Volume 11 Issue 6

Comparative Analysis of Fuel Subsidy Removal and the Diversification Policies for Agricultural Development in Nigeria

Authors- Rimamsitse Nuhu

Abstract- The ripple effects of the petrol crisis on the Nigerian economy is multi-faceted: price distortions, volatilities, dutch disease, corruption, and inefficiencies. This study takes a comparative analysis of fuel subsidy removal and the diversification policies for agricultural development in Nigerian. The study made use of secondary data obtained from Central Bank of Nigeria Statistical Bulletins, Petroleum Product Price Regulatory Agency (PPPRA), National Bureau of Statistics, Benue State Agricultural and Rural Development Authority (BNARDA), and FAO. Johansen co-integration model and t-test were the analytical tools used. After appropriate robustness checks and ensuring data stationarity, the study found that fuel subsidy removal had significant positive influence on the country’s GDP, significantly reduced inflation rate, and also reduced life expectancy of Nigerians. Specifically, a percentage increase in petrol price significantly increases GDP by 9.8%; a percentage increase in petrol price increases the prices of rice and maize by 0.75% and 1.50% respectively when the retracted percentage is reinjected into the economy through other sectors say Agricultural. The study concludes that increased petrol price had negative effects on GDP in the short run and adverse effects on the prices of crop produce, but the result seems not find any negative relationship between GDP and crop production. This may be as a result of the reinjection of the subsidy retracted percentage back into the economy thereby causing a balance up in other sectors. Government should diversify and develop other economies and provide adequate infrastructural facilities to cushion the effects of subsidy removal. Organic and low-input methods of farming should be adopted to reduce the need for fuel inputs to the food system at all levels.

Sponsored by: ABCD-Index RAMP

Line Follower Robot for Autonomous Warehousing Using Design Thinking Approach

Authors- Gandham Venkat Karthik , Shrivishal B, Harini P., Prof. Dr. V Murali Bhaskaran

Abstract- This paper explores the potential enhancements to modern warehouses through full automation. Specifically, it delves into the application of line follower robots designed for autonomous goods transportation and also to transport different goods into their respective storage compartment. These robots navigate complex warehouse layouts, loading and unloading of goods eliminating the need for manual intervention. By addressing challenges such as precise path tracking and integration with existing systems, they streamline material handling processes. The paper highlights the efficiency gains, reduced material handling times, and enhanced resource utilization achieved by incorporating these robots, ultimately revolutionizing warehouse management practices for greater efficiency and responsiveness in supply chain operations.

Module Current Control and Power Flow Control of Hexagonal Modular Multilevel Converter in Variable Frequency Power System

Authors- Md Roknuzzaman, Shinichi Hamasaki

Abstract- A transformer-less hexagonal modular multi-level converter (H-MMC) has been introduced for variable frequency regulation in the power generation side. A control algorithm based on the phase-locked loop (PLL) strategy is proposed in this paper to deal with variable frequency on the generation side. Regarding the control to keep balancing the capacitor voltages, the circulating current model is proposed in this paper to control the module current. This research proposes a power flow control strategy considering the current flow between the two sides of the H-MMC. In addition, a modified neutral offset voltage injection method is presented for suppressing the module capacitor voltage by balancing the branch energies.

DOI: /10.61463/ijset.vol.11.issue6.101

Littering Perceptions, and Behaviors among the General Public in Maseru, Lesotho

Authors- Ts’aletseng Siimane, Sekhoane Malaka , Sehoete Tsoahae

Abstract- Littering is a growing environmental problem that has caught the eyes of different sectors of society. This study, which was conducted in Maseru, Lesotho, aimed to understand littering perceptions and behaviours among the general public. A qualitative phenomenological study design was used where data was collected through semi-structured interviews from a sample of 57 respondents. Data was analysed through content analysis. Findings revealed that Maseru city is perceived as heavily littered, although littering was seen to have decreased over time. Furthermore, litterers are predominantly female, aged between 26-35 and most have secondary education. Material factors such as the availability of waste receptacles and the convenience of the receptacle location influence littering behaviour, while keeping a clean environment was identified as the main promoter of anti-littering behaviour. The study concluded that there are positive perceptions and negative behaviours in littering in Maseru. Recommendations for reducing littering included the supply of more litter bins and creating awareness through education.

Riverbed Classification and Evaluation of Woji Creek Portharcourt, Rivers State Nigeria Using Side Scan Sonar Technology

Authors- Igbokwe, J.I1. Ojo, P.E1., Oliha, A.O1. and Anyadiegwu, P.C2.

Abstract- The aim of this study is to classify and evaluate the riverbed in Woji Creek, Port Harcourt, Nigeria using side scan sonar technology. Its objectives are to; classify and evaluate the riverbed of Woji Creek using side-scan sonar technology, analyse and quantify the variations in water depth throughout Woji Creek, evaluate the water volume of Woji Creek for navigational suitability purposes and determine the turbidity levels of Woji Creek. The methodology involved the acquisition of Side-Scan Sonar (SSS) and Sub-bottom profile Data. The acquired data acquired underwent backscatter processing to obtain a geocoded back scatter image from which feature points were extracted and matched. The matched images were used to derive a riverbed classification, depth classification, water volume analysis and river turbidity analysis. The riverbed classification identified three predominant sediment types: Clayey Silty Sand, Silty Clay, and Silty Sand, each with distinct implications for navigation. Clayey Silty Sand, covering 43.11% of the riverbed, poses challenges due to its cohesive nature and sand content, potentially leading to increased frictional resistance and the formation of sandbars. Silty Clay (30.33%) influences sediment transport and water clarity, while Silty Sand (26.55%) is relatively mobile, potentially affecting channel migration. The depth classification analysis revealed the presence of Shallow, Moderate, Deep, and Very Deep areas within Woji Creek. Shallow areas (24.22% of the total area) may pose challenges for vessels with deeper drafts, while Moderate areas (30.54%) offer improved navigability. Deep areas (41.25%) provide favorable conditions for navigation, and Very Deep areas (3.98%) accommodate vessels with extreme drafts. The water volume distribution analysis is crucial for assessing depth limitations and planning routes. Shallow areas (11.15%) present smaller volumes, potentially posing navigational challenges. Moderate (25.65%) and deep (31.72%) areas offer larger volumes, facilitating navigation for vessels with moderate and deeper drafts, while Very Deep areas (31.49%) provide substantial volumes that require careful navigation. The assessment of river turbidity identified low (70.43%), moderate (23.67%), and high (5.91%) turbidity zones. Low turbidity indicates clear water with minimal suspended particles, while moderate and high turbidity suggest increased particle concentration and reduced visibility. It is recommended that the findings of this study be utilized as a decision support tool for navigation planning and management in Woji Creek. The comprehensive analysis of the riverbed classification, depth ranges, volume distribution, and turbidity provides valuable information that can inform strategic decision-making processes. It is recommended that the findings of this study be utilized as a decision support tool for navigation planning and management in Woji Creek.

Deep fake Detection using Deep Learning

Authors- Prof. Aparna Bagde, Sakshi Fand, Kanchan Varma, Aditya Gawali

Abstract- The rapid advancements in AI, machine learning, and deep learning technologies, which have given rise to new tools for manipulating multimedia. While these technologies have found legitimate applications in entertainment and education, they have also been exploited for malicious purposes. Notably, high-quality and realistic fake multimedia content, known as Deepfake, has been used to spread misinformation, incite political discord, and engage in malicious activities like harassment and blackmail. Deepfake algorithms possess the unsettling ability to craft counterfeit images and videos so convincingly that they elude human scrutiny. These algorithms adeptly fashion deceptive visual and auditory content, manipulating the appearances and behaviour of targeted individuals to such a degree that viewers instinctively place their trust in what is, in fact, a fabrication. Distinguishing these deepfakes from genuine content becomes a formidable challenge, as the human eye struggles to discern the difference. In response, this paper conducts a comprehensive exploration, delving into the array of tools and algorithms employed in the creation of deepfakes, while placing particular emphasis on the vital aspect of deepfake detection methods. Through in-depth discussions that encompass challenges, research endeavour, technological advancements, and strategic approaches linked to the realm of deepfakes, this survey scrutinizes the landscape. By tracing the evolution of deepfakes and appraising the current state of deepfake identification techniques, it offers a holistic evaluation of deepfake methodologies. This, in turn, contributes to the formulation of innovative and more resilient strategies, essential for countering the ever-growing sophistication of deepfake technology.

