication. The project’s methodology involves a structured approach to ensure the successful implementation of the project, including requirement analysis, system design, and testing. The project aims to establish a secure, user- friendly, and compliant digital environment that prioritizes both the integrity of the system and the satisfaction of its users. The components of the system include an user interface, backend server, blockchain network, and encryption module. Users can register and authenticate using their email address, manage password vaults, and perform encryption and decryption using AES encryption before storing them on the blockchain. Blockchain integration is achieved through APIs or SDKs, using libraries like Nethereum for Ethereum and REST APIs provided by blockchain platforms like Hyperledger Fabric. Smart contracts govern password management operations on the blockchain, utilizing a private blockchain network for decentralized storage of password data. This project aims to redefine the benchmarks of digital security and contribute to a future where user-centric control and tamper- resistant systems become the cornerstone of digital security protocols.

A Study on Employee’s Retentions Strategies at HDFC Sales

Authors- Ajay Kumar G, Muthumani S, Bethel Erastus-0bilo

Abstract-Employee retention is the conscious and deliberate effort to retain quality individuals on the company payroll. In today’s world, the power of intrinsic motivators cannot be undervalued. Employees are constantly exploring various avenues to develop their skills so that they can be promoted and employable in these fast-changing times. This project aims to understand the employee retention strategies adopted at HDFC SALES and to study the issues related to it. The primary objective of this project is to study the employee retention strategies adopted in the organization.

Diabetes Forecast Web App: ML Implementation

Authors- K.Vijayalakshmi, Kaviarasan M.S, Abdul Aziz, Dhanvarsh M

Abstract-One of the most deadly chronic diseases is diabetes, which can cause additional catastrophic complications. Building an RFR prediction model for diabetic complications and identifying the key characteristics connected with it are the objectives of this study. The diabetes risk factor was reduced to seven characteristics in this study: blood pressure, age, gender, BMI, blood glucose level, duration of diabetes, and family history of the disease. Thus, this dataset is analyzed using RFR tree-based classification methods. Following this investigation, we determined the linked feature and assessed each technique’s effectiveness. The greatest significant risk factor for retinopathy is therefore a female patient experiencing a hypertensive crisis. The most important risk factor for nephropathy is having diabetes for longer than four years. However, neuropathy was more common in female patients with BMIs over 25. There is no clear, statistically significant association between these comorbidities and a family history of diabetes. The suggested model has an overall accuracy of 80%, suggesting that it may be a useful alternative for diabetes illness prediction.

Bio Bricks with Agriculture Waste

Authors- Assistant Professor Mounika Chittem, Assistant Professor B Narsimha, Assistant Professor Modugu Naveen Kumar

Abstract-Our venture is entitled as “BIOBRICKS” which manages development materials. The principal expect to do this task is to lessen contamination and keep up with economical climate by reusing the Agro squander. Building development is quite possibly of the quickest developing industry in India and puts a tremendous weight on its restricted regular assets. Terminated mud blocks are one of the significant constituent material for development industry and it delivers an enormous measure of ozone harming substances which causes contamination. This examination attempts to feature the utilization of elective material and how they can be adjusted to suit the Indian development industry. Bio-blocks (or) Agro squander based blocks is one such materials that can possibly be practical and savvy arrangement. The concentrate likewise feature the utilization of Bio-blocks in different region of a design. Finally this exploration is to move and rouse modelers, originators specialists and manufacturers to empower and uphold the advancement of supportable and eco-delicate material in the development business.

IoT Infrastructure and its Attacks by Implementing Bluetooth

Authors- M.P.Naresh Kumar, M.Anand, R.L.Viswa, A. Rijo Aagash, Assistant Professor K. Gopal Ram

Abstract-The Internet of Things, also known as IoT, is a new networking paradigm made up of geographically dispersed wireless and wired networks that are connected by a “protected” backbone, which is effectively the Internet. It utilises several methods to connect billions of Things, which are heterogeneous device communication technology and offers end customers a wide range of intelligent applications. IoT constitutes a new evolution for the Internet in terms of diversity, size, and applications. It also invites cybercriminals to launch widespread, catastrophic attacks on IoT systems on a big scale, with potentially disastrous results. The safety of its wired and wireless infrastructures has a significant impact on the security of IoT infrastructures. Nonetheless, the wireless infrastructures are considered to be the most pervasive, significant, and exposed component of IoT. Here move on, Discuss the issues with Bluetooth connections to the IoT world in this essay.

DOI: /10.61463/ijset.vol.12.issue2.146

Experimental Investigation and Process Optimization during CNC Turning of AISI D3 Steel

Authors- Amit Thakur, Tanvir Singh, Jatinder Kumar

Abstract-The study delved into assessing the performance disparities between uncoated and TiNAl-coated carbide inserts during CNC turning of AISI D3 Steel under dry conditions, examining varying cutting parameters such as spindle speed, feed rate, and depth of cut. Process performance was gauged through surface roughness, Material Removal Rate (MRR), and Tool Flank Wear (TFW). Employing an L18 orthogonal array, experiments were conducted and optimized using the Taguchi method and subsequently validated with a Weighted Sum Model (WSM) approach. Findings underscored the significance of all cutting parameters in enhancing surface quality and MRR, with spindle speed emerging as the most influential contributor, accounting for 73.34% of the impact. Taguchi optimization recommended wet cutting conditions with a spindle speed of 800 rpm, feed rate of 0.16 mm/rev, and depth of cut of 1.3 mm for minimal surface roughness. Notably, machining factors like spindle speed, feed rate, and depth of cut played pivotal roles in achieving maximum MRR, while cutting environment, spindle speed, and feed rate significantly influenced TFW. Optimal responses were attained through WSM, with MRR at 420.165 m/sec, surface roughness at 0.75 µm, and TFW at 0.024 µm, utilizing cutting parameters of 1200 rpm, 0.12 mm/rev, and 1.3 mm, respectively. Confirmatory experiments validated substantial improvements in responses when employing the recommended parameter combinations.

An Analysis of Supply Chain Management: Crafting a Plan for Supply Chain and Inventory Management

Authors- Professor Joel Mark P. Rodriguez, Lourdes Q. Palallos

Abstract-This study examines supply chain and inventory management practices at IHI Incorporated, a company specializing in turbocharger supply and repair. Using a descriptive and correlational research method, data was collected from 51 respondents selected through purposive sampling. The findings revealed that most employees are male, around 35 years old, with college degrees, and 3-5 years of experience in rank-and-file positions. The assessment of supply chain management highlighted the importance of accurate demand forecasting, product diversification, and efficient distribution channels. Similarly, inventory management was emphasized for cost-effective operations, stressing the need to minimize inventory levels while maintaining adequate safety stocks. Statistical analysis showed a highly significant relationship (r = 0.9999) between supply chain management and inventory operations, indicating their close interdependence. Recommendations for improvement include regular assessments, enhanced inventory tracking systems, interdepartmental collaboration, training initiatives, and fostering a culture of continuous improvement. This study sheds light on supply chain and inventory dynamics at IHI Incorporated, offering actionable insights for enhancing operational efficiency and achieving organizational objectives.

 

A Review on Genetic Engineering Practices and the Impact of Gene Editing in Healthcare and Bio Technology

Authors- Professor Preetam L.Nikam, Miss Kajal Wakchoure, Miss Ashwini Pingale, Professor Vikas Shinde

Abstract-The creation of novel diagnostic methods, medications for treating illnesses in humans and animals, human-healthy meals, tissues, and cells for xenotransplantation all depend on genetic engineering. Peptides and other components may become essential to human life in the near future; proteins are found in nutraceuticals for human health and vaccinations for disease prevention. Significant advantages to human health and the environment can also be obtained from genetically modified animals. By introducing disease resistance and overall health, these animals become more productive in turning feed into animal protein and minimizing waste outputThe techniques permit individuals or groups of genes to be isolated from large masses of DNA and produced in virtually unlimited quantities. Genetic engineering in animal production has a growing number of practical benefits, such as in the production of transgenic animal’s resistant to disease, increasing the productivity of animals, in the treatment of genetic disorders, and the production of vaccines.

Analysis of Medical Images (MRI, CT Scans) for Early Detection of Abnormalities

Authors- Assistant Professor Dr. Pankaj Malik, Sadawarte Aniket Ajay, Parth Kothari, Tanish Kag, Parth Sharma

Abstract-Medical imaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans have become invaluable tools for diagnosing a wide range of medical conditions. Early detection of abnormalities in these images is critical for timely intervention and improved patient outcomes. This research paper provides a comprehensive overview of techniques for analyzing medical images to detect abnormalities at an early stage. We delve into preprocessing methods to enhance image quality, feature extraction techniques to capture relevant information, segmentation algorithms to delineate abnormal regions, and classification approaches to categorize these regions. Furthermore, we discuss the challenges involved, including data variability and interpretability issues, and propose future directions to address these challenges and improve the accuracy and efficiency of early detection systems. Through advancements in image analysis and machine learning, we aim to contribute to the development of more effective tools for early detection and diagnosis in clinical practice.

A Decentralized File Storage Solution Using IPFS and Blockchain

Authors- Ms. N. Vanitha, A Janani, Jeevasri Supraja D, Varsha Vijaykumar

Abstract-A centralized approach to cloud computing has its fair share of issues. The cost required to store data in centralized servers is more than required and the security it provides is quite less. The solution to this is to implement a decentralized cloud storage using blockchain which is a distributed database that is shared among the nodes of a computer network. Inter Planetary File System (IPFS) is a peer-to-peer network and a protocol for storing files and sharing data in a distributed file system. It is quite similar to cloud computing but it is decentralized. In the proposed system, we will build an application that will enable the users to store a file and view a file that is already stored and download a file already stored by them. The application stores the user’s file across multiple peers using the IPFS protocol which stores the file in IPFS objects and the hash returned by it that specifies the address of the file will be stored in the Ethereum blockchain using smart contracts. The purpose of storing the hash returned into the blockchain is to provide security against hacking the file as anything that is stored in the blockchain is immutable. This is also due to that security given by the proof of ownership consensus protocol that blockchain provides.

Detection of Glaucoma Using Image Processing Using Deep Learning

Authors- Assistant Professor R Suganya A, V Vigneshwaran A, M Sujairam A, P Shena Reddy A

Abstract-Glaucoma is an eye disorder that is categorized by elevated Intraocular Pressure (IOP). This increased IOP leads to damage to the optic nerve head. If it is will lead to loss of vision. Glaucoma will be detected by extraction of the optic cup (OC) and optic disc (OD), that is, the no of pixels in the optic disc and optic cup are calculated. In our project, we automatically extract the optic disc in retinal images by pixel-based segmentation and optic cup by mathematical morphology and watershed transformation. In the existing method, the optic cup and optic disc are segmented by creating a mask manually, and from that glaucoma is detected. But its experimental result doesn’t close to the clinical CDR value. But in our proposed system, we don’t need to select the OD and OC boundary by creating the mask. The OD is the high-intensity part of an eye. So, we easily and simply extract the disc boundary by the thresholding used in pixel-based segmentation. Cup segmentation is much more challenging compared to disc segmentation due to the presence of high-density vascular architecture in the region of the optic cup traversing the cup boundary. Also, the transition between the cup and the surrounding neuro retinal rim may be gradual and decrease the visibility of the cup boundary. But, in the proposed system watershed segmentation easily isolate the OD and OC boundary. The optic cup to optic disc ratio is calculated, to show the progression of glaucoma.

Gender Recognition from Selfie Images by Merging Convolutional Neural Networks with Genetic Algorithms

Authors- Assistant Professor Esraa Farouk

Abstract-Human gender plays an imperative role as social construct and an essential form of an individual’s personality. Gender classification uses the face of a person in each image to recognize the gender (male or female) of the given person. There are two types of input face images (standard or non-standard). It is highly reflected in social communication, forensic science, surveillance, and target marketing. Gender recognition previously depended only on standard face images. Using the term “standard” means that the image was taken in a standard light without any background variations and without any cropped parts. This type of image facilitates solving gender recognition problems. However, if some variations happened to these images, the result would not be accurate. But most real-life images are not captured in a suitable manner. These images are called non-standard images, as they have a lot of variations, like illumination and head pose. The image may also have a lot of faces, where one of them may wear sunglasses or other accessories. Using this type of image will affect the accuracy of gender recognition approaches. Nowadays, selfie images appear as they are non-standard images. People take selfie images of themselves. Selfie images are very complex, as some parts of the images are cropped and damaged. This paper proposes a (CNNGA) technique to increase the performance of gender recognition from unconstrained and selfie images. This technique relies on merging deep learning approach with a genetic algorithm. This proposed technique achieves 90.2% accuracy in recognizing gender from the selfie dataset. The experiments use various challenge datasets, which are widely adopted in the scientific community like LFW, Data Hub, FERET, and Caltech-web Faces.

Preventing Botnet Attacks on the Industrial Internet of Things through a Hybrid Deep Learning Method

Authors- Assistant Professor Mrs.G Suneetha, K Swarna Latha, K Gopi Krishna, Md Saheb, N Vigneshwar Reddy

Abstract-The Industrial Internet of Things (IIoT) has fundamentally changed and revolutionized industry 4.0 and global production by creating a richer ecosystem of intelligent, networked devices and opening up new avenues for digital innovation. Conversely, IIoT is a remarkable and possible target for cyber attackers due to its widely distributed nature, Industrial 5G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking. Sophisticated and multi variant bot attacks are deemed disastrous for IIoT connections. Furthermore, botnet attack detection is a highly intricate and precise process. Therefore, it is imperative that IIoT botnets be detected quickly and effectively. Our suggestion is a hybrid intelligent Deep Learning (DL) enabled system to protect IIoT infrastructure against highly skilled and deadly multi-variant botnet attacks. Using the most recent dataset available, standard and extended performance evaluation measures, and the most recent deep learning benchmark methods, the suggested mechanism has been thoroughly examined. Additionally, we cross-validate our findings to provide a comprehensive picture of overall performance. With a 99.94% detection rate, the suggested approaches outperform in precisely recognizing multi-variant complex bot attacks. Furthermore, the time of 0.066(ms) achieved by our suggested method demonstrates encouraging outcomes in terms of speed efficiency.

Chef Cart: A Restaurant Website

Authors- Amiya Soni, Shivangi Sahu, Shreya Dewangan, Abhinav Raj, Assistant Professor Jharna Chopra

Abstract-The undertaking is based on a web utility that may be used by the client to e-book the favored table and menu of their choice from a restaurant according to their comfort. previously table reservations became guides which is finishing up steadily in restaurants but nowadays human beings are getting into the virtual generation of reservations of restaurants, and providers are thinking about picking out a digital gadget for reserving. In a manual system, the whole thing relies upon upon waiter & booking diary and there’s no computerized device for maintaining the records. the overall goal is to build a reservation gadget for desks and menus to assist workers with solving basic problems with their guide reservation system. For instance, usage of time and cash. nowadays technology encourages high-tech offerings mainly over the net. consequently, the project is advanced proficiently to help restaurant owners automate their enterprise operations. In the proposed reservation machine, we offer the facility to the clients to reserve a table or menu or both according to their convenience. The purchaser can cancel the booking if failed to arrive on time and subsequently book the table inside the subsequent feasible time slot provided that the booking is canceled before 30 minutes of the chosen timeslot otherwise, the development paid will now not be refunded.

Commercial and Technical Elements Evaluation of Highway Construction with Optimization its Performance

Authors- Amrendra Ranjan, Professor Umesh Rathod

Abstract-The highway networks were built to connect the rural people to the town area or to other destination required by the local residents. Normally the highway will be developed to connect or increase the socio-economic opportunity in rural area. The green road is one of the key areas that can be look into to create the sustainable concept based on three key aspects namely social, environmental and economic factors. The main focus of the commercial and technical elements evaluation of highway construction is to increase the profits using more efficient resources, especially materials, improving the quality of life by meeting the national needs of social aspects and protecting the environment from the effects of CO2 emissions and efficient use of natural resources for environmental aspects. Therefore, it is important that stakeholders include sustainability criteria in their projects. The application of a sustainable concept on the road can be assessed by the green road evaluation tool. Therefore, the main aim of this study is to build review of commercial and technical elements to evaluate and declare highway.

Service Marketing in the Current Era:Enhancing Customer Experience and Engagement

Authors- Research Schollar K.Sri Varun Venkatesh

Abstract-This paper examines the evolving service marketing landscape, emphasizing the importance of enhancing customer experience and engagement. In today’s hyper-connected world, firms must prioritize customer-centric initiatives. It covers aspects like social media integration, customization, and technological integration. Understanding client behavior is crucial for tailoring services to meet their needs. Creating memorable experiences and fostering brand loyalty are also key. Drawing from recent research and industry best practices, this abstract offers valuable insights for marketers.

DOI: /10.61463/ijset.vol.12.issue2.137

Attendance Management System Using Facial Recognition

Authors- Vanitha N, Guruprasan V, Naveen S, Kiran Kumar Kesavan

Abstract-This paper presents a facial recognition-based attendance system designed to automate the attendance marking process in educational institutions and organizations. The Management whenever handle the attendance by hand, it might be very taxing for them. An automated and intelligent attendance management system is being used to address this issue. However, one crucial issue with this approach is authentication. The smart attendance system usually makes use of biometrics. One of the biometric techniques to enhance this system is face recognition. Facial recognition is a key component of biometric verification and is widely utilized in many applications, including network security, access systems that are located indoors, computer- human interaction, CCTV footage systems, and video monitoring. This architecture makes it easy to address the problem of proxies and students being marked present even when they are not. The system utilizes computer vision techniques implemented using Open CV and face recognition libraries for face detection and recognition. Attendance data is stored in a Mongo DB database for easy retrieval and analysis. The system offers a reliable, efficient, and secure solution for attendance management.

DOI: /10.61463/ijset.vol.12.issue2.133

A Blockchain-Based Financing Platform Utilizing Polygon Network

Authors- Midhunadharshini G, Aashrith D, Krishna Teja P, Dharanidharan P

Abstract-The suggested design is a decentralized, platform that would use blockchain and other technologies to build a reliable, transparent system for campaign funds. With the help of a Polygon-based Blockchain-centered platform, the platform has the potential to integrate blockchain technology into already- existing organizations that support the smooth transfer of donations from donors to actual in-need persons. Eliminating middlemen allows fundraisers and contributors to interact directly, resulting in a more meaningful and direct relationship between the giver and the recipient.

DOI: /10.61463/ijset.vol.12.issue2.134

Environmental Impact Assessment in Highway Construction Case Study and Data Sampling

Authors- Gaurav Kumar, Assistant Professor Jitendra Chauhan

Abstract-The Environmental Impact Assessment is a systematic investigation of both positive and negative impacts on the physical, biological socioeconomic environment, which would be caused or induced due to a proposed project. EIA provides a plan to reduce the negative environmental effect of proposed development project through alternative approaches, design modification and remedial measures. Highway construction is a major activity of economic development countries. Road development is major source of damage to the environment, including ecological destabilization, habitat disturbance and damage to flora and fauna. In this study, environment impacts are analyzed. The study concentrate on the environmental impact assessment of the project in the light of the existing situation at the site.

Simplifying the Opaqueness and Dilemma of the Computing Disciplines for the Common Person in Society

Authors- Mr. Geofrey Mwamba Nyabuto, Professor Franklin Wabwoba

Abstract-Computing fields have changed how society works and relates to one another. Since its inception in the mid-20th century, there have been several fields of computing emerging based on the need for each of them. The earliest field in computing was computer science, which ideally covered everything regarding the study and use of computing machines. As time advanced, other new fields have emerged including computer engineering, software engineering, information systems, information technology, artificial intelligence, cybersecurity and now data science. In the current times, there has been a lot of emphasis and developments in artificial intelligence and machine learning as well as cybersecurity. As we go towards the fifth industrial revolution, we stand to witness more use cases of technology and possible new computing fields that will give rise to a lot of ethical considerations within the computing fields. The aim of this paper is to review and help the common person in society understand the different fields of computing, their evolution and how they differ from one another. To better bring out this, the paper historically reviews computing fields, denoting their limitations and how other new fields evolved from the already existing ones. The paper also describes the current state of computing related fields.

DOI: /10.61463/ijset.vol.12.issue2.139

Depression Healing Chatbot Using AI and ML

Authors- Deepa B, Madhul Manoj, Kuzhali R, Senthamilselvi P

Abstract-Healer Bot-LUNA” is a revolutionary web application designed to revolutionize mental health support by addressing depression through an innovative and user-friendly interface. Harnessing cutting-edge chatbot technology, Heal Bot establishes a nurturing virtual environment where users can freely express their thoughts and emotions. This advanced chatbot engages users in empathetic conversations, providing a comforting companion for those navigating the challenges of depression. What sets Heal Bot apart is its commitment to a comprehensive approach to mental health. The platform incorporates recommendations from experienced psychiatrists and consultants, ensuring a well-rounded and personalized support system. Privacy and confidentiality are paramount in the Heal Bot experience, fostering a secure space for individuals. To seek assistance without judgment. The app’s accessible and straightforward design makes it a valuable tool for those actively managing their depression. Through personalized notifications and continuous monitoring, Heal Bot empowers users to take a proactive stance toward their mental well-being.

DOI: /10.61463/ijset.vol.12.issue2.144

Wind Speed Prediction

Authors- Nirmala Deve, Aravindan S, Koushik ER, Mohan M

Abstract-This paper investigates advanced techniques for wind speed prediction to enhance wind energy optimization. Traditional methods face challenges due to wind variability, leading to the exploration of new approaches. One such approach combines LSTM neural networks with decision tree regressors. Comparative experiments demonstrate that this hybrid model outperforms other deep learning architectures and regression models, providing higher accuracy and reduced prediction errors. This research contributes to improving wind speed forecasting for more efficient utilization of wind energy in the expanding renewable energy sector.

Planning and Designing of G+2 Residential Building

Authors- Asst. Prof. Meesa Mahendar, N Keerthana, Asst. Prof. Modugu Naveen Kumar

Abstract-The design arranging is a piece of metropolitan improvement it incorporates arranging of private houses, business buildings, administration streets, essential wellbeing habitats, school…& different conveniences sewerage framework for entire format (incorporates treatment, sewer line, storm water channels), water dissemination framework. This undertaking incorporates design& assessment of private structure in plot of format planned. Designing includes recognizing the heaps which follow up on a construction and the powers and stresses which emerge inside that design because of those heaps, perform examination to get minutes and shear powers on various components of the design and afterward plan the design for extreme burdens and minutes. The heaps can be self weight of the designs, other dead loads, live loads, moving (wheel) loads, wind load, quake load, load from temperature change and so forth. Assessment incorporates finding the amounts of materials expected for the development of the construction and prerequisites of work and so on, at long last deciding the general expense of the design before execution of work by utilizing Auto cadd. Underlying specialists are confronting the test of making progress toward the most effective and practical plan with exactness in arrangement, while guaranteeing that the last plan of a structure should be workable for its expected capability over its plan lifetime. This venture endeavors to comprehend the primary way of behaving of different parts in the multi-celebrated building. Examination, planning and assessment of multi-celebrated building has been taken up for Basement+G+2 Building, in this way relying upon the appropriateness of plan, design of bars and places of sections are fixed. Dead loads are determined in view of material properties and live loads are thought of as per the code IS875-section 2, footings are planned in light of safe bearing limit of soil. For the plan of sections and pillars outline examination is finished by limit state technique to know the minutes they are followed up on. Piece planning is finished relying on the kind of section (one way or two way), end conditions and the stacking.

Hybrid Wireless EV Charging System

Authors- Shahrukh Tanekhan, Ayush patil, Dhaval Patil, Nilesh Patil, Aditya Pawar, Shraddha Dagde

Abstract-Electric vehicles have now hit the road worldwide and are slowly growing in numbers. Apart from environmental benefits electric vehicles have also proven helpful in reducing cost of travel by replacing fuel by electricity which is way cheaper. However electric vehicles have 2 major disadvantages: 1. Long charging time 1-3 Hours Required for Charging. 2. Non availability of power for charging stations in off city and remote areas. Wireless Charging is technology of transmitting power through Air Gape to electrical device for the purpose of energy replenishment. Electric vehicles require fast, economical and reliable charging systems for efficient performance. Wireless charging systems remove the hassle to plug in the device to be charged when compared with the conventional wired charging systems. Moreover, wireless charging is considered to be environment and user friendly as the wires and mechanical connectors and related infrastructure are not required. This paper reviews the methods and techniques used for wireless charging in electric vehicles. First, the general techniques for wireless power transfer are described and explained. Capacitive power transfer and inductive power transfer which are the two main types of wireless charging are compared and contrasted. Next wireless charging systems for electric vehicles are classified and discussed in depth. Both the stationary and the dynamic wireless charging systems are discussed and reviewed. In addition, a typical model and design parameters of a dynamic charging system, which is a wireless charging system for moving vehicles, are examined. Control system functions of a wireless charging system of an electric vehicle are important for an effective and efficient performance., These are also discussed in the context of better efficiency of power transfer and improved communication between the transmitter and the receiver side of a vehicle charging system. Battery is an important part of an electric vehicle as different parameters of a charging system depend upon the battery characteristics. Therefore, different battery types are compared and battery models are reviewed.

Highway Road Defect and its Low Cost Maintenance Observation in Case Cracks and Deterioration of Pavement

Authors- Hitesh Sharma, Professor Jitendra Chouhan

Abstract-This work presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. Also this research work give a method crack treatment and low maintenance criteria.

Ultra-Wide Band Decoupling Design of a Microstrip Antenna Array by Using Complementary Split-Ring Resonators

Authors- S.Teja Sri Ajay, E.H.S.S. Prudvi, A. Hemanth, B. Prasanna Kumar, Dr. A. Suneel Kumar

Abstract-The mutual coupling between closely-spaced patches, we propose a two-step decoupling design approach for a micro strip antenna array with the dimensions of 44.9 × 30.495mm2. first step is designing a decoupling unit on the basis of wave guided complementary split-ring resonators (WCSRRs) to improve isolation. The second step is presenting an optimization method by using a fully connected neural network (FCNN) to enhance design efficiency. By inserting WCSRRs structure between two patches with the edge-to-edge distance of 0.24*0 (port-to-port distance with 0.66*0), measured isolation of the predicted micro strip antenna array is increased to 41.05 dB and 52.33 dB, respectively. All the simulated and predicted results are validated via the measurement to demonstrate the effectiveness of our design scheme.

Customer Churn Prediction in Telecom Industry Using Deep Learning

Authors- Nandini Gupta, Spardha Sahu, Neha Yadav, Proffesor Neha Soni

Abstract-Predicting customer competition involves knowing the value of identifying customers that will lead to competition, thus enabling businesses to take the necessary steps. It is important to keep them. Customer churn is an important factor for businesses looking to reduce revenue. It helps reduce customer churn, increase customer satisfaction, and ultimately increase business profitability. In this study, we present a new method for customer match prediction using deep learning neural networks. We focus on analyzing data based on historical data, such as customers, by leveraging the power of forward propagation and back propagation in deep learning algorithms. History and data) accurately predict customer churn. Negotiations with the company and the dataset to train the model were taken from the Kaggle Telecom Business Customer Chun Excel spreadsheet. The characteristics of the data (length of account, international tariffs, messaging plans, calls during the day, etc.) lead to input into the neural network. A process of using deep learning techniques, specifically forward and back propagation, for customer benefit prediction. Forward propagation involves passing input data through the neural network to make predictions, while back propagation involves updating the model’s parameters based on the error between the predicted and actual values. If models with new features obtained from the training dataset are proving to be more accurate than models with existing features, it suggests that these new features are providing additional information or capturing nuances in the data that were previously overlooked. This is a common phenomenon in machine learning, where feature engineering plays a crucial role in improving model performance.

Encouraging Perspective towards Sustainability and Green Economy

Authors- Assistant Professor Anila Singh

Abstract-The importance of ideas associated with sustainability has been the focus of the world economy for over three decades now. Understanding the key areas that can help the global community tackle the environmental issues ensuring sustainable developmental practices is pivotal. A green economy emphasizes the financial gains that movement toward sustainable development can bring for economies worldwide. This academic research is based on secondary data sources collected from various secondary data sources such as data published in journals, books, newspapers, reports, etc. A green economy’s ability to reduce carbon emissions and pollution, improve energy and resource efficiency, prevent the loss of biodiversity and ecosystem services, and invest in infrastructure and economic endeavours are what motivate employment and income growth. Public and private investment in these areas is crucial. To facilitate and encourage these green investments, targeted public spending, policy modifications, and tax and regulatory adjustments are all required. Employment and income growth are driven by a green economy’s potential to reduce carbon emissions and pollution, enhance energy and resource efficiency, stop the loss of biodiversity and ecosystem services, and invest in infrastructure and commercial ventures. Investing both public and private funds in these fields is essential. Tax and regulatory changes targeted public spending, and policy changes are all necessary to support and encourage these green initiatives. We must make this adjustment even though it is unlikely to be easy to execute if we are to ever accomplish the Sustainable Development Goals. However, the pace at which each country transitions to a green economy varies.

DOI: /10.61463/ijset.vol.12.issue2.140

EV Battery Protection from Over Charging & Over Temperature

Authors- Sushant More, Swapnil Mane, Sagar Nangare, Onkar Suryawanshi, Avishkar Swant, Sanskar Phalke

Abstract-Battery Pack Systems are used in many industrial and commercial systems to make the battery operation more efficient and for the estimation to keep the battery state, as long as possible, away from its destructive state, to increase battery life time. For this purpose, many monitoring techniques are used to monitor the battery state of charge, temperature and current. In the current paper, the monitoring system for battery powered Electric Vehicles (EV) has been implemented and tested. This system evaluates and displays the battery temperature, charging/discharging current and State of Charge (SOC) for the considered model battery. For monitoring purpose, digital and analog sensors with micro-controllers are used. The battery information and the obtained results explaining the main characteristics of the system are presented by photographs and some experimental results are given by the LCD screen.

DOI: /10.61463/ijset.vol.12.issue2.141

Named Entity Recognition Using NLP

Authors- Keerthana R, Assistant Professor Waseemuddin

Abstract-Recent advancements in Natural Language Processing (NLP) have paved the way for its widespread application across diverse fields such as business, law, and healthcare. An essential component of any NLP project is text preprocessing, a crucial step that involves modifying text data before feeding them into machine learning models. Typically, text preprocessing encompasses tasks like cleaning, filtering, removal, and replacement of certain texts to enhance model accuracy, robustness, reduce data size, or ensure privacy preservation. Named Entity Recognition (NER) stands as a key NLP tool, tasked with identifying Named Entities within text, including names, organizations, addresses, numbers, and dates. In this study, we propose a novel preprocessing approach leveraging NER to identify named entities and subsequently utilize them to enhance accuracy and safeguard privacy, instead of discarding them or allowing them to contribute noise to our data. Through a series of experiments conducted on various datasets, including some collected in-house, we evaluated our approach’s efficacy in text classification tasks. Our findings demonstrate that incorporating this approach not only boosts classifier accuracy and reduces dimensionality but also effectively preserves privacy. This underscores the significance of leveraging NER in text preprocessing to optimize NLP applications across different domains.

DOI: /10.61463/ijset.vol.12.issue2.142

Game-Based Learning Application

Authors- D.Roopa, Praveen V S, Nitheesh Raj M

Abstract-Educational games are games specifically designed to serve educational purposes or games that have incidental or secondary educational value. While all types of games can be used in an educational setting, educational games are specifically designed to help people learn about particular subjects, expand concepts, reinforce development, understand historical events or cultures, or assist in learning a skill while playing. Game types include boards, cards, and video games. As educators, governments, and parents recognize the psychological need and benefits that gaming has on learning, this educational tool has become mainstream. Games are interactive play that teaches goals, rules, adaptation, and problem-solving interaction, all represented as a story. They fulfill a fundamental need to learn by providing enjoyment, passionate involvement, structure, motivation, ego gratification, adrenaline, creativity, social interaction, and emotion within the game itself while learning takes place.

Design and Analysis of Battery Bracket for E Vehicle

Authors- Mr. Sagar Sasane, Professor Kharad B.N.

Abstract-The safety and durability of battery brackets in electric vehicles (EVs) are crucial for ensuring the reliable performance and protection of battery packs. Impact testing plays a vital role in evaluating the structural integrity and crashworthiness of battery brackets under various impact scenarios. This abstract provides an overview of research conducted on impact testing for battery brackets in EVs, focusing on experimental tests, numerical simulations, and finite element analysis. Researchers have investigated the impact performance of battery brackets under a maximum load of 5000N, considering different impact scenarios such as frontal collisions, side impacts, and high-speed loading. The response of battery brackets in terms of von Mises stress, deformation, and equivalent stress has been analyzed to understand failure modes, deformation characteristics, and energy absorption capacity.

Tribological Performance of Automobile Brake Pads by Using Metal Matrix Composites

Authors- Mr. Sandip M. Danave, Professor Kharad B.N.

Abstract-The wear and mechanical properties of aluminum alloy reinforced with titanium dioxide (TiO2) nanoparticles are investigated in this study. Aluminum-based composites have gained significant interest in engineering applications due to their desirable properties, and the incorporation of TiO2 nanoparticles offers a promising avenue to further enhance these properties. The composites were fabricated using a powder metallurgy method, and their mechanical behavior, including tensile strength, hardness, and impact resistance, was evaluated. Additionally, wear tests were conducted under varying conditions to assess the wear resistance of the composites. The results demonstrate that the addition of TiO2 nanoparticles improves both the mechanical properties and wear resistance of the aluminum alloy.