Relationship between Academic Anxiety, Emotional Intelligence and Wellbeing among Adolescents in Jammu District

Authors- Mr. Vishal Dogra, Dr. Ridhhi Agrawal

Abstract- Background: Adolescents are at the stage where alot of psychological transitional changes takes place in an individual’s life amongst which major ones are stress, trauma, depression and other related issues, but the severe one in according to them is Academic anxiety, by which their emotional intelligence and Wellbeing is also getting affected alot.
Objective: This study attempts to assess the impact of academic anxiety on emotional intelligence and wellbeing among adolescents studying in various schools (Akhnoor), Jammu District.
Method: The sample consisted of 100 students (50 males & 50 females), both from Rural as well Urban areas. Age range of the subjects was selected from (17-19) years and were selected through random sampling technique. For the assessment of Academic anxiety- Academic anxiety scale (AAS); developed by Sonal Sharma & Dr. Shakir; Emotional intelligence scale (EIS) ; developed by Sushma Talesara & Psychological Wellbeing scale (PWBS); developed by Dr. Anjum Ahmed.
Results: There is a very less significant difference had been detected amongst the male & female adolescents who are belonging toto Rural & Urban areas respectively having different types of reasons & factors underlying the situation prevailing towards psychological problems.

The Role of Finance in Sustainable Developments

Authors-Arpit Garg

Abstract- The World Bank and the Global Money related Asset should further develop Funding for Advancement (SDG) to accomplish the 17 Economical Improvement Objectives. The report on increasing the Supportable Improvement Objectives obviously underlined the significance of speeding up monetary advancement to accomplish the Reasonable Improvement Objectives. Despite the fact that obviously adequate assets are expected to accomplish the SDGs, very little scholarly examination has zeroed in on this subject. Whether creating elective energy sources or supporting organizations utilize moral and maintainable work rehearses, the monetary area immensely affects funding and consciousness of obligation. ” Great and supportable money” alludes to speculation choices that consider the natural, social and administration parts of the economy. The job of funding in accomplishing the objectives of supportable advancement is characterized in view of the ongoing needs of the field. As indicated by our examinations, the monetary area is fundamental to the accomplishment of reasonable objectives, for example, the battle against destitution, orientation uniformity, the quest for good work, financial development and environmental change. According to the study’s findings, the group of countries that were looked at achieved the SDGs more successfully the more sustainable the financial model was. We found a strong correlation between the SDGs for social, environmental, and economic sustainability (SDGs 1, 3, 4, 5, and 10) and the sustainable finance model. (SDG8, 9, 17).
The objectives of the research are examined:
• To systematically present the key gaps regarding the connection between sustainability and money.
• To investigate the relationship between sustainable development and financial inclusion.
• To ascertain whether financial management can promote the expansion and adoption of sustainable business practices.

Design and Analysis of an Algorithm for Slice Handover in 5G Networks

Authors- Kerime Haibatt,Asst. Prof. Arif Dolma

Abstract- – This research focuses on the mobility management technique of network slicing in 5G networks. Network slicing involves dividing the physical network infrastructure into multiple virtual networks called network slices, each optimized for specific applications or use cases. Network slice handover enables seamless transfer of a user’s connection between different network slices without interrupting the quality of service. The research aims to analyze and develop an algorithm for making network slice handover decisions in segmented 5G networks and evaluate its performance. The research proposes an algorithm for network slice handover delivery decisions based on an analytic model using Markov chain. The network model’s architecture and the implementation of the vertical delivery decision-making algorithm are described. The performance of the algorithm is evaluated using key performance indicators (KPIs) related to quality of service at the connection level, such as the likelihood of new calls being blocked, or connections being lost. Simulations are conducted to assess how changes in metrics, such as contact arrival rate, capability, new call threshold, base bandwidth unit, and call departure rate, impact the quality-of-service measurements. The simulation results show that the developed algorithm generally provides good quality of service levels, with a lower likelihood of dropping a distribution call compared to new calls being blocked in all cases. The research contributes to the understanding and improvement of network slice handover in 5G networks by proposing an algorithm and evaluating its performance through simulations. The results demonstrate the algorithm’s effectiveness in maintaining quality of service during network slice handover.

Experimental Analysis of Solar Panel Efficiency With Water Bottle Based Cooling System

Authors- Assistant Professor Thanuja K, Assistant Professor Rajath H G

Abstract- – The scripting and configuration of the Radio Access Network (RAN) are essential for integration and maximizing the functionality and performance of a telecommunication site. The effective use of radio resources is ensured by the right configuration of RAN parameters, leading to better coverage, higher throughput, and improved call quality. This paper presents a guide to RAN scripting to configure a Huawei Node B with the nomenclature “UABJ447”, Our main focus is on BSS parameters required for RAN configuration and scripting, taking into account the various configuration IPs and their functionalities. The command prompts employed demonstrate the use of Huawei iManager U2000 and M2000 for RAN configuration. The presented methodology provides RAN engineers with a practical approach to RAN scripting and configuration of Huawei Node B, resulting in enhanced network performance and user experience.

Radio Access Network Configuration Of A Huawei Node B (3g Site)

Authors- Barakur C.A, Oke O.A,

Abstract- – The scripting and configuration of the Radio Access Network (RAN) are essential for integration and maximizing the functionality and performance of a telecommunication site. The effective use of radio resources is ensured by the right configuration of RAN parameters, leading to better coverage, higher throughput, and improved call quality. This paper presents a guide to RAN scripting to configure a Huawei Node B with the nomenclature “UABJ447”, our main focus is on BSS parameters required for RAN configuration and scripting, taking into account the various configurations IPs and their functionalities. The command prompts employed demonstrate the use of Huawei manager U2000 and M2000 for RAN configuration. The presented methodology provides RAN engineers with a practical approach to RAN scripting and configuration of Huawei Node B, resulting in enhanced network performance and user experience.

Development of Self Cooling PV Cell by Phase Change Material

Authors- Assistant Professor Thanuja K , Assistant Professor Rajath H G

Abstract- -The rapidly growing use of photovoltaic systems depicts its importance in the field of power generation in the near future. Photovoltaic panel absorbs 80% of the incident solar radiation and converts 20% of this absorbed energy into electrical energy depends upon the efficiency of photovoltaic panel, remaining absorbed energy get converted into heat causes surface temperature rise of PV panel. As the temperature of solar panel increases its electrical performance deteriorates. For every degree rise in PV surface temperature efficiency decreases from 0.4 to 0.65%. So, cooling of photovoltaic pane is very essential to have better energy conversion efficiency. Many researchers have investigated the performance of PV panel integrated with phase change materials (PCMs) based cooling technique.

Media Evolution in Sangli And Kolhapur: A Comparative Analysis Of Traditional Vs. Online Newspapers Among The Top 5 Brands

Authors- Madhavi Manohar Peshave, Dr. Pranati Rohit Tilak

Abstract- -In the midst of rapid technological advancements, the media landscape is undergoing a transformative shift, especially in regions like Sangli and Kolhapur. These areas witness the coexistence of traditional and online newspapers, shaping the dynamics of information dissemination. This research delves into the intricate evolution of media in these districts, focusing on the top 5 newspaper brands: Pudhari, Punyanagari, Keshri, Sakal, and Punyanagari. Sangli and Kolhapur, situated in Maharashtra, represent microcosms of diverse communities. The media landscape here mirrors global trends but is infused with local nuances. The choice between traditional and online newspapers is not solely a technological preference but encapsulates socio-cultural elements shaping the identity of these districts.This research seeks a comprehensive understanding of media preferences in Sangli and Kolhapur by conducting a comparative analysis of content, readership, and impact between traditional and online newspapers. A survey involving 500 respondents (250 from each district) was conducted through a questionnaire. Utilizing a convenience sampling approach, the study assessed opinions and preferences regarding five prominent newspaper brands: Sakal, Pudhari, Punyanagari, Tarun Bharat, and Keshri. The research identified distinct preferences for the printed version across various newspapers, indicating a strong inclination towards traditional formats. Preferences varied across different newspapers, emphasizing the importance of considering individual brands when analyzing reader behaviors. Given the consistent preference for printed versions, newspaper brands should continue investing in and emphasizing the quality of their print editions. Recognizing the significant percentage preferring the web version, newspapers should focus on improving their digital presence, optimizing online platforms, and offering exclusive digital content. The research provides valuable insights into the nuanced preferences of newspaper readers in Sangli and Kolhapur. A multi-faceted approach balancing traditional strengths with strategic advancements in the digital realm is crucial for newspapers aiming to thrive in this dynamic media landscape.

Patterns Of Phulkari: Past And Present

Authors- Payal Ratan, Sukhvir Kaur

Abstract- – This paper examines the traditional phulkari art of Punjab as a design exemplar.Phulkari’s reflect the versatility, hard work and creativity of the rural women but it also represents the tradition and culture of Punjab. In contemporary Punjab, the authentic tradition of phulkari has started to fade out. Today, with the introduction of commercial phulkari, the traditional art is suffering manifold where the major focus lies in monetary return instead of conservation of traditional knowledge. Phulkari has been so densely interwoven in the lives of women that these two seem inseparable. In culture of punjab Learnt through the teachings and experience of the elders, a girl used to embroider her own world, dreams and aspirations onto canvas of khaddar. The designs and motifs were innumerable .With the change in this form of textile, women still embroider it for economic gains. Women folk paints the geometrical motifs of Phulkari using a needle and thread with an unlimited colour palette This study reveals the patterns of the old phulkari versus the new one.