Water Surface Cleaning Robot

Authors- Aman George Sebastian, Madhav Devnarayan, Bachu James, Abhishek Thomas

Abstract-We all depend on water for many things in our everyday lives. It is a fantastic, necessary source of life. For many towns and communities, rivers and other bodies of water continue to be a significant supply of drinking water. The amount of trash in these bodies of water, however, is more than simply a nuisance; it poses a threat to nature, our lives, and the lives of those we love. Even a single item of litter left on the ground can add to the buildup of trash in our rivers and creeks. Although the water is initially cleansed before it enters our houses, if these water bodies are still polluted, the water cannot be sufficiently purified to become unfit for human consumption. To ensure that water continues to flow from our taps, it is essential to maintain our river systems and keep them free of pollutants. These serve as the driving forces behind our project. We are working to create an autonomous water surface cleaning robot that will move through the aforementioned water bodies in an effort to gather rubbish that floats in them. Our main goal is to keep the water bodies clean without human supervision. The region will be given to the robot, which will navigate it while floating across the area and collecting the aforementioned garbage. We also intend to mount a camera on the robot to provide real-time visuals and detect any waste products present in the water body. A conveyor belt connected to the boat is to be used to collect the waste materials. The waste materials are conveyed on a conveyor belt to a collecting area, where they are gathered and kept until the boat docks again.

Experimental Analysis and Tribological Performance of Al7075 Based Hybrid Composites

Authors- Mr. Sumit T. Nimbalkar, Professor Kharad B.N

Abstract-Hybrid Metal Matrix Composites (HMMCs) indeed offer a promising avenue for enhancing material properties beyond what can be achieved with single-phase Metal Matrix Composites (MMCs). The combination of different reinforcement materials in a matrix can lead to synergistic effects, improving mechanical, thermal, and tribological properties tailored to specific applications. The development of Al7075/TiB2/MoS2 hybrid composites represents a strategic approach towards achieving advanced material solutions that meet the stringent requirements of modern engineering applications.

Experimental Analysis of Composite Angle Clamp for Industrial Crane Mounting

Authors- Mr. Shrikant Ajinath Anarase, Professor Kharad B.N

Abstract-Composite materials are ideal for structural application where high strength to weight and stiffness to weight ratio are required. Aircraft and spacecraft are typical weight sensitive structures in which composite materials are cost effective. The study of composite materials involves many topics for example manufacturing processes, anisotropy, elasticity strength of anisotropic materials and micromechanics. This study the study of new budding reinforced composite material to replace at applications of costly, heavy & rustable metals at tensile loadings. To analyze experimentally and by finite element method the mechanical behavior of composite component with slotted hole and its validation using FEA method.

Experimental Analysis of Coil Spring of Motorcycle Suspension System by Using Different Material

Authors- Mr. Shrikant Ajinath Anarase, Professor Kharad B.N

Abstract-Automobile suspension performs an important role in passenger relief and stability of the vehicle. So far now, many substances have been evaluated for manufacturing of helical suspension springs as per requirement. Objective of this work is comparative learn about and evaluation of suspension helical coil spring with two specific materials (chrome silicon alloy and hard metal alloy) static evaluation the usage of finite thing analysis to decide the finest fabric to reduce the stress and deflection. Suspension mannequin is created in Pro E CREO two and the mannequin is structurally analysed using ANSYS 15.0. The effects and comparative find out about shows the top-quality fabric that can be chosen as spring material for environment friendly function and lengthy life.

Design and Implementation of New supporting System for Visually Challenged People

Authors- Professor Dr.B.Senthil kumar, Madhan kumar.P, Sujithkumar M, Sourab kumar M

Abstract-The field of assistive technology continually innovates to enhance the lives of those with visual impairments. This pioneering project fuses real-time health monitoring and live location tracking, granting safety and independence to visually challenged individuals. At its core is the Arduino Nano microcontroller, orchestrating seamless component integration. A precision pulse sensor monitors vital signs, enabling early health issue detection. Simultaneously, a high-precision GPS tracker determines live location, offering peace of mind to caregivers. The GSM module facilitates robust communication, instantly alerting designated contacts in emergencies. This holistic approach to assistive technology represents significant progress, acknowledging diverse needs. It opens doors to broader applications, promising advancements in health monitoring and safety assurance, benefiting a wider audience. In summary, this project enriches visually impaired individuals’ lives, delivering real-time support, independence, and security, showcasing the ever- evolving landscape of assistive technology.

DOI: /10.61463/ijset.vol.12.issue2.150

Classification of Heart Diseases with ECG Monitoring Using MATLAB

Authors- Samruddhi Desai, saniya waghmare, naziya Munde, Payal choudhary, Sanika patil, Mrs. Swati patil

Abstract-The electrocardiogram (ECG) is a diagnostic tool for many cardiac conditions. To measure the heart’s electrical activity ECG surveillance is among the most commonly used technique. It provide a MATLAB-based image processing method for an ECG monitoring system that combines conventional neural networks with ECG data filtering. There are multiple phases used ECG data input image, where raw ECG images are obtained; preprocessing, which involves methods like noise reduction, contrast enhancement; Filtering, in which the ECG signals are made clearer and more pristine by using a conventional neural network; Segmentation: this separates out specific ECG signal components, like QRS complexes; Edge detection: this locates edges within segmented ECG(6) Classification, in which an ECG signal is trained using a standard neural network to identify whether it is normal or abnormal condition of heart disease; Output of disease, which indicating the existence or absence of heart disease. To increase the precision and dependability of heart disease identification using ECG data, this paper describes the technique and MATLAB implementation of the image processing technique.

DOI: /10.61463/ijset.vol.12.issue2.149

A Compact Double -Band Short-Ended Zeroth Resonator Antenna Featuring Backed Ground Plane to Enhance Bandwidth and Radiation Efficiency

Authors- G. Rakesh Reddy, Assistant Professor S. Thirupathamma, P. Durga Prasad, CH. Siva Sanjay, B. Hari Kishor

Abstract-This paper mainly represents a compact planar dual-band short-end metamaterial antenna with a backed ground plane that enhances the antenna’s bandwidth and radiation characteristics. The proposed dual-band metamaterial antenna (MTM) is based on the concept of composite right-handed or left-handed transmission line (CRLH-TL). Here, a reserved ground plane is used to create additional coupling capacitance (CC) to shift the ZOR frequency to a lower band while improving ZOR matching and increasing the impedance bandwidth of higher-order modes. In this proposed MTM antenna, an interdigital capacitance (IDC) is used instead of a simple series gap, and the higher-order impedance bandwidth is lower for second-band Worldwide Interoperability for Microwave Access uses which has a range of (3.3-3.7 GHz) Wi-MAX applications. Since the proposed antenna provides short-end MTM, the ZOR frequency is controlled by a set of lumped LC parameters. The suggested antenna delivers dual-band functionality, exhibiting a measured -10 dB impedance bandwidth of 5.57% at 2.62 GHz and 41.61% at 4.54 GHz, respectively. The total electrical size of the designed antenna is 0.225λ0 x 0.144 λ0 x 0.0144 λ0 at ZOR (f0 =2.70 GHz). where λ0 is taken as free space wavelength. Therefore, it can be applied to various Wi-MAX application bands (2.5~2.7GHz/3.3~3.8GHz). Moreover, the proposed dual-band metamaterial antenna offers small size, low loss, stable gain and radiation efficiency, and also provides multidirectional radiation pattern in E-plane and dipolar radiation pattern in H-plane, respectively.

 

Medi-Aid Using Image Processing for the Detection of Skin Diseases

Authors- Anirudh P Menon, Rishi Kumar R, Anirudh V, Deepak R

Abstract-Virtual image processing is used in many computer applications for manipulating different images using various algorithms. In skin disease detection image processing proves to be pivotal in identifying various kinds of disease and disorders and also to give further treatment related information based on the disease identified by image processing. Various papers have been published based on the development of skin detecting software but none of the papers have proved to correctly display the severity and possible treatments involved in curing those diseases. Our work and approach are used to overcome those limitations and be helpful to the users in diagnosis and treatment of those skin diseases.

DOI: /10.61463/ijset.vol.12.issue2.155

Answering in Every Medium: The Multimedia Question-Answer Systems

Authors- Baraniga, Charumathi, Deepak, Gowtham, Krishna

Abstract-The rise of platforms, with multimedia content has driven the advancement of conversational AI systems expanding beyond just text based interactions to include a wide range of media such as PDFs, audio files, web links and images. In our study we explore in detail the creation, enhancement and assessment of a chatbot that can handle questions across media formats. By utilizing cutting edge techniques in natural language processing (NLP) information retrieval and processing multimodal data our chatbot sets a standard for improving interactions between humans and computers using types of media. Our primary goal is to examine how effective and user friendly a chatbot that deals with multimedia questions can be in real world situations. We conduct tests to evaluate how well our chatbot interprets user queries retrieves relevant information from various media sources and provides contextually appropriate responses across different formats. Additionally we explore techniques for combining types of media to help the chatbot better understand and synthesize information from various sources creating a more engaging experience, for users.

Blockchain Based Access Control Model for Student Academic Record with Authentication

Authors- K. Ramya, M. Yamini, K. Prajwala, M. Jyothirmai, Sk. Mulla Almas

Abstract-A blockchain is a decentralized, distributed and public digital ledger that is used to record transactions across many computers so that the record cannot be altered retroactively. Schools and institutions keep student records and certificates for future requirements such as credit transfer and verification. To ensure that documents and data is unaltered, these procedures are usually carried out manually leads to more time and effort. Converting these procedures to digital format reduces time and energy consumption but at a cost of increased security risks. Many institutions store data on a centralized database which is vulnerable to many security issues like data breaches, injection attacks, and buffer overflow attacks. Even if the server is physically damaged, it will take longer to restore, recover, and restart the damaged data. So, we propose the Blockchain-based Student Academic Record Access Control Model with Authentication. The data captured in blocks with a cryptographic hash of previous blocks provides enhanced security in blockchain technology compared to traditional databases. The Academic Record System employs three layers of blockchain to provide the highest possible degree of security.

DOI: /10.61463/ijset.vol.12.issue2.147

Improving Properties of Black Cotton Soil through the Utilization of Construction and Demolition (C&D) Waste

Authors- Assistant professor Manjushree.V.Gaikwad, Kishor P. Survase, Shashikant R.Yadav, Malakarsidha C. Pujari, Onkar S. Basate

Abstract-Black cotton soil, renowned for its expansive characteristics and limited load-bearing capacity, poses significant hurdles in construction endeavors. When exposed to varying moisture levels, this soil type typically undergoes swelling and shrinking, showcasing weak mechanical properties. This study investigates into the potential of repurposing recycled Construction and Demolition (C&D) waste to stabilize black cotton soil. It examines the incorporation of diverse C&D waste materials—such as concrete, bricks, mortar, and other construction remnants—into black cotton soil to bolster its mechanical resilience and stability. The research evaluates the efficacy of blending C&D waste at varying percentages (5%, 10%, 15%, 20%, and 25%) to mitigate soil swelling, enhance strength, and improve load-bearing capacity. Parameters including plasticity index, optimum moisture content, maximum dry density, and differential free swell index are scrutinized concerning the integration of recycled C&D wastes for soil stabilization. Through comparative analysis, this study aims to offer valuable insights into sustainable methodologies for augmenting the performance of black cotton soil while minimizing construction-related waste generation.

DOI: /10.61463/ijset.vol.12.issue2.145

Enhancing Electric Vehicle Reliability: Advanced Fault Detection for Demagnetization Faults in PMSM Motor Using Artificial Neural Network

Authors- Suprotip Ghosh Hazra, Professor Rekha Chaudhari, Omkar R Kale, Niteen J. Kharade, Abhishek M Pandey

Abstract-Electric Vehicles (EVs) have emerged as a promising solution to address environmental challenges and reduce the dependence on fossil fuels. PMSMs have a critical function in the propulsion setups of EVs, delivering superior efficiency and power density. Nevertheless, demagnetisation faults in PMSM motors present notable challenges to the reliability and maintenance of EVs. In this investigation, we suggest an Artificial Neural Network (ANN)-based system for detecting faults to tackle demagnetisation issues in PMSM motors of EVs. Our research aims to develop a robust fault detection system to enhance the reliability and safety of EV propulsion systems. By employing advanced machine learning techniques, specifically artificial neural networks (ANNs), we conducted comprehensive experiments and analyses to assess the efficacy of our proposed approach. The ANN model was trained with motor performance data, encompassing motor current, torque, and rotor speed, to pinpoint demagnetisation faults precisely. The outcomes of our analysis reveal the efficacy of the ANN-centric fault detection strategy in accurately recognising demagnetisation faults in PMSM motors. The ANN model exhibits remarkable accuracy, precision, recall, and F1-score, exceeding conventional fault detection techniques. Visual depictions of motor performance data and the progress of neural network training further confirm the resilience and dependability of our suggested approach. The pragmatic implications of our research bear significance for the automotive sector, especially in refining EV reliability and maintenance protocols. The adoption of our fault detection system has the potential to curtail downtimes, reduce maintenance expenditures, and prolong the lifespan of EV propulsion systems, ultimately fostering consumer trust in electric vehicles. Furthermore, our analysis suggests potential avenues for future research, exploring advanced machine learning techniques such as deep learning and reinforcement learning. The examination of real-time integration of fault detection systems and the inclusion of sensor fusion methodologies are also pivotal in bolstering the reliability and resilience of fault detection algorithms.

DOI: /10.61463/ijset.vol.12.issue2.151

Life Line B: Saving Lives through Blood and Organ Donation

Authors- Pranav Nalawade

Abstract-Blood and organ donation are vital components of the modern healthcare sys- tem, a lifeline for patients in need of life-saving graft and transplantation This paper explores the major aspects of blood and organ donation a their meth- ods, advantages, challenges and ethical considerations are included in detail The steps involved in organ transplantation and its involvement are discussed in detail and then the types of organ donors and their a discussion of the vari- ous methods, including dead and living donations. The benefits and impact of donations are emphasized, and health outcomes for donors and recipients are emphasized, as well as the sense of community support and solidarity fostered by donation practices Furthermore , common myths and misconceptions about blood and organ donation are addressed Ethical and regulatory frameworks for donations, such as consent, confidentiality, and sharing policies are examined to ensure donation actions are authentic and ethical Case studies and testimonials show the significant impact donations have on individuals and communities . By increasing awareness, removing barriers, and advocating for fair and equitable charitable practices, we can increase donation rates and save more lives through charitable giving.

DOI: /10.61463/ijset.vol.12.issue2.154

Environmental Chemistry of Phosphonates

Authors- Assistant Professor D Anitha

Abstract-Phosphonates contain one or more C–PO(OH)2 groups and function as human complexing agents. Chelating agents and scale inhibitors are two of their many industrial and technical uses. Phosphonates are distinguished from other chelating agents by characteristics that have a significant impact on their behavior in the environment. Phosphonates have a significant removal in both natural and technological systems due to their strong interaction with surfaces. Because of this solid adsorption, practically zero remobilization of metals is normal. Phosphate is not broken down by biodegradation during water treatment, but the Fe(III)-complexes are broken down quickly by photo degradation. In the presence of Mn(II), aminopolyphosphonates also undergo rapid oxidation, resulting in the formation of oxygen and stable breakdown products that have been observed in wastewater. Analytical difficulties in determining phosphonates at trace concentrations in natural waters are to blame for the lack of environmental information. In this area, further method development, including speciation of these compounds, is urgently required. Based on the current understanding of speciation, we can conclude that phosphonates have no effect on metal speciation or transport because they are mostly Ca and Mg-complexes in natural waters.

Review on Polyherbal Lozenges

Authors- D.S.Deshmane, R.V.Shete, K.J.Kore

Abstract-Lozenges are one of the widely used dosage forms. The advantage of medicated lozenges is the prolongation of the residence time of the medicinal form in the oral cavity, which increases bioavailability, reduces stomach irritation and bypasses first-pass metabolism by the liver. Lozenges are solid dosage form, sweetened or flavored medicines that are taken and kept in the oral cavity to cure an oral infection. Lozenges are a solid dosage form that is meant to dissolve slowly in the mouth for therapeutic effect. The common cold and flu are common illnesses that usually infect the respiratory tract including symptoms such as headache and body ache, fever, drowsiness, runny nose, congestion and cough.