Study of EKC Hypothesis and Long Run and Short Run Linkages between CO2 Emission, GDP, Coal Consumption, power Consumption and Renewable Energy in India

Authors-Dr. Neetu Narwal

Abstract- – In this paper we used most recent and robust econometric technique for estimation of cointegration to provide decisive proof on the linkage between CO2 emission, electric power consumption, electricity production from coal source, renewable energy consumption and economic growth in India from 1971-2014. Furthermore, the study also explored the Granger causality among the variables. The empirical result suggest that the variables are cointegrated and hence suggest the existence of long term relationship between the variables. Granger Causality test reveals strong evidence of bi-directional causality from CO2 emission and renewable energy consumption as well as unidirectional causality from GDP, electricity production from coal source, electric power consumption to carbon emission. The implication of the results is further discussed.

Deep fake Detection using Deep Learning

Authors- Prof. Aparna Bagde, Sakshi Fand, Kanchan Varma, Aditya Gawali

Abstract- -The rapid advancements in AI, machine learning, and deep learning technologies, which have given rise to new tools for manipulating multimedia. While these technologies have found legitimate applications in entertainment and education, they have also been exploited for malicious purposes. Notably, high-quality and realistic fake multimedia content, known as Deepfake, has been used to spread misinformation, incite political discord, and engage in malicious activities like harassment and blackmail.Deepfake algorithms possess the unsettling ability to craft counterfeit images and videos so convincingly that they elude human scrutiny. These algorithms adeptly fashion deceptive visual and auditory content, manipulating the appearances and behaviour of targeted individuals to such a degree that viewers instinctively place their trust in what is, in fact, a fabrication. Distinguishing these deepfakes from genuine content becomes a formidable challenge, as the human eye struggles to discern the difference. In response, this paper conducts a comprehensive exploration, delving into the array of tools and algorithms employed in the creation of deepfakes, while placing particular emphasis on the vital aspect of deepfake detection methods. Through in-depth discussions that encompass challenges, research endeavour, technological advancements, and strategic approaches linked to the realm of deepfakes, this survey scrutinizes the landscape. By tracing the evolution of deepfakes and appraising the current state of deepfake identification techniques, it offers a holistic evaluation of deepfake methodologies. This, in turn, contributes to the formulation of innovative and more resilient strategies, essential for countering the ever-growing sophistication of deepfake technology.

Strategic Site Selection for Large-Scale Petrochemical Industry in Southeastern Nigeria: A Gis-Based Multi-Criteria Analysis Approach

Authors- Iwuanyanwu, P.E., Igbokwe, J.I., Oliha, A.O.

Abstract- – This study embarks on a comprehensive exploration of the site suitability assessment for the large-scale petrochemical industry in Southeast Nigeria, employing a multi-faceted approach facilitated by Geographic Information Systems (GIS). The primary objectives of this research endeavor encompass a thorough review of planning concepts and existing planning guidelines relevant to the establishment of the petrochemical industry in the area. Furthermore, it aims to delineate the crucial factors and criteria imperative for the industry’s successful implementation.The datasets mobilized for this investigation span a spectrum of critical information sources, including satellite imagery depicting land use, Shuttle Radar Topography Mission (SRTM) data, climatic data, geological information, soil characteristics, rainfall patterns, and disaster risk assessments. This wealth of data forms the foundation for the comprehensive evaluation of site suitability based on an array of criteria, including physiography, land slope, proximity to rivers, soil types, rainfall patterns, climatic conditions, land use and land cover, distance from geological structures, land systems, geomorphology, proximity to settlements, accessibility, distance from the Central Business District (CBD), and disaster risk assessment. The Analytical Hierarchical Process (AHP) methodology plays a pivotal role in the process by facilitating the comparison of criteria through matrix analysis and the derivation of relative weights for each criterion. To create the final suitability map, a weighted overlay method integrates the diverse suitability criteria maps, providing a comprehensive visualization of the most suitable sites for the petrochemical industry. An iterative post-aggregation constraint process is subsequently applied to identify potential sites, serving as the foundation for delineating areas suitable for the petrochemical industry’s establishment. The culmination of this extensive research effort reveals that approximately 31% of the Southeastern Nigeria region is deemed unsuitable for such industrial endeavors, primarily due to the presence of developed areas characterized by built-up infrastructure, residential, and commercial zones. In contrast, 35% of the region exhibits relatively lower suitability, while approximately 9% emerges as highly suitable for the petrochemical industry’s establishment.

Data Consistency, Transparency and Privacy in Blockchain

Authors-Mukta Patil,Udayan Gaikwad,Akash Bhagwat,Saniya Inamdar,Prof. Swati Jadhav

Abstract- -Block chain has swept the world by a storm. It has completely revolutionized many domains by providing more reliable features than traditional systems. It incorporated trust and security within existing systems, reduced human load and enabled processing of a large amount of data quickly. Its most celebrated features are providing privacy for sensitive data, ensuring transparency between the involved parties and an assured consistency of data in transactions across the network. This paper analyzes the implementation of these features, their strengths and weaknesses, the challenges and finally provides a few solutions to overcome the highlighted gaps. The implementation of this can be viewed in our project, wherein we created our own crypto currency transaction website, incorporating the mentioned features.

Shell and Tube Heat Exchanger And Their Study

Authors- M.Tech. Scholar Mohammad Shad Khan, Prof. Dr. Manoj Mohbe

Abstract- – A heat exchanger may be defined as a device that transmits thermal energy between two or more fluids of varying temperatures. Several industrial processes would indeed be impossible to complete without this equipment. Refrigeration, air conditioning, and chemical plants all use heat exchangers. It’s utilised for a variety of things, including transferring heat from a hot to a cold fluid. They’re commonly employed in a variety of industrial settings. Researchers had worked on a variety of projects in attempt to increase performance. The velocity and temperature contour fields upon that shell side, on the other hand, are much more complicated, and their performance is influenced by baffle elements such as their arrangement the spacing scheme.

Cost Efficient and Higher Accurate Intelligence Automated Highway System using Artificial Intelligence

Authors- Rajnandani, Prof. Shashikant B. Dhobale

Abstract- -Automated highway system (AHS) is an intelligent transportation system, which removes human drivers from the operation of vehicles during driving. AHS includes control problems from the vehicle level to the highway network and its challenging opportunities for intelligent mechatronics. This technology requires extreme accuracy in vehicle location within the least times. AHS refers to a set of designed lanes on a limited access roadway where specially equipped vehicles are operated under completely automatic control. It can help reduce fuel consumption and individual vehicle discharge. The AHS designed requires advanced sensors, actuators, and communication technologies. It managed transportation systems for traffic problems in big cities, congestions, accidents, delays. This technique can change the driving & safety scenario of India.

Improving Productivity in Flexible Manufacturing Environment

Authors-M.Tech. Scholar Piyush Savkare, Yogesh P Ladhe, Vipul Upadhayay

Abstract- – Improving productivity in a flexible manufacturing environment involves optimizing processes, utilizing technology, and fostering a culture of continuous improvement. Implement automated systems and robotic solutions to handle repetitive tasks, allowing human workers to focus on more complex and value-added activities. Use robotics for material handling, assembly, and quality control to increase efficiency and reduce cycle times. Utilize APS systems to optimize production schedules, considering factors like machine availability, workforce capacity, and customer demand. Real-time scheduling can help adapt quickly to changes in demand or unexpected disruptions. The key to success lies in a holistic approach that combines technological advancements, process optimization, and a commitment to continuous improvement across all levels of the organization.

A Review on Thermal Analysis of Single Effect Vapour Absorption System Integrated with Vapour Compression System

Authors- Pradeep Kumar, Sujeet Kumar Singh

Abstract- – This abstract provides a concise overview of a review focused on the thermal analysis of a hybrid system integrating a single-effect vapour absorption system with a vapour compression system. This integration represents a synergistic approach to enhance overall system efficiency and performance. The review encompasses an in-depth examination of the thermal characteristics, energy transfer processes, and performance metrics associated with the hybrid system. Various configurations, control strategies, and operating parameters are scrutinized to identify key factors influencing the integrated system’s thermal behavior. Additionally, the review explores the implications of this hybridization on energy savings, environmental impact, and overall sustainability. The synthesis of vapour absorption and compression technologies in a single-effect configuration presents a promising avenue for advancing the efficiency of refrigeration and air conditioning systems, making this review valuable for researchers, engineers, and practitioners seeking insights into the thermal dynamics and optimization potential of such integrated systems.

To Increase Efficiency of Air-Cooled Condenser by Using Hollow Cylindrical Fins: A Review

Authors- M.Tech. Scholar Deepak Awasthi, Associate Prof. Dinesh kumar koli

Abstract- – In 1970’s in United States of America the history of introduction of Air-Cooled Condenser was traced. However, interest of researchers shifted towards this area from last few decades. The reason of this shift was the increased awareness of the world towards environmental safety, security and efforts to minimize the pollution level. Taking the above mentioned issue in concern heat-transfer by convection in Air-cooled Condensers is being studied in this work. Those are the several ways to increase the efficiency of condensers like first increasing the air flow rate by using the fan. Second by lowering the temperature of air which will be used as coolant .Third by using the water as coolant. But for the third way huge amount of water availability will be required. Fourth, by using the shading of condenser to minimize high temperature effect caused by solar radiation. Fifth, Wind walls can be used to shift the direction of air according to the requirement of condenser. Sixth, by changing the shapes of tubes in which refrigerant flows for improving the COP of refrigeration plant. The tube in which refrigerant is flowing, can be inclined that specific angel, to increase the refrigeration effect. Also outside cleaning of Fin also improve heat transfer coefficient. Primarily, this research paper target to improve the efficiency of air-cooled condensers. Also we focus to reduce the harmful effects on environment that may occur while using more electric power. As we use lesser energy so consequently Financial loses are also minimized. Moreover, the maintenance cost of equipment used in refrigeration plant will be reduced.

Cloud-Based Agriculture Monitoring System for Precision Farming Research

Authors- Akshat Verma, Diya Bansal, Sandeep Kaur

Abstract- – Cloud-based agricultural monitoring for precision agriculture. Precision farming relies on real-time data and advanced analytics to optimize farming. The system uses cloud computing to collect, process and analyse data from various sources such as weather, soil, crop health and equipment. Remotes and devices collect data from the field and then send it to the cloud for processing using machine learning algorithms and data analysis. Through a user-friendly interface, farmers can receive information and advice to help them make decisions on resource allocation, pest and disease management, water, and planting.

Very Low Permeability Geomembranes Geosynthetics Design and Analysis of its Erosion Control Behavior

Authors- Shivam Yadav, Jitendra Chouhan

Abstract- – Geopolymers emerge as an ecological alternative for construction materials. These consist of a mixture of aluminosilicate sources and an alkaline solution that dissolves the silicon and aluminum monomers that come from the source to generate a gel called GP that will control the main properties of the geopolymer. The geopolymer stands out for having good resistance to compression, as well as good resistance to high temperatures and corrosive environments. They have great potential as a replacement for classical technologies such as concrete, however, require further applied research to determine their feasibility on an industrial scale.

Enhancement of Higher Performance Concrete (Hpc) By Using Waste Tyre Rubber Powder and waste Plastic in Modified Road Construction Process

Authors- Rishi Seth Manik, Prof. Shashikant B. Dhobale

Abstract- -Now-a-days it is necessary to utilize the wastes effectively with technical development in each field. The old abandoned tyres from cars,trucks,farm and construction equipment and off-road vehicles are stockpiled throughout the country. This leads to various environmental problems which include air pollution associated with open burning of tyres and other harmful contaminants like (polycyclic aromatic hydrocarbon, dioxin, furans and oxides of nitrogen) and aesthetic pollution. They are non-biodegradable; the waste tyre rubber has become a problem of disposal. This paper is intended to study the feasibility of waste tyre rubber as binding material in bitumen, the waste tyre rubber is used with aggregate in different layer and also on the top surface layer mixed with bitumen in percentage and carried out different test result based on it, finding through it the difference in result by forming normal and rubber pavement and calculate the increase in strength of road pavement and also economically achieve.This is not only minimizes the pollution occurred due to waste tyres but also minimizes the use of conventional aggregate which is available in exhaustible quantity.

Formulation and Characterization of Solid Self-Nanoemulsifying Drug Delivery System of Ornidazole for Enhanced Dissolution

Authors- Deepak Dwivedi, Avinash K. Kondalkar, Ambika P. Arya, Deepak Tripathi, Jogendra Singh

Abstract- -The solubility of ornidazole was determined in oils, surfactants, co-surfactants, mixture of oils and mixture of surfactants. Among the tested oils, ornidazole exhibited significantly higher solubility in lemon oil compared to all other oils. Span 60 and PEG 400 used in ratios of 3:1 (F10-F12) only exhibited nanoemulsion area with shortest emulsification time (less than 1 min). It was observed that with increase in the ratio of the PEG 400, spontaneity of the self-emulsification process got increased. Avicel PH 102 was utilized as the adsorbent carrier for preparation of S-SNEDDS as it is safe and can be effectively used for production of solid SNEDDS. All the formulated S-SNEDDS exhibited drug content of more than 95% adsorbed on the carrier particles. The values of CI% and HR for Avicel PH 102 adsorbed mixtures indicated acceptable flow properties. The in vitro dissolution studies revealed nearly superimposable drug release profiles for S-SNEDDS powders and L-SNEDDS, respectively. All the formulations exhibited quick drug release characteristics and almost complete drug release in 15-20 minutes. In contrast, the pure drug exhibited only a maximum of 41% release in 60 min duration.

E-Commerce Store

Authors- Prof.Manila Gupta, Amaan Shaikh, Anzar Arai, Raiyyan Patel, Al Khalid Sardar

Abstract- – The Full Stack E-Commerce and Dashboard System is a comprehensive solution that leverages a combination of cutting-edge technologies to facilitate e-commerce operations and administrative tasks. Built on Next.js 13, React, Tailwind CSS, Prisma, MySQL, and integrated with Clerk Authentication and Stripe payment processing, this system offers a wide array of features and capabilities. Key components of this system include an admin dashboard serving as both a content management system (CMS) and an API gateway, capable of managing multiple vendors and stores. Vendors can create, update, and delete categories and products, along with the flexibility to upload and manage multiple product images. The system also allows for the creation and management of filters like “Color” and “Size” To ensure operational efficiency and data accuracy, the system provides detailed insights and analytics, enabling users to track orders, sales,and revenue via interactive graphs.

Deep Forest-Based Automatic Generation Control Strategy

Authors- Research Scholar Hussain Shaik

Abstract- – The current automatic generation control (AGC) method needs to be enhanced and optimized as the power system’s size continues to grow and the energy landscape drastically shifts. The grid AGC in use now primarily uses closed-loop PI control. This research provides a real-time AGC technique based on a deep forest network by learning an outstanding data set that combines the features of DFT control and PI control. The approach chooses the controller for the evaluation cycle for power deviation regulation studies based on which controller performed better in control during each assessment period. The simulation findings demonstrate that the technique outperforms all taught solutions and can accomplish real-time AGC regulation with fewer operations.

A Study on Enhancement Of Nutrition Elements
of Food Recipes Using Cassia Fistula

Authors-Alpa D. Odedra, Dr. Mita R. Rajpura

Abstract- -Several wild plants have been used for several purposes by indigenous people since time immemorial. Some of them are cultivated, and some are collected from the forest. These are commonly used in human nutrition, as medicines, and for economic purposes. They are known to be excellent sources of nutrients such as carbohydrates, proteins, fats, vitamins and minerals, dietary fibers, and diverse secondary metabolites. Among the wild plants, Cassia fistula is one of them. It is used as food (a flower), medicine (all plant parts), and has economic values. It is planted as an ornamental plant along the roadside due to its beautiful flowers. Medicinally, these plants have various pharmacological compounds that cure several ailments. Keeping this in view, an attempt has been made to gather the food recipes, along with their role in upgrading the nutrition value. The results revealed that all plant parts are used for various purposes, and their cultivation and conservation are needed for sustainable utilization and development. Because of its beautiful flowers it is planted as an ornamental plant by the roadside. Medicinally, this plant contains various pharmacological compounds that cure many ailments. Keeping this in mind, an attempt has been made to increase the nutritional content of the food recipe using Cassia Fistula. The results show that using this Ayurveda herb in food preparation increases its nutritional value and has a positive effect on its taste, aroma and appearance.

Enhancement of Heat Transfer Using Nano Fluids –A Review

Authors- Asst. Prof. Mani Vannan M

Abstract- – The important factor for rapid cooling and heating application is thermal conductivity. Modern nanofluid have high heat transfer rate compared to base heat transfer fluids because of low thermal conductivity .Nanofluid is nano sized particle such as metal, oxide, and carbide etc., dispersed into base heat transfer fluid. In this paper shows important factors affecting the thermal conductivity of nanofluid at different conditions. Many researchers had done to increase the heat transfer rate by considering high thermal conductivity of nanofluid. Fluid parameters like shape, size, clustering, collision, porous layer, melting point etc., affects thermal conductivity of the nanofluid .Thermal conductivity of Nanofluid can controlled by increased by controlling this type of parameters.

Environmental Friendly Brick Blocks Using E-Waste

Authors- Deepak Aradhya S M , Vaishnavi V Jois , Ruchitha Mahanthesh M

Abstract- – In this study, an effort was made to reuse more e-waste substances (e-waste) as fillers to increase the structural integrity of concrete bricks, which are frequently used in construction. Here, a typical brick molding technique is used to create novel concrete brick designs. Cathode ray tubes are where e-waste is most concentrated. Due to premature obsolescence of CRT equipment and rapid technological adoption, which is deemed dangerous to the environment when they are discarded of improperly, managing old beam tube (CRT) technology is a significant issue around the globe. In the past, their output rose in tandem with the requirement for computers and televisions, but not with the pace of technical advancement. Product innovations like plasma displays (PDPs) and liquid crystal displays (LCDs) have taken the role of televisions and computer monitors. In affluent nations, this change generates a disproportionate amount of outmoded CRT garbage, and in the generations to follow, poorer nations will overtake them as the primary CRT waste generators. In addition, CRT glass has a high silica content, a low tidal absorbance, and a fair intrinsic toughness, allowing it an appropriate filler for building materials. The structural analysis of the created concrete bricks demonstrates that e-waste conversion into coarse aggregates offers superior flexural strength and compressive compared to bricks made from standard sources. A quick summary of the present worldwide e-waste situation is given in this assessment, focusing on CRT waste and the magnitude of the demand for and available choices for processing, dumping, and current reclamation.