DOI: /10.61463/ijset.vol.12.issue2.156

A Study of Self-Concept and Learning Style on Educational Adjustment of Secondary School Students

Authors- Associate Professor Dr. Suman Dalal

Abstract-The study proposed to define the relationship between Self-Concept , learning style and educational adjustment of secondary school students. Sample was selected 100 secondary school students of Sonepat district. Data analysed by using Mean, SD, t-test and Coefficient of Correlation. Self concept of male students is less than female students and t-test value is 0.39113676 which is not significant. Learning style of male students is more than female students and t-test value is 0.04173611 which is not significant. Educational adjustment of male students is less than the female students and t-test value is 0.501232976 which is not significant .The correlation value of self concept and learning style is -0.011106981. The correlation value of self concept and educational adjustment is – 0.124283155. The correlation value of learning style and educational adjustment is -0.154939827. Result depicted negative correlation between Self-Concept and Learning Style, Self-Concept and Educational Adjustment and Learning Style and Educational Adjustment.

Wine Quality Detection Using Random Forest and Similarity Index

Authors- Vinaya Bobhate, Shubhada Tawade, Pranav Nimbalkar, Yash Gavali, Professor Jyoti Gaikwad

Abstract-The quality of a wine is important for the consumers as well as the wine industry. The traditional (expert) way of measuring wine quality is time-consuming. Nowadays, machine learning models are important tools to replace human tasks. In this case, there are several features to predict the wine quality but the entire features will not be relevant for better prediction. So, our thesis work is focusing on what wine features are important to get the promising result. Machine learning techniques are employed in the analysis of wine attributes to predict wine quality accurately. The quality of wine depends on various attributes, which change with time. Data pre-processing is conducted to enhance data quality, including defining independent and dependent variables, handling missing data, feature scaling, and data splitting. Logistic regression and Random Forest classifier models are applied to predict wine quality.

Improving Cold Chain Logistics and Temperature Control Measure in Aavin Dairy Supply Chain

Authors- Thavithu Simeon, Assistant Professor Dr.N.Jayanthi

Abstract-Enhancing cold chain logistics and implementing robust temperature control measures are pivotal for optimizing Aavin’s dairy supply chain operations. In an era where quality and freshness are paramount, maintaining the integrity of dairy products throughout the supply chain journey is imperative. By strategically investing in advanced refrigeration technologies, efficient transportation systems, and stringent temperature monitoring protocols, Aavin can mitigate the risks associated with spoilage, ensure product safety, and uphold quality standards. This proactive approach not only safeguards the nutritional value and sensory attributes of dairy products but also extends their shelf life, minimizing wastage and maximizing customer satisfaction. Furthermore, a well-maintained cold chain infrastructure enables Aavin to tap into new markets, capitalize on emerging consumer trends, and strengthen its competitive position in the dynamic dairy industry landscape.

DOI: /10.61463/ijset.vol.12.issue2.164

Analyse the Effective Planning in Managing in Purchasing Activities at GS Associates

Authors- Sandhiya. K, Assistant Professor Dr.N. Jayanthi

Abstract-The focus of this paper is on purchasing and supplier involvement in the firm. Using the resource base view of the firm, hypotheses are developed concerning purchasing/supplier involvement, strategic purchasing and firm’s financial performance. A model of the hypothesized relationships is offered and empirically tested using structural equation modeling. The model is tested using data collected in 1999. Each factor in the model is measured by a number of scale items. Based on the results of confirmatory factor analysis, an overall fit of the model to the data is achieved. Both convergent and discriminate validity is demonstrated. The research findings reveal that the hypotheses tested in the model are supported. Purchasing/supplier involvement has a positive impact on strategic purchasing, and strategic purchasing has a positive impact on firm’s financial performance. The paper concludes with some research implications, limitations of the study and suggestions for future research.

DOI: /10.61463/ijset.vol.12.issue2.163

A Study on the in-Transit Motion Period in the Warehouse

Authors- Sivanesh Chinnaiah N, Assistant Professor Dr.N.Jayanthi

Abstract-The operating mechanism of supply chains has been altered by electronic commerce and its related business-to-business transaction capabilities. Information sharing has never been possible on such a large scale thanks to the Internet, frequently at a speed that is too quick for everyday use. Organizations lack the necessary resources to fully utilize data from transportation management systems, which typically store information on the locations of critical supply chain assets like products or vehicles, and warehouse management systems, which provide information on supplier/customer warehouse inventory levels and important customer ordering patterns. Through lowering lead-time variability, improving shipment and inventory accuracy, and cutting down on cycle times for shipping and receiving, the integration of these systems results in global inventory visibility and lower costs as well as better customer service. This study looks at the overall cost savings that suppliers and warehouses may realize from an integrated system’s improved worldwide visibility.

DOI: /10.61463/ijset.vol.12.issue2.162

A Comprehensive Study on Warehouse Functions and Process in TVS Mobility Pvt. Ltd

Authors- K .J. Prashanth, Assistant Professor Dr.N. Jayanthi

Abstract-This comprehensive study delves into the warehouse functions and processes within TVS Mobility Pvt. Ltd., offering insights into the company’s strategies for optimizing efficiency, accuracy, and overall performance in its warehousing operations. TVS Mobility Pvt. Ltd. operates in a dynamic market environment characterized by rapid technological advancements, evolving consumer demands, and competitive pressures. As such, the effective management of warehouse operations is critical to ensuring timely delivery, minimizing costs, and maintaining customer satisfaction. The study employs a mixed-methods approach, combining quantitative analysis of key performance indicators (KPIs) with qualitative assessments of operational practices and strategies.

DOI: /10.61463/ijset.vol.12.issue2.161

Study on Customs Clearance Procedure and Documentation in Chennai Port Authority

Authors- Adam Shajith J, Dr.N.Jayanthi

Abstract-The Chennai Port Authority plays a crucial role in customs clearance procedures and documentation for goods entering or leaving the port. This involves ensuring compliance with customs regulations, processing necessary paperwork such as bills of lading, invoices, and customs declarations, and facilitating inspections and assessments of duties and taxes. Additionally, the port authority may provide assistance and guidance to importers and exporters to navigate the customs clearance process efficiently.

DOI: /10.61463/ijset.vol.12.issue2.160

Towards Sustainable Solid Waste Management: Estimating Resource Recovery Potentials in Bhimdatta Municipality, Kanchanpur, Nepal

Authors- Sagar Hamal

Abstract-Rapid urbanization and population growth in developing countries like Nepal have led to a significant increase in municipal solid waste (MSW) generation. The prevalent practice of haphazardly dumping waste in open sites poses both environmental and financial challenges. To address this issue, a study centered on Bhimdatta municipality of Kanchanpur district in Nepal explores alternative waste management options, specifically emphasizing composting and recycling. The study aims to calculate per capita solid waste generation, estimate the recovery value from MSW through compost, biogas, plastics, and paper in the municipality, and encourage the adoption of alternative solid waste management options such as composting and recycling. Following established guidelines and assumptions, calculations for the recovery values of compost, biogas, plastic, and paper are conducted based on the composition and quantity of MSW generation. The study calculates per capita solid waste generation of 0.02208 kg/cap/day. It evaluates different combinations of solid waste allocation for composting and landfilling to optimize revenue generation. The proposed optimal combination of 0.209 tonne/day for composting and 0.1 tonne/day for landfilling, resulted in a net recovery of NPR 1080.24 per day. Additionally, plastic and paper contribute recovery values of NPR 103.44 and NPR 92.8, respectively. The study highlights a substantial daily recovery value from MSW, totaling NPR 1,276.48 or approximately NPR 4.66 lakhs per year. However, it is essential to consider investment costs for implementing a compost plant and landfill biogas collection system, which can be recovered over a few years of operation.

DOI: /10.61463/ijset.vol.12.issue2.159

MERN Based Text to Image Generator

Authors- Professor Subod Karve, Chetan Bhasney, Priyanka Bonde, Suraj Manda

Abstract-Text-to-image generation is a burgeoning field at the intersection of artificial intelligence and computer vision, aiming to generate realistic images from textual descriptions automatically. This research paper provides a comprehensive review and analysis of text-to-image generation techniques, methodologies, and advancements. Central to text-to-image generation is the encoder-decoder architecture, where textual descriptions are encoded into a latent space representation and subsequently decoded into visual outputs. Techniques such as conditional Generative Adversarial Networks (GANs) and attention mechanisms have been instrumental in improving the quality and coherence of generated images. Furthermore, transformer-based architectures, exemplified by models like GPT (Generative Pre-trained Transformer), have demonstrated promising results in capturing semantic relationships and generating diverse images. Despite progress, challenges persist, including the generation of semantically accurate and diverse images, handling ambiguous textual inputs, and ensuring interpretability. This paper discusses these challenges and proposes future research directions to address them, including the exploration of novel architectures and training methodologies. Through empirical evaluations and critical analysis of existing literature, this research contributes to a deeper understanding of text-to-image generation and lays the groundwork for future advancements in this exciting area of research.

Regenerative Breaking

Authors- Sharayu Mane, Suchit Tapale, Aditya Hattikar, Prathmesh Nangare, Abhijeet Jadhav

Abstract-In area of industrialization failure of motor is not considerable. Three phase induction motor generally suffers from under voltage, over voltage, single phasing and phase reversal problems. Mainly the induction motor need protection from variation of input supply for small motor which is in common use not only in big industries but also in small scale industries. The main objective of the work is to make a cheap and reliable protection system for three phase induction motor. The protection system should protect motor from further to improve the technique to run motor in phase splitting, the over and under voltage, over load and other techniques in monitoring temperature, noise and phase selection. Also protection technologies have been presented.

IOT Based Real Time Health Monitoring System

Authors- Assistant Professor A. Raghavendra Prasad, P. Sudeva Kumar, C. Manohar, J. Samir Kumar, D. Siva Kumar, T. Ranjith Kumar

Abstract-With the emergence of the new coronavirus, healthcare has become an extremely important issue for every nation. Internet of Things (IoT) health monitoring systems are therefore the most effective response to this kind of pandemic. One rapidly expanding field of study, the Internet of Things (IoT) is having a profound impact on the medical field. The rapid development of these methods of remote health care monitoring is directly attributable to the proliferation of smartphones and other wearable sensors. Internet of Things (IoT) health monitoring aids in illness prevention and accurate diagnosis, regardless of the patient’s location, even when a doctor is far away. Here we see a portable physiological monitoring framework that can keep an eye on vitals including the patient’s temperature, heart rate, and room lighting in real time. We presented a continuous monitoring and control system that uses Wi-Fi module-based remote communication to monitor the patient’s status and save their data in a server. The authors suggest an Internet of Things (IoT)-based remote health monitoring system in which authorised personnel may access data stored on any IoT platform and use these values received, the diseases are diagnosed by the doctors from a distance.

Wifi Dumper Tool

Authors- Navaneeth Pai V, Navodith V Bhat, Prajwal D, Assistant Professor K.R Mamatha

Abstract-In the realm of cybersecurity, the assessment of network vulnerabilities remains a critical endeavor. This paper presents the design and implementation of a WiFi dumper and de-authentication tool aimed at enhancing network security evaluation methodologies. Leveraging Python programming language and readily available open-source libraries, the tool facilitates the collection of WiFi packets and enables de- authentication attacks on wireless networks. Through a comprehensive analysis of existing methodologies and tools, our solution offers enhanced flexibility, efficiency, and usability in assessing WiFi network vulnerabilities. The tool’s efficacy is demonstrated through practical experimentation, showcasing its ability to uncover potential security loopholes and aid in the formulation of robust defense strategies. This research contributes to the arsenal of cybersecurity professionals, offering a valuable resource for network security assessment and strengthening the resilience of wireless networks against malicious intrusions.

GPT 2 Implementation

Authors- Rucha Bhide, Jhanvi Sandeep, Anjali Kakekar, Professor Jyoti Gaikwad

Abstract-This paper aims at implementing GPT-2, a state-of-the-art natural language processing model developed by Open AI, for multiple applications in natural language understanding and generation. GPT-2, which stands for “Generative Pre-trained Transformer 2,” is known for its ability to generate coherent and contextually relevant text, making it a powerful tool for tasks such as text generation, language translation, sentiment analysis, and more. The project involves training and fine-tuning the GPT-2 model on specific datasets to adapt it to particular tasks. The steps required in this project will be data preprocessing, model training, and hyper parameter tuning. The trained model can be used for a wide range of applications such as chat bots, content generation, translation, and text summarization. It will address the computational requirements for deploying GPT-2 in real-world applications and potential scalability challenges. In conclusion, the implementation of GPT-2 offers the opportunity to harness cutting-edge natural language processing capabilities for a multitude of applications. However, it is crucial to balance the power of this technology with ethical considerations and responsible usage, which will be a central focus of this project.

DOI: /10.61463/ijset.vol.12.issue2.167

Enhanced Voltage Control with Cascaded H-Bridge DSTATCOM in Electric Grid

Authors- Asst.Prof. G.S.N.M.Venkatesh, S. Sravya, K. Janaki, D.Devendara Sai, G.Jagadheesh Kumar

Abstract-This paper examines the performance of a STATCOM in improving power quality and balancing network operations in the distributed energy sector. It explores the structure and characteristics of STATCOMs, emphasizing their role in maintaining balanced network operations through reactive power generation. Additionally, the study delves into the significance of Pulse Width Modulation in enhancing device efficiency and showcases its advantages. A novel hybrid D-STATCOM topology is proposed to address unbalanced and nonlinear loads in distribution systems, aiming to improve power transmission efficiency by utilizing Cascaded-H-Bridge and other complementary methodologies. The paper also discusses control strategies and simulation operations using MATLAB, providing insights into practical implementation for enhanced grid stability and power quality enhancements. By using Cascaded H-Bridge D-STATCOM topology total harmonic distortion in the currents has been decreased to 0.9%.

DOI: /10.61463/ijset.vol.12.issue2.166

Review of Design and Fabrication of On-Chip Low Power Microstrip Filter for Enhanced Wireless Communication

Authors- M.Tech Scholar Ms. Madhu Vishwakarma, Assistant Professor Ms. Deepali Sahu

Abstract-A filter is an electronic device designed to allow a specific range of frequencies to pass through while attenuating frequencies outside of this range. In the realm of bandpass filters, there exist two main categories: active bandpass filters and passive band pass filters. Active bandpass filters require an external power source and typically employ active components like transistors and integrated circuits. On the other hand, passive band pass filters do not require external power and are composed solely of passive components such as inductors and capacitors. In recent years, there has been a growing trend in research focused on microstrip bandpass filters within wireless communication technology. Particularly, bandpass filters designed from coupled lines have gained significant traction due to their advantages such as planar structure, straightforward synthesis procedures, and repeatability. Among various microstrip filter structures, the parallel-coupled microstrip filter stands out as one of the most popular choices. This popularity stems from its simplicity in design and implementation, as it does not necessitate short circuits. In this project, the focus will be on designing and simulating the structure of a parallel-coupled microstrip bandpass filter using Ansoft Designer software before proceeding to the fabrication stage. The design and performance of the filter will be thoroughly analyzed based on all relevant parameters.

A Survey on Cooperative Spectrum Sensing Techniques and Requirements

Authors- Research Scholar Atul Garg, Professor Dr.Pritaj Yadav

Abstract-The rise of cloud computing, facilitated by virtualization technologies, offers substantial opportunities for cost-effective hosting of virtual resources without the need for physical infrastructure ownership. Within cloud data centers, a variety of heterogeneous commodity servers typically accommodate numerous IoT devices with diverse specifications and fluctuating resource demands. However, this diversity may lead to imbalanced resource utilization across servers, potentially resulting in performance degradation and violations of service level agreements (SLAs). As a remedy, edge computing has emerged to mitigate these challenges by redistributing the workload, thereby enhancing overall system performance. This paper has brief the edge computing requirement with its working architecture. Further paper has brief different techniques of load balancing used in balancing. Various models proposed by researcher for improving the performance of the edge network was also summarized in the paper. Finally, various evaluation parameters were also listed in the paper for the comparison of load balancing models.

Ultra Sound Nerve Segmentation of Brachial Flexes Using RNN

Authors- Associate Professor Dr. A Vijayaprabhu, Scholar C Sasi Kumar, Scholar S Sailaja, Scholar R Rajalakshmi, Scholar D Siva Lokesh, Scholar D Reddy Pradeep Reddy

Abstract-In order to accurately diagnose and track a variety of medical disorders, ultrasound imaging is essential. In the case of brachial flexes, nerve segmentation is a crucial duty for correct assessment. In this work, we provide a novel method for ultrasonic nerve segmentation in brachial flexes that makes use of recurrent neural networks (RNNs). The suggested approach entails creating a unique RNN architecture that draws inspiration from the U-Net framework in order to efficiently capture temporal and spatial relationships in the ultrasound picture sequences. Model training and assessment were conducted using a dataset that included manually identified nerve areas and ultrasound pictures of brachial flexes. Intersection over Union (IoU), dice coefficient, and pixel accuracy are examples of common segmentation metrics that were used to evaluate the effectiveness of the suggested method. Our experimental results reveal that RNNs are successful in catching the fine structural characteristics of nerves in ultrasound pictures, with promising segmentation accuracy. Furthermore, visual inspection qualitative analysis validates the model’s accuracy in defining nerve boundaries. All things considered, this study advances automated ultrasound nerve segmentation methods, which may find use in clinical settings to improve therapeutic and diagnostic approaches.