Futuristic Trends in Artificial Intelligence

Authors- Rahul Bora, Asst. Prof. Smita Parte

Abstract- – The field of artificial intelligence is interdisciplinary, combining ideas from information theory, cloud computing, big data, and machine learning. One important element is machine learning, which gives a significant benefit by utilizing historical data to allow machines to learn and forecast the future on their own. This method of making decisions is useful in a variety of industries, including social media, healthcare, and finance. Within the domain of Big Data and Cloud Computing, the continuous and increasing amount of data is processed effectively with low power consumption thanks to machine learning integration. This feature has the potential to accelerate the development of smart grids by offering a platform that includes AI and IoT support in addition to communication infrastructure. A multitenant system is made possible by this integration, a feature that will be further enhanced by the upcoming Massive Internet of Things (MIoT), which is a key component of the 5G and 6G network environment. It is anticipated that the continuous progress in Deep Learning, AI, and ML will produce better results for applications down the road. This conversation explores the new developments and difficulties in the field of artificial intelligence.

Physical characterization of Soil from BudhaBagicha Area, Balrampur, Chhattisgarh and its comparative Study with Soils of Other Areas

Authors- Asst. Prof. & HOD Shailesh Kumar Dewangana, Mushkan Mahantb

Abstract- -This research paper focuses on the assessment of physicochemical parameters of water quality in Ajirma-Kalan, Ambikapur, Chhattisgarh. Water quality is a crucial aspect of environmental and human health, and understanding the physicochemical properties of water is essential for evaluating its suitability for various purposes. The study involves the collection of water samples from different sources in Ajirma-Kalan, including rivers, lakes, and groundwater. These samples are then analyzed for various physicochemical parameters, including pH, turbidity, total dissolved solids (TDS), heavy metal contamination, nutrient levels and microbial contamination. The data obtained from these analyses provide valuable insights into the quality of water in Ajirma-Kalan. It helps in identifying potential issues such as high turbidity, elevated levels of TDS or heavy metals, nutrient imbalances, high BOD, pesticide contamination, and microbial risks. This information is crucial for understanding the overall health and safety of water sources in the area. The results of this study will contribute to a better understanding of the physicochemical properties of water in Ajirma-Kalan and their implications for environmental and human health. It will help in identifying areas of concern and guide future interventions and management strategies to improve water quality in the region. This research is vital for policymakers, environmental agencies, and local communities as it provides valuable information to make informed decisions regarding water resource management, pollution control measures, and the protection of public health. Additionally, it serves as a baseline for future monitoring and comparison of water quality in Ajirma-Kalan, Ambikapur, Chhattisgarh.

Research- Introduction to SEO

Authors- Naila R.

Abstract- – The article provides a comprehensive guide to effective SEO strategies, centering on a profound understanding of Google’s algorithms. Emphasizing the significance of valuable content and a positive user experience, the guide navigates through the intricacies of keyword usage without succumbing to stuffing. With a keen focus on optimizing meta descriptions, titles, and initial content, the article unveils the strategic placement of primary, secondary, and long-tail keywords. It advocates for keyword diversification and explores various facets, including headings, Alt text, URLs, and anchor text links, offering a holistic approach to content optimization. The SEO pro tips underscore the importance of adhering to specified keyword limits in articles, and the reference to Google’s algorithms aims to demystify their impact on search engine rankings. The article encompasses a plethora of insights from experts, studies, and historical perspectives, weaving a rich tapestry of SEO knowledge. The diverse array of references underscores the dynamic nature of SEO, highlighting its continuous evolution and the need for adaptation to emerging best practices. Overall, the article serves as a valuable resource for practitioners seeking to master the nuanced art and science of search engine optimization.

NetSuite’s Next Frontier: Leveraging AI for Business Growth

Authors-Md Rokibul Hasan, Mba, Csm, Pmp

Abstract- – The incorporation of artificial intelligence with NetSuite presents substantial opportunities for organizations in the U.S.A. to streamline their operations, boost decision-making, and enhance customer experiences. This research reiterates the key features and capacities of NetSuite, entailing financial management, e-commerce, inventory management, customer relation management, and human resources. This research presents the benefits of NetSuite consolidation, such as effective financial management, inventory management, and supply chain management. To completely optimize the capability for business growth, companies in the U.S.A. should keenly consider factors such as data quality and accessibility, efficient change management tactics, and proceeding support and training. By efficiently optimizing NetSuite’s AI capabilities, organizations can achieve operational productivity, achieve a competitive edge, and navigate the dynamic business landscape successfully.

Plant Leaf Disease Classification Using Feature Extraction with SVM and k-NN

Authors- Ms. Sadhvi Biltharey, Asst. Prof. Aditya Patel

Abstract- -The recognition of plants has become an active area as per the research many plants taxonomic group are at hazard of extermination. To preserve the endanger species new technique are been developed by the researchers. With the great advancement in the technology these days become the key factor for the identification the product and preserve that product with the help of mobile. The area of recognition of plants is trending nowadays because of the fact there are many more plant that need to be identify. More and more advancements are in progress to quickly and efficiently identify the plant. Today’s world is all about speed and accuracy of the results. In this thesis, an efficient method of learning is used for the purpose of classification. In this thesis, we are using two approaches to identify the leaf and both approach consists of three Phases such as pre-processing, extraction of classification and execution phase. And of them first approach is shape based and second approach is texture based. The pre-processing phase involves a representation of image processing steps such as grayscale transformation and limits improvement. The main contribution of shape based approach and texture based approach use support vector machine (SVM) and k-NN classifiers for efficient recognition of leaves. In shaped based approach 13 sheet characteristics that they are extracted and orthogonal into 6 main variables given as an input vector to the different methods and in texture based local binary pattern (LBP) use to extract features depending upon radius and nearest neighbor.

Twitter Fake Speech Detection Using Model of Machine Learning and NLPN

Authors- Deepika Saraswat, Dr. Nirupama Tiwari

Abstract- – Social Networking platforms are the most efficient way to express or convey one’s feelings or thoughts. The growth of social media platforms has led people to indulge in illegal and unethical activities. People started using social media platforms as a tool to express their hate, anger, and criticism towards an individual or an ethnic group. Today the use of Twitter is increasing day by day and the fact that most people come here to express their thoughts regarding social or economic problems, but some people use Twitter as a platform to target and spread hate towards someone based on sex, religion, race, etc using hateful hash tags. In this paper, we will be using Twitter tweets and NLP sentiment analysis techniques to detect whether the tweet is hateful or not. By categorizing the tweets into the label 0 and 1, where 0 represents non-hateful speech and 1 represents hateful speech. This helps us to detect and control hate speech.

Advancements in Health Monitoring through IoT and Flutter Integration

Authors- Asst. Prof. Dr. Pooja Nayak, Sindhu A, Skanda Prasad K S,Sneha V Viveki, Sumanth Eshwar Naik

Abstract- – After wearable sensors for health care monitoring were developed, industry markets received a lot of interest and these cutting-edge sensors also made an impact on health care monitoring device. One of the key components of the internet of things (IoT) that is being focused on here is smart and connected health care. This poll discusses data transmission, cloud storage, security, messaging alerts, and the prompt feedback provided by the many writers.. There is intense rivalry among the numerous frameworks available for developing mobile applications. These days, the aim of any developer is to make development easier, and flutter has come with the platform required to facilitate the development of applications for both the iOS and Android platforms, reducing the expense and complexity of doing so. Developers may construct trustworthy and high-performing applications for both iOS and Android with the help of the open-source Flutter SDK tool for cross-platform mobile application development. This paper aims to show why flutter is a superior app development platform compared to others. Virtus functions in this way as wireless sensor application layer middleware. Furthermore, an early warning system (EWS) will be implemented in the hospital to facilitate efficient patient monitoring.

A Review on Wind and Seismic Analysis of RCC Building

Authors- M.Tech. Scholar Manoj Chopdey, Professor Dr. Rajeev Chandak

Abstract- – This review paper provides a comprehensive analysis of the state-of-the-art methodologies and advancements in the field of wind and seismic analysis applied to Reinforced Concrete (RCC) buildings. With a growing emphasis on resilient and sustainable infrastructure, understanding the dynamic behavior of structures under wind and seismic loads is imperative. The review synthesizes key research findings and methodological developments, offering a valuable resource for engineers, researchers, and practitioners involved in the design and analysis, of RCC buildings.