Design and Fabrication of On-Chip Low Power Microstrip Filter for Enhanced Wireless Communication

Authors- M.Tech Scholar Ms. Madhu Vishwakarma, Assistant professor Ms. Deepali Sahu

Abstract-Abstract: A time-domain technique is proposed for the design of ultra wide band (UWB) micro strip filters. The design method utilizes the reflectance coefficient S11 specified in the frequency domain. Given the frequency response of a UWB filter, its time-domain response is estimated using a series of UWB pulses. These UWB pulses, Gaussian in nature with identical bandwidths but varying delays, are employed to ascertain the filter coefficient’s value as a function of the number of pulses. The technique leverages a time-domain reflection scenario with very narrow Gaussian pulses to obtain the impulse response of the system for pulse duplication, considering pulse amplitude and delay. The desired low-pass filter performance is evidenced by the sharp S11 roll-off, commencing around 5GHz, with some deviation observed near the higher end of the frequency range. In the S11 results, the stop band for the calculated curve is slightly narrower than the measured results.

Enhanced Voltage Control with Cascaded H-Bridge DSTATCOM in Electric Grid

Authors- Assistant Professor G.S.N.M.Venkatesh, S. Sravya, K. Janaki, D.Devendara Sai, G.Jagadheesh Kumar

Abstract-Abstract: This paper examines the performance of a STATCOM in improving power quality and balancing network operations in the distributed energy sector. It explores the structure and characteristics of STATCOMs, emphasizing their role in maintaining balanced network operations through reactive power generation. Additionally, the study delves into the significance of Pulse Width Modulation in enhancing device efficiency and showcases its advantages. A novel hybrid D-STATCOM topology is proposed to address unbalanced and nonlinear loads in distribution systems, aiming to improve power transmission efficiency by utilizing Cascaded-H-Bridge and other complementary methodologies. The paper also discusses control strategies and simulation operations using MATLAB, providing insights into practical implementation for enhanced grid stability and power quality enhancements. By using Cascaded H-Bridge D-STATCOM topology total harmonic distortion in the currents has been decreased to 0.9%.

Role of Industrial Training Institute’s in Youth Development and ITI Ecosystem of India

Authors- Er.Vikas Sanjay Nirmal

Abstract-Abstract: The Government of India sees skill development as a crucial issue to solve the unemployment problem in the country. Although there are approximately 15,024 Government and Private Industrial Training Institutes (ITI’s) in the country with a total capacity of 2.65 million seats, emphasis should be placed on skill development in the country. Our hon’ble Prime Minister, shri. Narendra Modi ji, announced new plans to make India the best destination for global companies and investors through Make in India and Technological India Campaigns, ITI’s role in creating skilled workers and entrepreneurs. Its benefits cannot be ignored at all. Recognizing the role of ITIs in creating skilled workforce in the country, 25 ITI qualified entrepreneurs have been selected as National Vocational Training Brand Ambassadors for the Pandit Deen Dayal Upadhyay Shramev Jayate™ programme. In some developed countries such as South Korea, Japan and Germany, the rate of educated workers is 96%, 80% and 75% respectively. In contrast, only 10% of all Indian workers have formal or informal education. 90 percent of them have not received any training. To achieve our goal of Make in India, there needs to be more vocational training in the country.

Comprehensive Study on Import and Export Documentation Procedures on Electronic Parts

Authors- Adithyan B, Assistant Professor Dr. N.Jayanthi

Abstract-Abstract: This study delves into the intricacies of import and export documentation procedures concerning electronic parts, aiming to enhance efficiency and compliance in global trade operations. The primary objective is to analyze and optimize import, export, and customs clearance procedures, with a focus on identifying documentation requirements, understanding customs clearance processes, and examining tariff and duty implications. By conducting a comprehensive examination of these procedures, this study seeks to provide valuable insights for businesses operating in the electronic parts industry, facilitating smoother transactions, minimizing risks, and maximizing profitability. Through a combination of qualitative analysis and empirical research, this study sheds light on key challenges and opportunities within the import and export documentation landscape, offering practical recommendations for stakeholders to navigate regulatory complexities and achieve operational excellence in international trade.

Improving Exports Customs Compliance and Regulatory Challenges

Authors- Sethu Ashwin.L, Assistant Professor Dr.N.Jayanthi

Abstract-Abstract: This essay explores the vital topic of improving exportation-related customs compliance and overcoming regulatory obstacles. As a result of globalization propelling the growth of international trade, it is now critical for companies hoping to succeed in the global economy to guarantee seamless compliance with customs laws and handle regulatory obstacles. In order to improve export customs compliance, the article looks at a number of tactics and best practices, such as utilizing technology to expedite procedures, encouraging stakeholder participation, and keeping up with changing regulatory frameworks. It also looks at the main regulatory issues that exporters deal with, like complicated documentation requirements, trade barriers, and shifting trade laws. Businesses can reduce risks by proactively addressing these issues and putting in place efficient compliance systems.

Enhancing Supply Chain Visibility and Efficiency: A Strategic Analysis at Parekh Integrated Service Pvt Ltd

Authors- Keshav V, Assistant Professor Dr.N.Jayanthi

Abstract-Abstract: Parekh Integrated Services Pvt. Ltd. (PISPL) faces the challenge of optimizing its supply chain visibility and efficiency. This strategic analysis will delve into a systematic assessment of PISPL’s current supply chain processes, pinpointing areas for improvement and evaluating potential solutions. The analysis will employ a multi-pronged approach to identify bottlenecks and inefficiencies. This may involve mapping the entire supply chain network, analyzing inventory management practices, and evaluating logistics operations. By pinpointing critical areas for intervention, the analysis will recommend a course of action that enhances both visibility and efficiency.

A Comprehensive Analysis on Optimizing Efficiency and Warehouse Effectiveness

Authors- Manoj Krishnan J, Assistant Professor Dr. N. Jayanthi

Abstract-Abstract: This work presents relevant research concerns in accordance with the suggested taxonomy and thoroughly examines the current state-of-the-art warehousing literature. Performance metrics influencing each aspect of warehousing and overall productivity are ignored in favor of a broad focus on warehouse design and operational concerns in all prior studies. Therefore, the purpose of this article is to explore the impact of the various approaches and performance measurements on the logistics system as a whole. Important conclusions have been drawn from each section’s conclusion, and the final section summarizes these conclusions together with unified research recommendations. Research in existing and uncharted areas of warehouse operations and management would benefit from having the suggested gaps as a future road map.

A Study of Warehouse Efficiency and Effectiveness

Authors- Harish. M R, Assistant Professor Dr. N. Jayanthi

Abstract-Abstract: This paper examines key strategies for enhancing efficiency and effectiveness in 3PL warehouses, such as layout optimization, inventory management techniques, workforce training, and technology integration. The role of advanced technologies like warehouse management systems (WMS) is highlighted in driving improvements. Furthermore, the significance of performance metrics and continuous improvement initiatives is discussed to sustain and further enhance warehouse operations. Overall, this abstract underscores the importance of balancing efficiency and effectiveness in 3PL warehouses to achieve competitive advantages and customer satisfaction in today’s dynamic logistics landscape.

Prediction of COPD Using Deep Learning

Authors- Ms. Jeya Sudikshaa M M, Ms. Nivetha S, Ms. Priyanka R, Assistant Professor Mr. Mustafa Nawaz S

Abstract-Abstract: In recent years, assistive solutions to difficulties in the medical arena have been made possible in large part by technologies like deep learning and machine learning. Through the use of audio analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Given a scarcity of skilled human resources, medical professionals are grateful for this technological assistance since it helps them handle an increasing amount of patients. In addition to life-threatening diseases like diabetes and cancer, the impact of respiratory illnesses is progressively increasing and compromising people’s lives in society. X-rays of the chest and audio recordings of respiratory sounds have been shown to be extremely beneficial in the diagnosis and treatment of respiratory ailments since early detection and immediate intervention are crucial. The aim of the research that is being discussed is to use methods of deep learning based on convolutional neural networks in order to support physicians by providing a thorough and precise examination of medical respiratory audio recordings for identifying the presence of chronic obstructive pulmonary disease. We have used Librosa machine learning library features, comprising MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q), and Chroma CENS, in the conducted evaluations. The severity of the diagnosed disease, such as mild, moderate, or acute, might also be determined by the system through which the diagnosis was given. The results of the study confirm that the specified deep learning strategy works. An increased 93% ICBHI score has been obtained for the system’s classification accuracy. Additionally, we utilized K-fold Cross-Validation with ten splits in the experiments to achieve the greatest the effectiveness of the deep learning strategy that was mentioned.

A Study on the Export & Import Documentation Procedure

Authors- Manibharathi.D, Assistant Professor Dr .N.Jayanthi

Abstract-Abstract: International trade hinges on meticulous documentation, ensuring compliance with regulations and facilitating smooth cross-border transactions. This overview delves into crucial procedures and requirements for global commerce. Exporters must understand varied export controls and use systems like HS codes to classify goods, alongside preparing essential documents like commercial invoices and certificates of origin. Importers face challenges securing permits and licenses, necessitating core documents such as commercial invoices and bills of lading for customs clearance. Both exporting and importing may require certifications like inspection certificates and phytosanitary certificates. Navigating export and import documentation demands attention to detail and a deep grasp of trade regulations. Errors can lead to delays or fines, underscoring the importance of accuracy. In an era of evolving trade dynamics, staying updated on regulatory changes is crucial. This guide emphasizes adhering to best practices to mitigate risks and enhance efficiency, enabling businesses to thrive globally. Prioritizing accurate documentation empowers businesses to navigate international trade complexities with confidence and success.

Advancements in Building Information Modeling Software a Comprehensive Study

Authors- Assistant professor Dr. B. Raghunath Reddy, Mr. S. Sanwar Hussain, Ms. K. Meghana

Abstract-Abstract: This project aims to delve into the advancements and functionalities of Building Information Modeling (BIM) software solutions. BIM has revolutionized the architecture, engineering, and construction industries by enabling stakeholders to collaboratively plan, design, construct, and manage buildings and infrastructure. Through an extensive review of existing BIM software, this project aims to identify key features, emerging trends, challenges, and future prospects in the field. Building Information Modeling (BIM) software has become an indispensable tool in the contemporary landscape of construction and architecture. This project delves into the significance, functionalities, and advancements of BIM software, examining its pivotal role in streamlining the entire lifecycle of building projects. By enabling collaborative planning, design, construction, and management processes, BIM software facilitates enhanced efficiency, accuracy, and sustainability in the built environment. This study conducts a comprehensive review of prominent BIM software solutions, analyzing their key features, integration capabilities, and industry adoption. It explores the evolution of BIM technology, from basic 3D modeling to sophisticated parametric design and data-driven decision-making. Furthermore, the project investigates emerging trends such as the integration of artificial intelligence, cloud-based solutions, and augmented reality within BIM platforms. Challenges and limitations surrounding BIM implementation, including interoperability issues and data security concerns, are also addressed. Through real-world case studies and success stories, the project illustrates the practical applications and benefits of BIM software across diverse construction projects, ranging from commercial buildings to infrastructure development. Moreover, this project anticipates future trends and opportunities in the realm of BIM, including its role in sustainable design, smart cities, and digital twins. Recommendations for stakeholders, including architects, engineers, contractors, and policymakers, are provided to harness the full potential of BIM software and navigate its complexities effectively. Overall, this project aims to contribute to the understanding of Building Information Modeling (BIM) software as a transformative tool in modern construction and architecture, facilitating informed decision-making and innovation in the built environment.

Multi Disease Net: A Web-Based Platform for Predictive Modeling of Multiple Diseases using Machine Learning and Deep Learning Algorithms

Authors- Assistant Professor K L Sujitha, Shreeniketan Nayak, Vinay B V, Mohammed Zaid, Venkatesh M S

Abstract-Abstract: The project “Multi Disease Net” showcases the transformative potential of machine learning and deep learning techniques in healthcare, particularly in disease prediction. Focusing on the simultaneous prediction of multiple diseases, our initiative seeks to redefine early diagnosis and treatment, leading to improved patient outcomes and reduced healthcare expenses. This endeavor encompasses a thorough examination of various machine learning models and commonly accessed data sources for disease prediction. Noteworthy is the utilization of feature selection algorithms to identify key factors contributing to accurate disease prediction, thereby enhancing model interpretability. Through rigorous evaluation using real-world datasets, our project demonstrates the commendable accuracy, sensitivity, and specificity of the model in predicting multiple diseases concurrently. This research provides valuable insights into the advantages, obstacles, and future prospects of harnessing machine learning for multi-disease prediction in the healthcare domain.

DOI: /10.61463/ijset.vol.12.issue2.170

Smart Disease Prediction and Analyzer

Authors- Rohit Mithagari, Aditya More, Ashitosh Yadav, Jayant Sawarkar

Abstract-Abstract: The Smart Disease Prediction Project represents a cutting- edge healthcare initiative that harnesses data and technology to revolutionize healthcare outcomes. It focuses on early disease detection, efficient resource allocation, and informed decision-making for patients and healthcare professionals. By leveraging advanced data analysis, machine learning, and predictive modeling, the project identifies disease patterns, risk factors, and trends in healthcare data. This proactive approach enhances the quality of life for individuals, reduces the strain on healthcare systems, and promotes preventive healthcare measures. The Smart Disease Prediction Project embodies the power of data-driven innovation in the pursuit of better health outcomes. Machine learning and continuous data analysis play pivotal roles in this project’s evolution. The system evolves and refines its predictive capabilities over time as it ingests new data and gains deeper insights into the ever-changing healthcare landscape. In summary, the Smart Disease Prediction Project is an innovative and forward- thinking initiative that exemplifies the transformative potential of data and technology in healthcare. It rises to meet various healthcare challenges, emphasizing early disease detection, efficient resource allocation, and informed decision-making, all of which have the potential to yield superior health outcomes and elevate the quality of life for individuals. This project embodies the profound impact of data-driven innovation on the journey toward better health and well- being.

Identifying the Product Damages in Glocol Delivery Solution Pvt Ltd Warehouse

Authors- Karthick.K, Assistant Professor Dr. N. Jayanthi

Abstract-Abstract: This project looks at box damages identification at warehouse with a particular emphasis on kitchen appliances. Food processors, mixer grinder, wet grinder, glass top stove & stainless-steel stove, pressure cookers and chimneys are among the kitchen appliances that can sustain damage from handling and storage in a warehouse. The goal of this project is to create a through method for effectively diagnosing and recording product damages. It may reduce financial losses, raise customer happiness, and optimize warehouse productivity by efficiently recognizing product faults. A standardized damage identification system with explicit documentation methods will be proposed by the project. In addition, the research will investigate possible ways to lessen the like hood of damage occurring. This could entail suggestions for better packing supplies, safe handling procedures for warehouse workers, and well-organized storage spaces. It is possible to guarantee the integrity and quality of their inventory of kitchen appliances by putting in place a strong damage identification and mitigation strategy.

A Investigating Strategies for Reducing Inbound Costs in Packaging at Radha Engineering Works, Chennai Pvt Ltd.

Authors- S.A. Balamurugan, Assistant Professor Dr.N.Jayanthi

Abstract-Abstract: The present study explores the challenges faced by Radha Engineering Works Chennai Pvt Ltd. (REWCPL) in managing inbound packaging costs and proposes strategies to mitigate these challenges. Inbound packaging plays a pivotal role in the logistics and supply chain operations of manufacturing companies, impacting both cost efficiency and product quality. This research adopts a mixed-method approach, combining qualitative interviews with key stakeholders and quantitative analysis of inbound packaging costs and processes. The findings reveal several key factors contributing to high inbound packaging costs, including inefficient packaging design, over-reliance on non-standardized packaging materials, and suboptimal handling practices. Based on these findings, the study proposes a comprehensive framework for reducing inbound packaging costs, encompassing initiatives such as standardization of packaging materials, optimization of packaging designs for efficiency and sustainability, and implementation of best practices in handling and storage. Additionally, the research underscores the importance of collaboration with suppliers and logistics partners to streamline packaging processes and achieve cost savings. The proposed strategies offer practical insights for REWCPL and other manufacturing firms seeking to enhance efficiency and competitiveness in their inbound packaging operations.

A Review on Pushover Analysis of Framed Structure Building Using ETABS Software

Authors- Scholar bhoopendra Singh, Associate Professor R.K. Grover

Abstract-Abstract: Non-linear analysis is necessary to evaluate the seismic demand of the proposed or existing structure, as linear analysis is inadequate in assessing the seismic demand under severe earthquakes. In this article non- linear static analysis (pushover analysis)has been done to understand the behavior 8 multistoried residential building located in different seismic zones of India having similar geometrical properties using ETABS2019. The behavior of multistoried building has been investigated in terms of force- displacement relationships, inelastic behavior of structure and sequential hinge formations etc. Plastic hinge formation gives real behavior of the structure. From the analysis results, it was observed that, when of structure. Results indicate that, the damage in a building is limited and columns at the lower stories can be retrofitted based on the importance of the structure. If the zone increasing, base shear, story displacement, story drift, story shear, monitored displacement and auto lateral loads has been increased gradually, indicating the severity of seismic activity. In this analysis, firstly hinges were formed in beams and then in columns at ground floor of structure because of column member of the structure are critical in seimic design. The hinge formation propagates from ground floor to middle floor columns and then finally to the upper floor columns. The propagation of hinges from lower stories to upper stories leads to collapse.

Customer Segmentation and Targeting Marketing Strategies

Authors- Saransh Jain, Pramod Vishwakarma

Abstract-Abstract: A key tactic in marketing is customer segmentation, which aims to comprehend and meet the various demands of customers. The application of K-means and hierarchical clustering, two well-liked clustering approaches, for consumer segmentation and ensuing targeted marketing tactics is examined in this research study. After doing a thorough literature research and practical analysis, we clarify the methods and advantages of using these clustering algorithms for consumer segmentation. The paper starts out by going over the theoretical underpinnings of hierarchical clustering and K- means, explaining each method’s advantages and disadvantages in terms of consumer data segmentation. We give a detailed explanation of how these algorithms can be applied successfully in practical marketing settings, stressing the significance of feature selection, data preprocessing, and validation methods. In order to demonstrate how K-means and hierarchical clustering are applied in various different industries, including retail, e- commerce, and telecommunications, the research also explores real-world case studies and simulations. By using these examples, we hope to demonstrate how companies can better target particular customer segments using marketing tactics that are tailored to yield meaningful insights from segmented customer data. The article also addresses new developments in customer segmentation, such as the incorporation of machine learning methods and moral issues pertaining to data privacy and consent from customers. We emphasize how segmentation tactics must be continuously improved and adjusted in response to shifting customer and market factors. This research piece concludes by providing a thorough overview of K-means and hierarchical clustering-based targeted marketing tactics and client segmentation.

TCCL-Dense Fuse: Infrared and Water Vapour Satellite Image Fusion Model Using Deep Learning

Authors- Rakshana Kasikumar, Sakthi Sree Raghavan, Professor Dr Sathiya Priya S

Abstract-Abstract: Tropical cyclone (TC) monitoring relies on infrared and water vapour channels; this work offers a novel method for fusing satellite images in this area. We present a Dense Net-based TCCL-Dense Fuse model that makes use of infrared radiation and data on the distribution of water vapour. The fusion quality is optimized by our approach by using multi scale structural similarity and brightness temperature gradient. Superior performance in maintaining data gathered from both channels was shown via evaluation utilizing seven objective metrics. Our evaluation of the model’s performance in TC center localization also shows improved accuracy over state-of-the-art approaches. In locations prone to tropical cyclones, the TCCL- Dense Fuse model, together with Alex Net, ResNet50, UNet, Mobile Net, and GoogLe Net, provides a useful tool for disaster management by allowing for more effective warning and monitoring systems. The results of this study have important significance for the development of methods for analyzing satellite images, which may help lessen the severity of the effects of natural catastrophes.

Investigation of Linear Dynamic Analysis and Ductile Design of High Rise Structure as Per Revised Indian Code

Authors- Ms. Ghodake Swapnali, Professor Dr.C.P.Pise

Abstract-Abstract: This thesis gives a comparative analysis of ductile column design using the New IS 13920-2016 and the Old IS 13920-1993 standards. The recent earthquakes in India revealed unequivocally that conventional structural design and construction techniques fail to meet fundamental seismic resistance standards. The use of ductile design and detailing methods in conventional construction is a critical topic that requires attention. The ductility of reinforced concrete structures as a whole is a difficult topic. However, specific design factors and reinforcing details may be used in particular critical spots of the building structure to reduce seismic damage and life-threatening collapse. The approaches are straightforward, affordable, and extensively detailed in the Indian Bureau of Standard Code of Practice’s (IS13920). It is recommended to conduct a comparative assessment of multi-story framed buildings, including their column c/s aspect ratio and minimum column requirements, using the response spectrum analysis in accordance with the provisions of both the new IS 13920-2016 and the older IS 13920-1993. Additionally, it is recommended to analyse and design multi-story buildings using computational software such as ETABS and compare characteristics such as time period, base shear, storey displacement, mode shape.

Study of Cold Storage Warehouse an Effectiveness of Outbound Logistics at Hatsun Agro Products Ltd

Authors- Aswin Bharathi.P, Assistant Professor Dr.N. Jayanthi

Abstract-Abstract: The study looks into the complexities of cold storage warehouse management and the effectiveness of outbound logistics operations at Hatsun Agro Products Ltd, a major player in the dairy and food processing industries. This research provides significant insights into optimizing the cold storage industry’s supply chain processes by conducting a complete investigation of numerous aspects such as transportation, order receipt, information flow, finance flow, storage equipment, and gas emissions. Transportation is critical in the smooth transfer of commodities from manufacturing facilities to cold storage warehouses, and finally to distribution hubs and marketplaces. This study provides light on increasing efficiency while reducing costs and delivery lead times by evaluating Hatsun Agro’s transportation tactics, which include mode selection, route optimization, and carrier management. Supply chain management relies heavily on information and financial flow, especially in cold storage facilities. This study examines Hatsun Agro’s information and finance flow systems to evaluate the integration of technologies such as warehouse management systems (WMS) and enterprise resource planning (ERP) software to enable real-time tracking, inventory management, and financial transactions.

System for Detecting Intrusions Utilizing an Approach Based on Machine Learning

Authors- Research Scholar Kamini Sharma, Assistant Professor Virendra Verma

Abstract-Abstract: Occasional occurrences of random destructive acts targeting individual computers or entire networks are observed on the internet. With the exponential growth of computer connections, keeping pace with such threats becomes increasingly challenging. Security concerns in cyberspace mirror those in physical environments. The Intrusion Detection System (IDS) serves as a critical tool for identifying and analyzing such host00ile behaviors across networks, aiding in attack detection and intruder identification. To enhance IDS performance, various machine learning (ML) techniques have been integrated into intrusion detection systems. This study proposes an efficient IDS strategy leveraging Principal Component Analysis (PCA) and the Convolutional Neural Network (CNN) classification algorithm. PCA organizes data by reducing its dimensionality, while CNN efficiently categorizes data using random forest. Tests conducted on the KDD Knowledge Discovery Dataset demonstrate the superiority of the proposed method over alternative approaches such as Support Vector Machines (SVM), Naive Bayes, and Decision Trees in terms of accuracy. Results from our proposed method reveal a performance time of 2.41 minutes, an accuracy rate of 98.63%, and a mistake rate of only 0.18%. This approach showcases promising potential for enhancing IDS effectiveness, offering robust defense mechanisms against cyber threats in network environments.

Optimizing Load Balancing in Cloud Computing: A Hybrid Approach

Authors- Research Scholar Ankit Ukey, Assistant Professor Jitendra Khaire

Abstract-Abstract: Cloud registration facilitates the exchange of information and provides consumers with assets, charging them only for the resources they use. Cloud computing stores data and maintains information accessibility. However, in open circumstances, information hoarding escalates rapidly. Stack adjustment serves as a test during cloudy weather, while load adjustment distributes dynamic workloads across hubs to prevent overloading, thereby optimizing resource usage and enhancing system performance. A majority of available calculations enable stack adjustment and improved asset utilization in cloud computing, utilizing memory, CPU, and system stacks. Load adjustment detects overloaded hubs and redistributes the load to underloaded ones, ensuring equitable resource allocation across the shared system’s cloud data centers. This study proposes a hybrid load balancing method, combining Honey Bee (HB) with Particle Swarm Optimization (PSO), aiming to achieve an acceptable response time. The hybrid algorithm is tested using the CloudSim simulator, demonstrating faster reaction times compared to Honey Bee (HB) and Particle Swarm Optimization (PSO) load balancing techniques. The research evaluates response time, request servicing, data center loading, and cost in virtual machines using the simulator.

Safe Zone Area Prediction for Glacial Lake Outburst Floods

Authors- Mr. Krish Jalwal

Abstract-Abstract: Glacial Lake Outburst Floods (GLOFs) pose a significant threat worldwide, intensified by the escalating impacts of global warming. As glaciers retreat and thin due to climate change, new glacial lakes are forming in regions like India, Pakistan, Peru, China, and Europe. These GLOF disasters have occurred across various regions worldwide, including the Himalayas, Peruvian Cordillera Blanca, Chilean Patagonia, the Canadian Rockies, the Alps, and the Tianshan Mountains. With over 21 recorded GLOF disasters in the past 65 years and nearly 30,000 fatalities in the Peruvian Cordillera Blanca alone, proactive measures are essential to mitigate risks and safeguard vulnerable populations. This proposal aims to leverage Transfer Learning strategies of AI to predict safe zone areas, contributing to proactive risk management strategies and protecting vulnerable communities.

DOI: /10.61463/ijset.vol.12.issue2.169

A Study on Mechanical Properties of Banana Fibre Reinforced Composite

Authors- Mr. Siva Jothi S, A.Sathya, N. Shindhuja, M. Uma Maheswari

Abstract-Abstract: Natural fibres are available in abundance in nature and can be used to reinforce polymers to obtain light and strong materials. Natural fibres from plants are beginning to find their way into commercial application such as automotive industries, household application. In this project banana fibre is used for reinforcement of the composite. The aim of the project is to determine various mechanical properties such as strength fatigue and shear stress of natural banana fibre reinforced composite. In order to conserve natural resources and economize energy, weight reduction has been the main focus of machine parts manufacturers in the present scenario. Weight reduction can be achieved primarily by the introduction of better material, design optimization and better manufacturing processes. The banana fibre reinforced banana composite is one of the potential items for weight reduction of about 20%-30%. The introduction of banana composite materials was made it possible to reduce the weight without any reduction on load carrying capacity, more elastic strain energy storage capacity and high strength to weight ratio as compared with those of steel. The objective of this project is to investigate various mechanical properties such ad strength, fatigue, shear and thermal stresses and testing by means of NDT (Non-Destructive testing).

Unveiling the Mole: A Comprehensive Exploration of its Role and Applications in Chemistry

Authors- Azza Asem, Aya Ahmed, Rawan Mohamed, Hager Ibrahim, Dr. Doaa Abd EL Monam

Abstract-Abstract: This research paper delves into the pivotal role of the mole in chemistry, elucidating its historical development, theoretical foundations, and practical applications. The mole concept, which dates back to the early 20th century, has revolutionized the field by bridging macroscopic and microscopic measurements and providing a universal language for quantifying substances. Through an in-depth exploration of the mole’s significance, this paper highlights its implications for understanding chemical reactions, determining stoichiometry, and predicting reaction outcomes. It also investigates various methods for converting between mass and molar quantities, offering readers valuable tools for navigating complex chemical measurements. The study aims to demystify the mole unit and demonstrate its relevance in contemporary chemical research and applications.

DOI: /10.61463/ijset.vol.12.issue2.168

Algo Graph: Exploring Path Finding Algorithms through Interactive Visualization

Authors- Mohak Chauhan, Srijan Verma, Eshwar Chawda, Jayendra Sonkusare

Abstract-Abstract: Path finding algorithms are essential across various domains, demanding a deep understanding from students, researchers, and practitioners. Algo Graph, a novel web application built with React and JavaScript, aims to bridge the gap between theory and practice in these algorithms. This paper explores the development, features, and implications of Algo Graph, focusing on Dijkstra’s, Depth-First Search (DFS), and Breadth-First Search (BFS) algorithms. Algo Graph’s inception addresses the need for practical understanding of path finding algorithms. Leveraging modern web technologies, Algo Graph provides a dynamic environment for users to visualize algorithm execution step-by-step. The application’s architecture facilitates real-time visualization, offering adjustable speed and features like weighted grids. User testing and feedback reflect Algo Graph’s effectiveness in enhancing understanding, despite some developmental challenges. In the discussion, the paper interprets findings within the algorithm visualization and educational technology context, standing out as a significant advancement in algorithm visualization, offering a user-centric approach to understanding complex algorithms. By providing a visually engaging platform, Algo Graph empowers users to explore Dijkstra’s, DFS, and BFS algorithms comprehensively. This paper underscores the importance of interactive visualization tools and lays groundwork for future algorithm education and research advancements.

Pneumatic Exoskeleton Arm

Authors- Assistant Professor Shivapradeep Muthupandi, Raj Raghunath Yadav, Bhavesh Babulal Suthar, Harishankar kalikinkar Mishra

Abstract-Abstract: The future of technology is heavily reliant on research and development. The concept of anthropomorphic equipment arose from intensive research and a desire to attain powers beyond human competence. The construction of the “Human Exoskeleton” satiated the hunger for invincible power. A Human Exoskeleton, also known as Powered Armour, Exo-frame, Hard Suit, or Exosuit, is a wearable mobile machine powered by a system of motors, pneumatics, levers, or hydraulics that amplifies the operator’s force and allows them to have superhuman strength. This notion has a lot of room for improvement and is an intriguing research topic. The term “weakness” can be eradicated from human perception with the advent of this technological technology. This initiative intends to empower physically challenged individuals by assisting them in harnessing the power of pneumatics to increase the strength of their arm. It is possible to considerably minimize the amount of physical work required by an individual while lifting loads weighing up to thirty kilograms by introducing a pneumatic cylinder into the system. The concept was realised for one arm but can be expanded to include the second arm as well. The exoskeleton arm is a subassembly of a full portable suit that allows external weights to be transferred to stronger areas of the body via precisely placed linkages and joints. The arm is meant to be an auxiliary source of strength for anyone looking to get back into shape or execute strenuous domestic activities.

Differences in Knowledge and Practice Regarding Arecanut Use between Students of a Public and Private School, in Male’ City, Maldives

Authors- Abdul Azeez Hameed

Abstract-Abstract: Objective: To identify differences in knowledge on health effects and practice towards arecanut use between students of public and private school in Male’ city, Maldives. Methods: The study was a part of a cross-sectional survey conducted using pre-coded questionnaire at 4 different schools in Male’. For identifying differences in knowledge and practice related to arecanut consumption between a public and private school, one public and the only private school included in original study were selected for this study for comparison. The sample size was recalculated by utilizing total population of both and 288 sample subjects were used in data analysis. The sample subjects were selected using simple random sampling for private school, while census sampling used for public school. Descriptive statistics were presented using SPSS version 21.0. Results: Out of 288 school goers 144 (50%) were boys and 144 (50%) were girls. Secondary school children studying in selected public school in Male’ city have better knowledge on harmful effects of arecanut use than those who studies at selected private school. Public school goers are more aware on most of the health effects caused by arecanut consumption such as teeth discoloration, gum disease, tooth decay, asthma worsening, cancer, kidney disease and heart disease. School goers who started arecanut use before 15 years of age are more at private school, more students at private school said arecanut was introduced to them by their own family member and frequency of daily use, overall daily consumption and duration of arecanut use is higher among private school children rather than public school children. Supari was more consumed by students of private school considering Rasily supari as their favorite brand. School goers who consume arecanut at school ground is more in public school. More students at private school swallow arecanut or its liquid while consuming it. Conclusion: Secondary school goers of public school in Male’ city have better knowledge regarding harmful effects of arecanut use such as teeth discoloration, gum disease, tooth decay, asthma worsening, cancer, heart and kidney disease. There is a difference in practices related to arecanut consumption between public and private school children.

Connecting the Unconnected: Student Experiences and the Effectiveness of Distance Learning in the New Normal

Authors- Assistant Professor Lister M. Cabonilas

Abstract-Abstract: This qualitative-phenomenological study explored the lived experiences and perceptions of fifteen (15) education students regarding distance learning during the new normal. Interviews revealed both challenges and coping mechanisms encountered by students. Challenges included unstable internet connectivity, communication delays, difficulty understanding materials, lack of resources, personal struggles (laziness, time management, finances), and social media distractions. Distance learning was perceived to potentially lead to lower academic performance, inconsistent grades, and reliance on instructor strategies. However, some students reported increased confidence with distance learning, despite inconsistency in class participation due to connectivity issues. Students perceived face-to-face participation as more effective. The study identifies a need for improvements in distance learning practices. The findings offer recommendations for policymakers, instructors and professors, and students themselves. Policymakers should focus on improving internet infrastructure and providing digital resources. Instructors and professors are encouraged to consider student concerns, provide ample learning materials, and utilize appropriate teaching methods to motivate students. Finally, students are called upon to be resourceful and utilize available resources to succeed in the distance learning environment.