Demo Model in Smart Home Application Design

Authors- Nguyen Tai Tuyen, Nguyen Dinh Thanh

Abstract- – This page introduces a demo model in designing an application for a smart home. In the smart home, appliances and amenities can be remotely controlled through mobile devices and computers connected to the Internet. The authors’ team used the ESP32 module, gas sensors, fire sensors, sound sensors, light sensors, temperature and humidity sensors, and the Arduino development tool to create the demo model. The model includes steps of program installation, smart home model design, installation, and testing of the demo model. The smart home model is connected with the Blynk application, allowing users to control the system through a smartphone. Various sensor systems are used to control security, monitoring, alerts, temperature adjustment, lighting system control, and surveillance camera systems. The model has been tested and works with full functions, collecting environmental information thanks to sensors and then processed before being sent to Blynk’s server before being transferred to the user’s handheld device.

Design of a Tiny ML-Based Predictive Heart Disease Screening Device

Authors- Acquah Andrews, Quaye Nii Attoh Gabriel

Abstract- -Cardiovascular disorders have become a global issue. There is a prevalence of heart failure and its impact on public health, particularly in regions with limited healthcare resources such as Africa. To combat this challenge, governments and healthcare institutions have initiated awareness campaigns and infrastructure improvements. Technological advancements, particularly in machine learning, offer promising solutions for early detection and prognosis. This study explores the application of Tiny Machine Learning (TinyML) in the context of heart disease, leveraging its potential for quantized models on resource-constrained devices. The research examines traditional diagnostic methodologies and highlights the application of TinyML in predicting heart failure. The research contributes a unique perspective by deploying a Shallow Neural Network (SNN) model on an Arduino BLE 33 Sense for heart failure prediction, focusing on eight features. The resulting classification report demonstrates the model’s accuracy of 82.61% and ROC_AUC of 92.15% for both absence and presence of heart disease. This paper serves as a foundation for future enhancements and applications in predictive healthcare technologies.

Embedded Dynamic Feature Selection Methods in the Detection of Fake Reviews in Social Media.

Authors- Nelson B. Wekesa, Dr. Kennedy Ogada, Dr. Tobias Mwalili

Abstract- – Technological advancement has led to the growth of internet users, and hence social media usage. Social media plays a pivotal role in society today as compared to in the past. However, there are chances of deceptive social media reviews with massive usage; Hence, it is imperative that there is a need for improved authenticity and robust fake social media reviews detection tools. Embedded feature selection methods seem to be more effective in detecting fake social media reviews owing to the massive content generated daily. This study aims to assess the use of feature selection methods to detect fake reviews in social media data. LASSO, RIDGE, and Random Forest classification methods are experimented. The study findings are that the three feature selection methods perform the same. Classification models using methods experimented had an accuracy of ninety percent (90%). Classification models without feature selection (all features present) recorded the lowest accuracy of eighty-nine percent (89%). The classification model using LASSO outperforms RIDGE irrespective of the penalization technique used. Feature selection is critical to improve the classification model prediction accuracy and precision, reduce training time, and test time.

Real Time Hand Gestures Recognition Using Convolutional Neural Network

Authors- M.Tech Scholar Ms. Kirti Sahu, Prof.Dr. Ashish Kumar Khare

Abstract- – HCI (Human-Computer Interaction) is a huge area that encompasses a variety of interactions, including gestures. In HCI, gesture recognition refers to nonverbal motions that are employed as a way of communication. A system could be used to detect and recognize gestures and use them to communicate information for device control. This is a significant field in HCI that deals with device interfaces and users. Gesture recognition is the process of recording gestures that are created in a specific way and then being detected by a device such as a camera. Hand gestures can be used for a variety of purposes as a means of communication. People with various disabilities, such as those with hearing impairments, speech impairments, and stroke patients, can utilize it to communicate and meet their fundamental needs. Hand gestures have been the subject of numerous studies in the past. Different strategies for implementing hand gesture tests were proposed in some publications. There are a variety of methods for extracting characteristics from photos, as well as Artificial Intelligence (AI), which offers a variety of classifiers for classifying various sorts of data. This study examines the problem using a variety of algorithms. This study used image processing technologies including Wavelet Transforms and Empirical Mode Decomposition to extract picture features in order to detect 2D hand movements.

Acoustic Environment Wireless Network Optimization by Frog Algorithm

Authors- Vikas Malviya, Sumit Sharma

Abstract- – Within underwater acoustic sensor networks (UWASN), achieving energy-efficient data transmission presents a formidable challenge. This is attributed to disruptions in acoustic transmission stemming from heightened noise levels, exceptionally prolonged propagation delays, an elevated bit error rate, restricted bandwidth capabilities, and interference. A paramount concern for UWASN researchers revolves around extending the longevity of data transmission. The intricate process of transferring data from a source node to a destination node in UWASN remains a complex and pivotal focus for researchers. Proposed model AWNOFA is a network clustering approach to reduce the energy losses during communication. Use of memeplex concept in Frog Algorithm has increase the work efficiency. Selected nodes were used in network as cluster center to pass packets to the base station. It was obtained that results are better than other existing methods of acoustic network optimization algorithm on different parameters.

AI In Cyber Security: Threat Insights And Prevention

Authors- Asst. Prof. Ms. Suvitha S, Asst. Prof. Mr. Selvaraj A, Gokulavasan P, Sakthivel P, Parisayram K

Abstract- -The document explores the symbiotic relationship between Artificial Intelligence (AI) and cyber security, with a particular focus on threat insights and prevention. In a rapidly evolving digital landscape where cyber threats continue to escalate in sophistication, AI emerges as a pivotal tool in fortifying defences. From understanding the foundational elements of AI in cyber security to dissecting the current threat landscape and envisioning future trends, this comprehensive exploration delves into the transformative impact of AI on proactive threat detection, automated response, and innovative prevention strategies. Addressing challenges, ethical considerations, and the intersection of AI with emerging technologies, the document offers actionable recommendations for organizations looking to bolster their cyber security posture. This abstract encapsulates the depth and breadth of insights presented, emphasizing the imperative of AI as a linchpin in modern cybersecurity. Harnessing sophisticated machine learning algorithms, AI analyses vast datasets to swiftly identify patterns indicative of potential cyber threats. This empowers security systems with real-time detection capabilities, reducing response times and mitigating potential damages. The continuous learning aspect of AI allows it to evolve and adapt to emerging threats, enhancing its predictive prowess. Automated response mechanisms further strengthen defences by facilitating rapid threat containment. By augmenting human capabilities, AI fortifies cybersecurity measures, creating a dynamic shield against a diverse range of cyber threats. The integration of AI not only bolsters traditional defences strategies but also anticipates future threats, fostering a resilient security infrastructure.

To Increase Efficiency of Air-Cooled Condenser by Using Hollow Cylindrical Fins

Authors- Deepak Awasthi, Asso. Prof. Dinesh Kumar Koli

Abstract- -In 1970 in the United States of America the concept of Air-cooled Condenser was came into light. But it was recently the last decade when experiments paid their attention to this concept. The reason behind this was to secure the environment for mankind. As the increasing pollution level caused in rise of global temperature thus causing the global warming which in turn creates inconvenience to humans and other living beings. The pollution levels were increasing day by day so this phenomenon created vast requirement to research in the field of innovative technologies in air conditioning. To overthrow such environmental conditions the researchers started to work upon this and propounded different- ideas. They also designed various devices.In this research work, it is being tried to upgrade the design of air-cooled condenser to raise the efficiency of refrigerator, central, split & window air conditioner, water cooler, Ice plants, freezer.For this purpose the shape of fins has been changed although the quantity of metal was same as used previously. By this concept the area of fins increases many times. As per the design used the efficiency output increases 2.215 times by taking suitable dimensions and by using the same quantity of metal. This research work has many advantages as given below- Primarily this research work is paying attention to enhance the efficiency of air-cooled condenser. Consequently the co-efficient of performance of air condenser is increased. Secondly it minimize the adverse effect on surroundings by making the air conditioning more environment friendly.It also curtails the unwanted energy wastage and manages financial loses. This design will also reduce the carbon emission across the world. Further more the maintenance cost of equipment also get reduced. And it becomes also customer friendly.