A Novel Survey on the Unique Benefits of SDN and Big Data in Embedded System Applications

Authors- Jude K. Agujiobi, Stanley C. Odigbo, Nneka Maureen Nwogah, Gerald Agujiobi

Abstract-Abstract: Big data has permeated various spheres, becoming ubiquitous across academia, business, and media. However, the lack of a universally accepted definition has led to conflicting interpretations, hindering effective communication among stakeholders grappling with vast data. This study aims to combine several definitions of big data into a coherent and understandable whole. It alludes to large datasets with diverse structures, sizes, and levels of complexity that pose difficulties for analysis, storage, and display. Big data analysis involves mining these datasets for hidden patterns and correlations, offering organizations a deeper and broader understanding to gain a competitive edge. The transformative potential of software-defined networking (SDN) and big data, which have attracted much interest from the academic and business communities, is undeniable. Their convergence holds promise for simplifying data acquisition, transmission, storage, and processing, opening a new era of possibilities. However, it is essential to note that big data also presents significant challenges, such as scheduling, data supply, optimization, processing in cloud data centers, and scientific designs, which are examined in this article. It draws attention to how well SDN can manage these difficulties. Furthermore, the paper examines the latest applications of Internet of Things (IoT)-based embedded systems, such as home automation and modern vehicle management, in the context of SDN-enabled IoT and embedded systems.

Survey on Machine Learning’s Multi-Model Learning

Authors- Jude K. Agujiobi, Stanley Odigbo, Uthman A. Salman, Nneka Maureen Nwogah, Gerald Agujiobi

Abstract-Abstract: Big data has permeated various spheres, becoming ubiquitous across academia, business, and media. However, the lack of a universally accepted definition has led to conflicting interpretations, hindering effective communication among stakeholders grappling with vast data. This study aims to combine several definitions of big data into a coherent and understandable whole. It alludes to large datasets with diverse structures, sizes, and levels of complexity that pose difficulties for analysis, storage, and display. Big data analysis involves mining these datasets for hidden patterns and correlations, offering organizations a deeper and broader understanding to gain a competitive edge. The transformative potential of software-defined networking (SDN) and big data, which have attracted much interest from the academic and business communities, is revolutionary. Their convergence holds promise for simplifying data acquisition, transmission, storage, and processing, opening a new era of possibilities. However, it is essential to note that big data also presents significant challenges, such as scheduling, data supply, optimization, processing in cloud data centers, and scientific designs, which are examined in this article. It draws attention to how well SDN can manage these difficulties. Furthermore, the paper examines the latest applications of Internet of Things (IoT)-based embedded systems, such as home automation and modern vehicle management, in the context of SDN-enabled IoT and embedded systems.

Feasibility and Efficacy of the Regenerative AI Framework

Authors- Jude K. Agujiobi, Olalekan Ola Adaramola, Uthman A. Salman, Sarah Sejoro, M.S., Abel E. Agujiobi

Abstract-Abstract: The advent of Artificial Intelligence AI, over the years from the initial rule-based system to the present machine learning stages has contributed to the development of technology, and while there are some drawbacks in their exponential expansion, so are several positives. This necessitates the assessment of the strategy as the world moves more to the development and use of AI. Hence, the introduction of regenerative AI, as an alternative in the present system to entirely make good use of the AI potential and at the same time encourage ethical growth, and environmental restoration at the same time allowing beneficial social effects. This work investigates the need to balance the use of AI’s potential in moving innovation and advancement and find a way to mitigate the potential negative consequences on society and the environment in general, including the computational demands of sophisticated AI models together with the resource-intensive training process that contributed to a substantial carbon footprint and the ethical issue that constitute another facet of the problems being faced. The research employs a multifaceted methodology to develop and evaluate the Regenerative AI framework. The Regenerative AI model integrates ethical principles through fairness-aware algorithms, transparency mechanisms, and accountability frameworks. Environmental sustainability is addressed by optimizing algorithms for energy efficiency and exploring renewable energy sources for computations. The results of the study demonstrate the feasibility and efficacy of the Regenerative AI framework. Energy consumption in AI computations is significantly reduced, contributing to a more sustainable AI ecosystem.

Beyond the Game: Exploring the Impact and Significance of Esports in Contemporary Society

Authors- Mukil Aadhithian M S, Santhosh Reddy Sunkara

Abstract-Abstract: The emergence of esports has changed the gaming business, attracting millions of gamers and spectators worldwide. However, the impact of esports goes well beyond the virtual arena, impacting many elements of modern culture. This research investigates the larger ramifications of esports, including its influence on culture, the economy, education, and social connections. Our findings show that esports has evolved into a cultural phenomenon, instilling a feeling of community and identity in both players and viewers. The industry’s quick expansion has also created a lucrative market, with esports tournaments and leagues earning significant income and opening up new job possibilities. Esports has also been included into school courses, encouraging skills like teamwork, communication, and problem-solving. The research also delves into the social dynamics of esports, such as the blurring of real and virtual worlds and the introduction of new kinds of social contact and connection. Our findings underline the need of recognizing esports as a fundamental part of modern culture, with far- reaching implications for how we view leisure, entertainment, and social involvement. By exploring esports’ multidimensional influence, this study helps to a better understanding of the intricate interplay between technology, culture, and society, as well as the potential benefits and problems of this fast expanding area.

 

Adsorption Technologies for the Removal of Heavy Metal Ions in Potable Water

Authors- Riya Sharma, Associate Professor Jeevan Jyoti Mohindru

Abstract-Abstract: Scarcity of Potable Water has become major threat in today’s world. Collection of heavy metal a few of them, is potentially toxic and these get distributed to different areas through different pathways with increase in the earth population, development and industrialization increase rapidly and these are the major source of water contamination with heavy metals in lakes, rivers, groundwater and various water sources water gets polluted by the increased concentration of heavy metals and metalloids through release from the suddenly mine tailing, disposal of high metal waste, usage of fertilizers inland, leaded gasoline and paints, E waste, sewage, sludge, pesticides etc. Increase of heavy metal ions in water linked to so many harmful diseases. There are even chances of death in case of huge amount of exposure to such heavy metals which may cross the permissible limit in Potable water.

Generalitation of the function N in Computational Analysis

Authors- Rosanna Festa

Abstract-Abstract: The parallel research is contemporary to analyse processes and localisations in artificial intelligence (AI) associated with connexionism and learning algorithms.In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for Boolean functions of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. With a pattern N we use the calculator in synthesis applying polynomial advanced systems.

Intracranial Pressure Monitoring System Using Ultrasonic Sensors

Authors- Assistant Professor Chaitrashree R, Adithi C, Aishwarya C, Chitra G, Subramanya P

Abstract-Abstract: Intracranial pressure (ICP) is a critical physiological parameter that reflects the pressure exerted within the skull and surrounding brain tissue. Monitoring ICP is essential in various clinical scenarios, including traumatic brain injury, hydrocephalus, intracranial hemorrhage, and other neurological conditions. Elevated ICP can lead to severe complications, including cerebral herniation and neurological deterioration, necessitating prompt diagnosis and intervention. Traditional methods for measuring ICP involve invasive procedures, such as inserting intracranial probes or performing lumbar punctures, which carry inherent risks and limitations. Non-invasive techniques offer a safer and more patient-friendly approach to ICP monitoring, reducing the need for invasive interventions and improving patient comfort and compliance. This document provides an overview of intracranial pressure, including its physiological significance, clinical relevance, measurement techniques, and monitoring considerations. It explores both invasive and non-invasive methods for assessing ICP, highlighting the advantages and limitations of each approach. Additionally, it discusses emerging technologies and innovations in non-invasive ICP monitoring, emphasizing the importance of accurate and reliable ICP measurement in clinical practice. By understanding the principles and applications of intracranial pressure monitoring, healthcare professionals can make informed decisions regarding patient care, optimize treatment strategies, and improve outcomes for individuals with neurological disorders. This document serves as a comprehensive resource for clinicians, researchers, and healthcare stakeholders involved in the management of intracranial pathology and critical care patients.

Design and Execution of an Online E-Commerce Website for Online Shopping

Authors- M.Tech.Scholar Vishal Agarwal, Dr. Vishal Shrivastava, Assistant Professor Dr. Akhil Pandey

Abstract-Abstract: It’s critical to be able to react to customer requests in a timely and efficient way in the ever evolving corporate world of today. If your clients want to view your company on the internet and have immediate access to your goods or services. Online shopping is a type of lifestyle e-commerce where different fashion and lifestyle products (men’s wear currently) are sold. With the help of PayPal’s Instant Pay payment processor, registered users can instantly purchase desired products through this initiative. Alternatively, they can place an order using the Cash on Delivery (Pay Later)option. Administrators and managers now have simple access to view orders placed with Pay Later and Instant Pay options thanks to this initiative.

DOI: /10.61463/ijset.vol.12.issue2.172

Efficient Implementation and Performance Enhancement of Gabor Filters for Image Analysis and Recognition

Authors- Aladesote Olomi Isaiah, Johnson Tunde Fakoya, Micheal Olalekan Ajinaja

Abstract-Abstract: Efficiently implementing and enhancing the performance of Gabor filters is crucial for image analysis and recognition tasks. Gabor filters, renowned for their ability to capture spatial and frequency information effectively, play a pivotal role in various applications. However, challenges such as computational complexity and parameter optimization hinder their practical applicability. This paper proposes novel methodologies to address these challenges and enhance the efficiency and effectiveness of Gabor filters. Algorithmic optimization techniques, including fast Fourier transform-based methods and parallel processing strategies, are explored to improve computational efficiency. Automatic parameter selection and tuning methodologies are investigated to adapt Gabor filters to different image analysis tasks. Moreover, the integration of Gabor filters with multi-scale and multi-orientation analysis techniques, such as wavelet transforms, is examined to enhance their effectiveness in capturing complex image features. Deep learning integration approaches, specifically convolutional neural networks, are explored to leverage the complementary strengths of both techniques for improved image analysis and recognition. To validate the proposed methodologies, experimental evaluations are conducted using simple mathematical examples. The results demonstrated significant improvements in the efficiency and quality of topics extracted. The proposed multi-level model outperforms traditional approaches, providing better quality of topics extracted for image analysis and recognition tasks.

Enhancement of Micro-Strip Performance with Improvement of Antenna Gain and Feeding Technique

Authors- Pawan Kumar Nishad, Ashish Suryavanshi

Abstract-Abstract: Abstract In general, antenna is designed for transmit or receive electromagnetic waves. Among different kinds of antenna Microstrip patch antenna is most widely used antenna because of its low profile, easy fabrication and inexpensive. The microstrip patch antenna has another advantage that it can be designed for any shape. There are four different shapes are taken for this analysis. But the major problem with these antennas is narrow bandwidth. In this paper microstrip patch antenna is designed for four different shapes and substrates. The substrate materials are taken according to the dielectric constant values. And the antenna parameters such as gain, directivity, bandwidth and return loss are variable with different shapes and substrates. Then the antenna parameters are noted MATLAB software.

AI Assisted Tele-Medicine KIOSK

Authors- Dr Nandini S, Anusha k, Chethana D P, Ganasudha G K, Lavanya

Abstract-Abstract: The growing prevalence of chronic diseases has resulted in a heightened need for primary healthcare services in numerous industrialized nations. Modern healthcare technology tools have the potential to address the shortage of primary care providers. In this study we discuss the creation and implementation of an automated healthcare kiosk in a primary care setting for managing patients with stable chronic conditions. One hundred individuals with stable chronic illnesses were enlisted from a primary care clinic and utilized the kiosk instead of traditional doctor appointments for two consecutive follow-up visits. Both patients and physicians expressed contentment with the usage of the kiosk with positive feedback in all areas. However the independent utilization of the kiosk was dependent on the patient’s proficiency in language and level of education. The use of healthcare kiosks presents an alternative approach to managing stable chronic diseases potentially replacing the need for physician consultations and enhancing access to primary healthcare. Patients including those with limited literacy and education welcomed the use of these innovative healthcare technology tools. Prior to implementing kiosk-based technology in healthcare settings certain factors such as environment and patient needs should be optimized. Despite recent advancements in healthcare providing adequate medical services in rural India remains a persistent challenge that requires innovative solutions. To bridge this gap an AI-assisted telemedicine robotic kiosk can be set up in any village providing easy access to expert doctors based on the individual’s medical condition. Users can authenticate their identity through a biometric scanner followed by a consultation with a robot that inquires about their illness. The individual is then directed to an expert doctor via an e-sanjeevani app. Following the consultation necessary medication and services can be provided through a local Asha worker without any delay.

Heart Diseases Prediction Using Logistic Regression

Authors- Dr Abha Choubey, Shankar Sharan Tripathi, Anurag Sahu,Harsh Tiwari, Uzair Ahmed, P Priyansh

Abstract-Abstract: This research paper presents the development and implementation of a heart disease detection system leveraging logistic regression, aimed at augmenting diagnostic accuracy and efficiency in healthcare. The proposed system integrates patient data analysis, feature selection techniques, and logistic regression algorithms to deliver precise predictions tailored to individual health profiles. Through meticulous model refinement and validation, the system’s capabilities have been honed to accurately identify potential heart disease cases based on key indicators such as blood pressure, cholesterol levels, and other pertinent factors. The findings of this research contribute to the ongoing discourse on the integration of machine learning into healthcare practices, offering insights into the design, implementation, and evaluation of predictive models for enhanced disease detection and patient care.

Demographic Variations in Breast Cancer

Authors- Wasi Akbar Rizvi

Abstract-Abstract:Demographic variations in breast cancer refer to the disparities and differences in the occurrence, characteristics, and outcomes of breast cancer across various demographic factors. These factors include age, gender, race, ethnicity, socioeconomic status, and geographic location. Research indicates that breast cancer incidence and mortality rates vary among different demographic groups, highlighting the importance of understanding and addressing these variations. Factors such as genetic predisposition, access to healthcare, lifestyle choices, and environmental influences contribute to the complex interplay of demographics in breast cancer. Efforts to reduce disparities often involve targeted awareness campaigns, improved healthcare access, and tailored interventions to address the unique challenges faced by different demographic populations. Breast cancer poses a significant health challenge in India, characterized by both a high prevalence of triple-negative breast cancer (TNBC) and a low overall survival (OS) rate. The current study aimed to assess the diagnostic trends and prevalence of breast cancer among Indian women treated at major private oncology superspecialty hospitals. Methods. A retrospective study was conducted from January 2010 to December 2016 on 5,688 female patients with primary breast cancer in four hospitals in India. The data were analyzed for breast cancer epidemiology in terms of demographics, ER, PR, and HER2/neu data. TNM staging was used to calculate the survival data, which were available for 2,376 patients. Results. TNBC was observed in 21.6% (629/2913) of the patients. Overall, the study found favorable OS and recurrence-free survival outcomes and were diagnosed at an early stage compared to the non-TNBC group. Conclusions. The study highlights the critical need for comprehensive strategies to enhance the prognosis of Indian women with breast cancer.

DOI: /10.61463/ijset.vol.12.issue2.173

Sign Language Recognition

Authors- N.Sushmita, Ritesh Kumar, Shruti Mishra, Shweta C, Professor Aakanksha S Choubey

Abstract-Abstract: Sign Language Recognition (SLR) is a significant area of research aimed at bridging communication gaps between native sign language users and non-native speakers. This paper presents a study on the development and implementation of a novel system using advanced machine learning techniques. Our proposed system utilizes deep learning to interpret hand gestures accurately and convert them into text or speech in real-time. We explore various aspects of SLR, including gesture recognition, hand shape recognition, gesture measurement, and translation, to enable efficient and reliable translation across various datasets. Experimental results demonstrate the effectiveness and efficiency of our system in recognizing a variety of gestures with high accuracy and efficiency. Furthermore, we discuss the benefits of our research in providing interactive communication solutions for people who are deaf or hard of hearing, as well as the potential for integrating technology into communication devices. This research contributes to sign language awareness and underscores the importance of technology in promoting accessibility and community inclusion.

Survey on an Intelligent Grid Integrated Solar PV Array

Authors- Research Scholar Manish Kumar, Assistant Professor Abhijeet Patil

Abstract-Abstract: Power gadgets have been generally utilized in different applications since it was born. The single phase inverter, which changes over DC voltage/current into single phase AC voltage/current, is one of its generally important and prevalent converters. It has been broadly utilized in Uninterruptible Power Supplies (UPS), utilized in AC motor control, grid associated PV system, and so forth. Converters are electrical gadgets that convert current. Converters convert the voltage of an electric gadget, typically Alternating Current (AC) to Direct Current (DC). In this paper we discuss the papers related to converter and study how we can improve performance of system.