Sales Prediction and Inventory Restocking Using Machine Learning

Authors- Asso. Prof. Rashmi Amardeep, Prakhar Ananth, Priya B Gunali, Riya Raikar, S Deepak Dhore Reddy

Abstract- -In the dynamic landscape of retail, accurate demand forecasting stands as a cornerstone for optimizing inventory, enhancing customer satisfaction, and maximizing profitability. This research addresses the intricate challenges associated with demand forecasting in unorganized retail settings, with a specific focus on super shops. Leveraging machine learning and deep learning techniques, the study explores innovative algorithms to predict future product demand. The methodology begins with comprehensive data collection, overcoming hurdles inherent in unorganized retail data accessibility. Key attributes, including temporal factors (time, month, occasion, and location), promotional activities, and environmental influences, form the basis of the analysis. A statistical approach transforms raw data into a processed dataset, setting the stage for the implementation of three prominent machine learning algorithms—K Nearest Neighbor (KNN), Gaussian Naive Bayes, and Decision Tree Classifier. Results highlight the distinct strengths of each algorithm in capturing local patterns, probabilistic classification, and handling complex decision-making scenarios. Through a meticulous evaluation of performance metrics, K-Nearest Neighbor emerges as the most effective model for demand forecasting in unorganized retail. The research identifies critical factors influencing demand, including temporal variations, promotional impacts, weekend and holiday fluctuations, festival-induced demand shifts, weather conditions, product and market dynamics, shelf availability, brand category influence, and consumption rate dynamics. Past data proves invaluable, serving as a foundation for understanding non-linear demand patterns for individual products. In conclusion, the study contributes to the evolving discourse on demand forecasting in unorganized retail, offering insights into the multifaceted challenges faced by super shops. The identified best model, K-Nearest Neighbor, holds promise for retailers seeking to navigate the complexities of unorganized retail, enabling precise anticipation of consumer demands and informed decision-making. The implications extend to inventory optimization, resource allocation, and strategic planning in the ever-evolving retail sector.

Bankruptcy prediction of the BSE Sensex companies Using ALTMAN Z–SCORE Method

Authors- K M Prathiba, Asst. prof. Dinesh K, prof. Dr. Janet Jyothi D’souza

Abstract- -Detecting fraud and identifying manipulated financial statements are critical aspects of financial analysis and auditing. Fraudulent activities, such as intentional misrepresentation or manipulation of financial data, investors, and stakeholders. The effective detection of fraud is essential for the integrity of financial reporting and ensures trust in financial markets. We analyse financial distress among the BSE Sensex 30 stocks by applying the Altman Z-score to assess the financial health and bankruptcy risk of a company. Several key insights and techniques have emerged to enhance our understanding of fraud detection using the Altman Z-score tool and continually improve practices, safeguard the integrity of financial reporting, and promote investor confidence in the marketplace.

Object Movement Detection

Authors- Asso. Prof. Mr. Sriram Parabrahma Chari, N. Virachitha,
P. Sai Prabhas Reddy, P. Dikshitha 5P. Yathin

Abstract- -A primary objective of computer vision systems is to identify and track moving objects. Though, these systems often face challenges due, to factors, such as changes in the environment, which make the process of detecting moving objects difficult. Motion detection is a established field in computer technology and image processing that focuses on identifying types of objects or instances within digital photos and videos such as humans, flowers or animals. Object motion detection has been extensively studied for applications like face recognition, character identification and vehicle tracking. Additionally, object counting plays a role in refining and strengthening object detection using Open CV’s capabilities. Various techniques provided by Open CV are valuable for both object detection and counting tasks. Object counting finds its applications across domains including transportation, medicine, and environmental science. The continuous advancements, in computer vision and image processing research have significantly contributed to enhancing the quality of life. To overcome the limitations of existing and recently developed methods we conducted tests, on open-source images using a set of variables. The software system for motion detection presented in this paper enables us to detect movement in relation, to an object or a specific visual area. This project implements a motion detection and notification system using webcams, combining image processing and deep learning techniques. The system uses Open CV for real-time image capture and motion detection with image differentiation, and integrates a pre-trained You Only Look Once (YOLO) deep learning model with Py Torch. When motion is detected, the system takes a snapshot, sends an email notification with an image attachment for remote access, and sounds an alarm. This hybrid approach improves the system’s ability to efficiently detect and respond to movement. This application is related to the field of home automation, security systems and surveillance and provides an instant multimodal notification system with visual evidence and audible alarms.

Machine learning techniques and algorithms: A survey</strong

Authors- Unde Suvarna

Abstract- -Machine learning, which analyzes research and creates algorithms based on prediction from data. It creates a model based on the material used to make decisions or predictions. Machine learning algorithms help bridge the perception gap. In these articles we learn many machine learning methods and techniques and explains the concepts and process of machine learning, which is a well-known branch of computer science.

Evaluation of Gross Alpha and Beta Radioactivity in Potable Water (Tap and Borehole Sources) From Selected Areas With in Mubi-North Metropolis, Adamawa State, Nigeria</strong

Authors- Ahmad Ibrahim

Abstract- -Ionizing radiation is a type of radiation that carries enough energy to remove tightly bound electrons from atoms, creating ions. This type of radiation includes alpha particles, beta particles, and gamma rays. Exposure to ionizing radiation can have harmful effects on living organisms, including humans, especially when ingested. The primary objective of the work is to measure and analyze the activities of alpha and beta radiation in the water samples and then use these results to assess the water concentrations in terms of radiation levels. The study collected water samples from five different locations within Mubi-North Metropolis and analyzed them using a desktop Alpha/Beta counting machine or detector (MPG 2000B-DP).The results of the analysis are provided for both alpha and beta radiation activities in each of the sample locations. For instance, the alpha activities ranged from 0.009844 Bq/L to 0.1821 Bq/L, while the beta activities ranged from 0.04922 Bq/L to 10.21 Bq/L, across the different locations.
The overall conclusion drawn from the results is that the alpha activities in all collected samples were below the screening levels for drinking water radioactivity, as recommended by various organizations such as EPA, WHO, and GEG-FAO. However, the beta activities in the samples, except for the one from Federal Polytechnic Reservoir, exceeded the recommended screening levels. This indicates that the beta radiation levels in those samples could pose a significant health risk to individuals consuming the water.In summary, the study provides insights into the levels of alpha and beta radiation activities in water samples from various locations in Mubi-North Metropolis. The results indicate that while alpha radiation levels are within safe limits, beta radiation levels in some samples could potentially be hazardous to human health.

NANOFLOWERS- Recent Advances and Future Aspects for Multi Applications

Authors- Suryansh Pandey, Ajay Patil, Dnyanraj Parbat, Nikita Rane, Rajesh Oswal

Abstract- -Given their outstanding stability and increased efficiency, nanoflowers are garnering the interest of scientists and industry. Nanoflowers can be used in optoelectronics or sensors, catalysis, and solar cells. Nanoflowers have been identified to have a high potential for prospective applications in nanotechnology, such as sensors for hydrogen peroxide and glucose, as well as field emission features.Nanoflowers have a variety of range of uses, including purification of enzyme, dye and heavy metal clearance from water, and gas presence detection utilizing nickel oxide. Recent research demonstrates the 3D structure of nanoflowers for improving surface sensitivity using a methodology called Raman spectroscopy. Based on its great face area with volume proportion & exceptional adsorptivity effectiveness on its husk, the nanoflowers are future of optimum advanced drug delivery system.This article covers its extent on recent advances of nanotechnology related to nanoflower mainly in pharmaceutical field and future prospects of multi-application of nanoflower technology in field of pharma.

Towards an Intelligent and Adapted Small Scale Landslides Monitoring System in East Africa: Shyira Landslide Monitoring Using Sentinel – 1 SAR Data on Google Earth Engine Cloud Computing

Authors- Bernard Hakizimana1, Kingsley Chika Chukwu2

Abstract- -Rainfall-induced landslides pose significant threats in Rwanda’s North-Western provinces, contributing to major disasters. This paper addresses technological challenges in disaster response, specifically focusing on soil displacement quantification. The study centers around the Mukungwa River at a local scale, utilizing remote sensing techniques and community science. The methodology employs In SAR polarization and phase measurements, with a specific focus on Shyira Landslide Monitoring using Google Earth Engine Cloud Computing. A citizen science approach is seamlessly integrated into the study’s framework. The landslide detection methodology involves carefully selecting an Area of Interest (AOI) and distinct time periods Before Event (B Event) and After Event (A Event), corresponding to the landslide occurrence. To comprehensively represent ground surface properties pre- and post-landslide, SAR image stacks are generated. These stacks, calculated as temporal medians of SAR data, are constructed for ascending data, descending data, and a combination of both. Landslide detection entails assessing changes in the backscatter coefficient using the standard SAR intensity log ratio approach.
The classification process categorizes changes into three classes: stable, subsidence/decrease, and increase/uplift. To deepen insights, a CSV file is generated for statistical analysis, providing a quantitative examination of landslide event dynamics. The study conducts comprehensive statistical analysis and derives meaningful recommendations. This research significantly contributes to understanding landslide monitoring through a robust methodology that combines remote sensing technologies, community engagement, and statistical analysis. Findings include the impact and damages of the landslide; out of the 5,000 surveyed buildings, 96 were completely destroyed, 231 suffered extensive damage, and 1,150 were moderately affected. The derived recommendations have implications for disaster response strategies and underscore the importance of technological advancements in addressing the challenges posed by landslides.