E-Commerce Website Using Shopify

Authors- Assistant Professor Dr Priyanka Dubey

Abstract-Abstract: E-commerce websites have become an essential part of the modern business landscape, providing a convenient and accessible platform for businesses to sell their products and services to customers around the world. One of the most popular platforms for building and managing an e-commerce website is Shopify, which allows businesses to easily set up and run their online store. The core technology behind Shopify is a combination of HTML, Liquid, and CSS. HTML (HyperText Markup Language) is a standard markup language used to create web pages. It provides the structure and content of a web page, including headings, paragraphs, and links. Liquid is a template language developed by Shopify that is used to build and customize the look and feel of an e-commerce website. It allows merchants to easily create dynamic and interactive pages by inserting Liquid tags and variables into their HTML code. CSS (Cascading Style Sheets) is a style sheet language used to control the appearance and layout of web pages. It allows merchants to customize the look and feel of their e-commerce website, including the font, color, and layout of elements such as headings, paragraphs, and buttons. Together, HTML, Liquid, and CSS form the foundation of an e-commerce website using Shopify. They provide the structure, content, and style needed to create a professional and user-friendly online store.

Self Driving Car

Authors- Assistant Professor Dr Priyanka Dubey

Abstract-Abstract: AI in driving is introduced as a self-driving in automobile industries. This research paper is p resents a scient metric and bibliometric analysis on self-driving cars. A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of senso rs, cameras, radar and artificial intelligence (AI) to travel between destinations without a hum an operator. To qualify as fully autonomous, a vehicle must be able to navigate without huma n intervention to a predetermined destination over roads that have not been adapted for its use. Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, G eneral Motors, Tesla, Volkswagen and Volvo, Tata Motors and Mahindra. Google\’s test invol ved a fleet of self-driving cars — including Toyota Prii and an Audi TT — navigating over 140,000 miles of California streets and highways. Through an examination of quantitative empiri cal evidence, we explore the importance of Artificial Intelligence (AI) as machine learning, de ep learning and data mining on self-driving car research and development as measured by pat ents and papers. Alongside the exponential growth in the rate of inventive activities and schol arly efforts, we find evidence for a rapid and meaningful shift in the application of the technol ogies related to data gathering and processing for the purpose of self-driving cars after 2009. We show that this shift mirrors major changes in the landscape of innovators as well as increa sing scholarly attention to the ethical, legal and social aspects of self-driving cars.

Structural Behaviour Analysis of TRT Outfall Slope- A Case Study of Tehri Pumped Storage Plant (1000 MW) Project

Authors- Rajeev Prasad, Kshitij Mudgal

Abstract-Abstract: The hilly terrain of lesser Himalayas is among the most vulnerable region of India due to natural hazards. Different kinds of landslides occurs due to tectonically and geo-dynamically active region of Himalayas. Here in the case of TRT out all slope of Tehri Pumped Storage Plant faces several adverse conditions due to existence of Sheared Phyllite (SP) and Thinly bedded Phyllitic Quartzite (PQT). To maintain the cut slope with proper support detail analysis was carried out. Geotechnical investigations were carried out to know the behaviour of rock mass. Based on Geotechnical parameters obtained from the analysis support system was finalised. Geological sections at different direction prepared to show the orientation of different sets of joints with respect to the slope face. Finally, the Factor of safety (FOS) of each rock slopes were determined. Monitoring of each bench with detail analysis of each bench during excavation was carried out along with behaviour of rock mass which is briefly discussed in this article.

Enabling Data Security Using Nature Inspired Algorithms – An Analytical Study

Authors- Research Scholar N V A Pavan Kumar Inguva, Professor Oludotun Oni,
Professor Michael Powell

Abstract-Abstract: – Data confidentiality, integrity, and availability are essential for a variety of reasons. In order to ensure the security of the data, it must be strongly protected. While bio-inspired algorithms for data security have provided elegant answers to tough challenges, there is still a significant amount of research needed in this field to achieve major progress. We in this paper made a study of various such algorithms and analysed how they are applied in providing security. Also few insights are provided on how the tailor made algorithms could be used.

DOI: /10.61463/ijset.vol.12.issue2.222

Integrating Soft Skills: A Holistic Approach to Cultural Values and Ethos

Authors- Assistant Professor Madhvi Kaushik, Assistant Professor Garima Singh

Abstract-Abstract: – Soft skills are essential for professional success and contribute significantly to thriving in the workplace, serving as a key factor for survival in any career. Additionally, the teachings of Spiritual Vedanta can guide individuals in becoming more effective employees or entrepreneurs. Since people spend a large portion of their lives at work, it is crucial to utilize this time for both material and spiritual growth. Work itself can be transformed into a spiritual practice, where negative energies such as conflict can be converted into positive sources of inspiration. Emotions like anger, fear, worry, and stress can be redirected towards creativity, ambition, forgiveness, and joy. Even feelings of stagnation can shift into accomplishments, turning boredom, discouragement, failure, and depression into qualities like generosity, contentment, calmness, and relaxation. This paper will explore key Vedanta principles alongside essential soft skills, presenting them within a conceptual and empirical framework. It will discuss how Vedantic ethical perspectives can be integrated with soft skills to foster personal growth and promote the development of organizations.

DOI: /10.61463/ijset.vol.12.issue2.682

Reimagining the Financial Landscape of Higher Education: Opportunities and Challenges of Privatization

Authors- Assistant Professor Dr Mohd. Danish, Mohd Tasleem

Abstract-Abstract: – The financial landscape of higher education is undergoing significant transformation, with privatization emerging as a pivotal force shaping its future. This paper explores the opportunities and challenges associated with privatization in the higher education sector. Privatization introduces potential benefits, including increased revenue generation, enhanced institutional autonomy, and the fostering of innovation through public-private partnerships. However, it also presents significant challenges, such as exacerbating socioeconomic inequalities, compromising affordability, and raising questions about quality assurance and accountability. By critically analyzing both the opportunities and challenges, this study aims to provide a comprehensive understanding of privatization’s impact on the accessibility, quality, and sustainability of higher education. The findings underscore the need for balanced policies that leverage privatization’s benefits while safeguarding the sector’s public good mission.

DOI: /10.61463/ijset.vol.12.issue2.683

Tensile Test Analysis of Kevlar Based Epoxy Composites in Finite Element Analysis Method

Authors- P.Muthukumar, R.Dharmaraj, S.Jagadeeswaran, C.Gokulakannan

Abstract-Abstract: – This project focuses on analyzing the tensile behavior of a composite material comprising pure Kevlar 49 as the reinforcement and epoxy resin as the matrix, utilizing Finite Element Analysis. Under the tensile load conditions the objective is to simulate and evaluate mechanical properties, stress distribution, and failure modes. Material properties and parameters for both epoxy resin and Kevlar 49 are gathered to develop a 3D finite element model using Ansys 19.2 software. The simulation analyzes stress distribution, strain, deformation, and displacement, emphasizing identification of failure modes and critical regions. Validation against experimental theoretical data and optimization of composite design or material composition for enhanced performance and durability are pursued. This investigation aims to offer valuable insights for the design and optimization of composite materials across engineering applications.

DOI: /10.61463/ijset.vol.12.issue2.684

Low-Cost Indigenous Paddy Collector and Spreading Machine for the Benefit of the Farmer

Authors- N.Kawin, S.L.Harish, A.B.Hirthik Roshan, D.Dharmarajan

Abstract- The project aims to address the challenges faced by farmers in the manual sun-drying process of harvested grains by introducing a low-cost indigenous paddy collecting and spreading machine. Currently, the sun-drying process involves the labour-intensive efforts of 20-30 workers and is financially burdensome for farmers. Additionally, alternative methods utilizing fuel or expensive foreign machines present additional barriers to adoption due to cost and maintenance concerns. The proposed machine, constructed from recycled plastic and powered solely by mechanical energy, offers an affordable and sustainable solution to grain drying. By automating the collecting and spreading of paddy, the machine streamlines the drying process, reducing labour requirements and operational costs for farmers. Moreover, its indigenous design ensures that maintenance and replacement of parts are feasible and cost-effective. Through the development and implementation of this innovative machine, farmers can enhance the efficiency of their grain-drying operations while reducing dependency on manual labour and costly equipment. The project underscores the potential of indigenous technology to empower farmers and improve agricultural practices, ultimately contributing to sustainable rural development and food security.

DOI: /10.61463/ijset.vol.12.issue2.685

Assessing the Mechanical Properties of Banana Glass Fiber Reinforced Epoxy Hybrid Composites

Ra. Aravind, V. Nagamanikam, J. Jasim Ahamrd, C. Kirubakaran

Abstract- – The increasing demand for environmentally friendly materials and the desire to reduce the cost of traditional fiber lead to the development of natural fiber composites. Natural fibers presented in the composite have some important advantages such as low density, appropriate stiffness, mechanical properties and renewability. In the present work deal with fabrication and investigation of mechanical properties of banana fiber, glass fiber and reinforced with epoxy resin as natural hybrid composite, they are recyclable and biodegradable. The Composites of different combinations with varied fiber content were prepared using hand lay-up technique using epoxy resin and hardener as reinforcing materials. Banana fiber with 30, 25 and 20% were hybridized with 10, 15 and 20% of E-glass fiber to form composites and compared with normal Banana fiber and epoxy resin composites. The results thus obtained signified mechanical properties got improved in Banana -glass hybrid composite with increased glass fiber content from 10%-20%, thus acting as a positive reinforcement in providing extra strength and smooth surface finish to the composite and at the same time the Banana fiber imparted elasticity to the composite.

DOI: /10.61463/ijset.vol.12.issue2.686

Emerging Trends in Self-Healing Protective Coatings: From Microcapsules to Nano-Enhanced Systems

S.Sivaganesan

Abstract- – The emergence of self-healing protective coatings represents a transformative advancement in material science, offering autonomous damage repair and prolonged durability in harsh environments. Inspired by natural healing mechanisms, these coatings mitigate structural degradation by restoring functionality after mechanical, chemical, or environmental stressors. This paper provides a comprehensive review of self-healing mechanisms in protective coatings, focusing on microcapsule-based, vascular-network-based, and intrinsic self-healing strategies. Additionally, recent innovations in nanomaterial-enhanced coatings, stimuli-responsive systems, and hybrid healing approaches are explored. Despite significant progress, challenges related to scalability, long-term stability, and industrial integration hinder widespread adoption. Future research directions emphasize cost-effective fabrication methods, eco-friendly healing agents, and multi-functional coatings tailored for aerospace, marine, and automotive applications. The findings of this study highlight the potential of self-healing coatings to revolutionize corrosion protection, paving the way for sustainable and intelligent surface engineering solutions.

Fuzzy Logic Controller based Non-Isolated DC Converter for Renewable Energy Applications

S.Ragul, B.Balaji, R.Jeeva, R.Sedhupathi

Abstract- – The Photovoltaic (PV) energy generation, utilizing parallel arrangements of small voltage-generating solar cells, stands as a prominent source for large-scale electric power generation. In renewable energy applications, non-isolated DC-DC converters play a pivotal role by facilitating voltage level adjustments within solar energy systems, crucial for efficient charging. Incorporating fuzzy logic concepts into the control mechanisms of these converters enables adaptive and intelligent voltage regulation in dynamic environmental conditions. Specifically, fuzzy logic allows for the dynamic adjustment of voltage levels to align with the requirements of charging processes. In the realm of renewable energy, DC fast chargers are in dispensable for delivering high power levels, thus significantly reducing charging times. Consequently, selecting a converter with high efficiency becomes paramount for optimizing the charging process.

DOI: /10.61463/ijset.vol.12.issue2.689

Eco Friendly Paint Made From Agricultural Waste

S.Kavipriya, Logeshr, Uthayakumaranm, Yogeshwaranv

Abstract- – At present days the pollution is becoming a major concern. The building materials used in the construction site cause a significant amount of pollution globally. Paints make the house and furniture look attractive, new and even keep safe from different insects. Regular paints in the house keep away the boredom and give freshness to the surrounding. But with giving good vibes it can even become a serious problem for the people living in the house. The effects of paints on the environment and health may last forever if we did not make choices wisely. To reduce the pollution, we need to promote the use of ecofriendly materials. Eco- friendly organic paints are made of either by natural minerals or the wastes available from nature like agricultural waste and water based. They have the ability to control pollution, killing bacteria and fungi. The conventional chemical paints are a major contributor of Total Volatile Organic Compounds, Formaldehyde. The toxic paints that contain chemicals like chemicals, oils, lead are harmful to children and critical to the environment. The pigments in the paints can be natural or manmade. The natural pigments can also be dangerous than manmade. This project has provided ways to make the environment clean and pollution free. The purpose of the study is to make consumers aware of toxic and harmful paints which have an effect on human health.

DOI: /10.61463/ijset.vol.12.issue2.690

Experimental Study on Concrete Using Waste from Ariyamangalam Dump Yard

A.Karthick, S. Kathiravan, V. Arunachalam, S. Selvam

Abstract- – The disposal of solid waste has become a pressing environmental issue, necessitating sustainable solutions to mitigate its adverse impacts. This study investigates the feasibility of incorporating solid waste from the Ariyamangalam dump into concrete as a sustainable alternative to traditional construction materials. The experiment aims to evaluate the mechanical properties, durability, and environmental impact of the resulting concrete mixtures.

DOI: /10.61463/ijset.vol.12.issue2.692

AR Image Recognition and Tracking App

Associate Professor Dr. M. Supriya, Nithin.A, Karthikeyan.S, Kishore Kumar.M, Nelson Solomon Fernandes.S

Abstract- – This project focuses on developing a method for implementing augmented reality (AR) image tracking in a 3D environment using Unity, Vuforia, and Blender. By integrating these technologies, we enable immersive AR experiences where virtual objects interact with real-world images. The approach begins with selecting an image as the target for AR tracking, serving as the anchor for virtual content placement. Unity provides the platform for creating and managing the AR scene, while Vuforia handles image recognition and tracking. Blender is used to design and animate 3D models, which are then imported into Unity and anchored to the tracked image. Unity’s scripting capabilities allow for interactivity and dynamic behavior of virtual objects, ensuring a seamless AR experience. This method enables users to view and interact with 3D objects overlaid onto real-world images in real-time. The proposed technology has broad applications in education, gaming, marketing, and simulation, offering innovative ways to enhance user engagement and interaction.

DOI: /10.61463/ijset.vol.12.issue2.693

Real-Time Surveillance For Road Safety: Object Detection and Compliance Monitoring

Assistant Professor Dr. Pon Partheeban, Aslin R, Harish Suresh Kumar, Roshan Lal J, Vijesh G

Abstract- – Road safety is a critical concern worldwide, with two-wheeler violations posing significant risks to riders and other road users. This project presents a comprehensive real- time surveillance system for monitoring and detecting road safety violations by two-wheelers using state-of-the-art computer vision techniques. The proposed approach leverages the powerful YOLOv8 object detection architecture to accurately identify and localize two-wheelers, riders, helmets, mobile devices, and license plates in video streams. The system employs a multi- task learning strategy, where a single deep neural network is trained to simultaneously detect and classify multiple objects of interest. Post-processing algorithms analyze the spatial and contextual relationships between detected objects to identify specific violations, such as lack of helmet usage, triple riding, and distracted driving due to mobile phone usage. To ensure real-world applicability, the system is designed for seamless integration with existing traffic monitoring infrastructure, enabling real-time violation detection and automated reporting to relevant authorities. Extensive experiments on diverse datasets demonstrate the system’s robustness and efficiency, achieving state-of-the-art performance in detecting road safety violations by two-wheelers. The proposed solution offers a scalable and cost- effective approach to improving road safety, with the potential to significantly reduce accidents and casualties involving two- wheelers. This project contributes to the field of computer vision and intelligent transportation systems by presenting a novel application of deep learning object detection for enhancing road safety compliance monitoring.

DOI: /10.61463/ijset.vol.12.issue2.694

Pulmonary Artery Blockage Detection

Assistant Professor Senthil Kumar, Aswini. P, Durshika.K.J, Vinusha.V.S

Abstract- – This study investigates the application of Machine Learning (ML) techniques for automated detection of pulmonary artery blockage using medical imaging data. Pulmonary arterial obstruction represents a potentially life-threatening condition requiring rapid and accurate diagnosis for optimal clinical outcomes. We present a novel computational approach that leverages advanced ML algorithms to analyze and interpret pulmonary angiography images. Our methodology incorporates comprehensive image preprocessing, segmentation, and feature extraction techniques to prepare data for classification. Multiple ML architectures were implemented and comparatively evaluated, including Convolutional Neural Networks (CNNs) and Sup- port Vector Machines (SVMs). Performance assessment utilized standard metrics including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Results demonstrate that our ML-based approach achieves superior diagnostic accuracy compared to conventional methods, with potential applications as a clinical decision support tool. This research contributes to the evolving landscape of computer-aided diagnosis in pulmonary vascular pathology and offers promising avenues for improving early detection and treatment planning for patients with suspected pulmonary artery blockage.

DOI: /10.61463/ijset.vol.12.issue2.695