DOI: /10.61463/ijset.vol.11.issue6.105

Artificial Intelligence Powered Tools for People who are Blind Users in Libraries – An Overview to Guide Special Users by the Librarians

Authors-Dr. Stephen. G, Mr. Asik Ikbal

Abstract- -Blind people access information through a variety of assistive technologies, such as screen readers, Braille displays, and audiobooks. Braille displays are devices that convert text into Braille, a system of raised dots that can be read by touch. Screen readers are software programs that read text on a computer or mobile device aloud, allowing blind users to access websites, documents, and other digital content. Blind people can use search engines (e.g., Google) to find information using screen reader software that works with search engines. Some screen readers also have built-in support for Google search, allowing users to perform a search and navigate the results directly within the screen reader software. Since electricity, artificial intelligence (AI) has been one of the most revolutionary technologies (Ng, 2018). The world as we know it was changed by electricity, which also enabled advancements in other sectors including manufacturing, transportation, and healthcare. In a similar vein, industry, healthcare, education, and finance have all benefited from AI and machine learning (ML) solutions. Information has always been guarded by libraries. Misinformation has become rife as AI takes over and libraries are unwilling to adapt. To find basic answers, users rely on voice assistants such as Google Assistant, Amazon Alexa, and Apple Siri. The user has few options because these private systems are created and maintained by massive technology companies, and the AI models they employ are opaque. This paper explained about library services to the visually impaired users, AI in libraries, various AI tools/apps for blind users and how to be used etc.

DOI: /10.61463/ijset.vol.11.issue6.106

Seizure Detection and Probability Prediction using Random Forests

Authors-Udayan Gaikwad, Mukta Patil, Akash Bhagwat, Saniya Inamdar, Saraswati Patil

Abstract- -This paper implements methodologies for seizure detection and prediction using mathematical techniques like FFT and Machine Learning classifiers such as Random Forest. The dataset for this project includes both training data, called Ictal and testing data called Interictal. For data pre-processing the Fast Fourier Transform is applied to each 1 second clip, taking frequency magnitudes in the range 1-47Hz and discarding phase information. Correlation coefficients and their eigenvalues are then calculated in both the time and frequency domains and are appended to the FFT data to form the feature set. This feature set is then trained on a Random Forest classifier using 3000 trees. The approach is used to train per-patient classifiers. A prediction module concludes this project by presenting the probability of seizure within a patient. The results are visualized for easy and clear representation.

Detection of Diabetic Retinopathy Using Machine Learning

Authors-Associate. Prof. Sri Parabramha Chari, T. Maanvi, V. Balaji,
T. Akanksha, DVS. Harshavardhan

Abstract- -High blood sugar levels are what lead to diabetes. Numerous illnesses, including heart conditions, kidney problems, nerve damage, and eye impairment, can be brought on by diabetes. Diabetic retinopathy is one such complication brought on by diabetes that, if not treated or detected in a timely manner, may also result in vision loss. By training algorithms on retinal images to recognize specific features, categorize the presence or absence of the condition, or divide the image into discrete parts, machine learning can be used to recognize and diagnose diabetic retinopathy. Support Vector Machine, logistic regression, Convolutional Neural Net-work, K-Nearest-Neighbor, and random forest are the current techniques utilized to identify diabetic retinopathy. The most often used deep learning methods for image detection are Convolutional Neural Networks. Toper form image classification tasks, a Convolutional Neural Network (CNN) architecture known as VGG16 was trained on a sizable dataset of images. For image classification problems, a well-known deep learning architecture is VGG16. The photos are classified using the retrieved features using a variety of machine learning techniques, such as KNN, SVM, Logistic Regression, Boost, Ada Boost, Decision Tree, Voting Classifier, Naïve Bayes, and Random Forest. This methodology is used to group diabetic retinopathy into one of five severity-based classifications (0,1,2,3,4). The proposed system will facilitate the removal of ambiguous diagnoses done by ophthalmologists. This would enable the faster and more accurate prediction and diagnosis of patients’ condition.

DOI: /10.61463/ijset.vol.11.issue6.110

Stubble Burning Problem in Punjab Region

Authors- Vinayak Sharma

Abstract- -Burning the leftover stubble from the harvest of grains like wheat and rice is known as stubble burning. One of the problems that contribute to environmental pollution and are becoming increasingly prevalent in the nation is the burning of stubble. Burning stubble releases particulate and gaseous pollutants into the atmosphere that have a detrimental impact on both human health and the environment. This is one of the main causes of air pollution worldwide. Even though it ranks third after industrial and vehicle emissions, it is still a substantial source of air pollution in many regions of the world.
The major aim of this paper is to look deeply into the problem of stubble burning in Punjab, analyse the trend of stubble burning events in different cities of Punjab with the help of data taken from Punjab remote sensing centre and provide effective measures that can provide alternatives of stubble burning to farmers and will help to combat pollution generated due to stubble pollution. The duopoly of two crops (wheat and rice) in Punjab in a rotating pattern is a root cause of stubble burning. As most of farmers in Punjab region are dependent on agricultural practices so they find it less costly and easy to burn the stubble after cultivation rather than treating it in an effective way. An extensive awareness programme is the need of hour from government to enlighten the farmers on the environmental and economic benefits of using alternative approaches of stubble burning. The pollutants released from stubble burning pose a threat to health which can lead to death. Also, a stubble burning is also providing extra thrust to global warming and climate change. This paper will examine the air quality during stubble burning period with the help of Air quality Index. It will help to provide alternative approaches to stubble burning that are environment friendly that can help India to fulfil sustainable development goals adopted in 2015. There are many technological solutions of managing stubble generated from crops that will provide a greener environment and also help in generating higher yield like Happy seeder. The government should have to provide incentive to farmers so that they can shift towards these technological measures. Also, Punjab should have to take a break on this crop rotating of wheat and paddy and shift towards cultivation of other crops.

Sales Prediction and Inventory Restocking Using Machine Learning

Authors- Associate Professor Rashmi Amardeep, Prakhar Ananth,
Priya B Gunali, Riya Raikar, S Deepak Dhore Reddy

Abstract- -In the dynamic landscape of retail, accurate demand forecasting stands as a cornerstone for optimizing inventory, enhancing customer satisfaction, and maximizing profitability. This research addresses the intricate challenges associated with demand forecasting in unorganized retail settings, with a specific focus on super shops. Leveraging machine learning and deep learning techniques, the study explores innovative algorithms to predict future product demand. The methodology begins with comprehensive data collection, overcoming hurdles inherent in unorganized retail data accessibility. Key attributes, including temporal factors (time, month, occasion, and location), promotional activities, and environmental influences, form the basis of the analysis. A statistical approach transforms raw data into a processed dataset, setting the stage for the implementation of three prominent machine learning algorithms—K Nearest Neighbor (KNN), Gaussian Naive Bayes, and Decision Tree Classifier. Results highlight the distinct strengths of each algorithm in capturing local patterns, probabilistic classification, and handling complex decision-making scenarios. Through a meticulous evaluation of performance metrics, K-Nearest Neighbor emerges as the most effective model for demand forecasting in unorganized retail. The research identifies critical factors influencing demand, including temporal variations, promotional impacts, weekend and holiday fluctuations, festival-induced demand shifts, weather conditions, product and market dynamics, shelf availability, brand category influence, and consumption rate dynamics. Past data proves invaluable, serving as a foundation for understanding non-linear demand patterns for individual products. In conclusion, the study contributes to the evolving discourse on demand forecasting in unorganized retail, offering insights into the multifaceted challenges faced by super shops. The identified best model, K-Nearest Neighbor, holds promise for retailers seeking to navigate the complexities of unorganized retail, enabling precise anticipation of consumer demands and informed decision-making. The implications extend to inventory optimization, resource allocation, and strategic planning in the ever-evolving retail sector.

Optimal Power Flow of Power System with Static VAR Compensator Using Moth Flame Optimization

Authors- P.Meenakshi, G.V.Nagesh Kumar

Abstract- – In the present transmission systems, it has become mandatory to utilize the available resources and also to substitute it with the renewable energy sources at the earliest. The optimal utilization of the resources provides an added advantage of reduction of its cost to the end consumers of electrical energy. In this paper, a multi- objective optimal power flow (OPF) in the existence of FACTS devices has been proposed for an integrated transmission system. The uniqueness of this paper is the choice of the multi-objective function. The objective function includes minimization of voltage deviation, power loss and negative social welfare (NSW). The reduction of loss and NSW ensures the reduction of per-unit charge of electricity at the customer-end leading to a greater customer satisfaction. The FACTS device used for the problem is Static var compensator (SVC). The hypothesis has been applied on an IEEE 14 bus system. The Mouth Flame Optimization Algorithm has been used for the optimization of objective function. The results obtained have been presented, compared and analysed in detail.

DOI: /10.61463/ijset.vol.11.issue6.104

Fake Profile Identification Using Machine Learning

Associate Professor. G Swathi, R Vaishnavi, Shaik Noorus Sabiha, P Rakesh Anand, P Nithish Kumar

Abstract- – The abstract highlights the enormous user engagement of social net-working sites like Twitter and Facebook and explores their substantial influence on modern digital life. It also highlights the influence that user interactions have on day-to-day life and the growing problem of spammers creating phony personas to disseminate unwanted information. The emphasis is the need for more effective techniques to identify and block phony social media profiles and material. The abstract recognizes the limitations of current machine learning-based techniques, pointing to low accuracy and difficult security maintenance. The suggested method uses supervised learning, especially Extreme Gradient Boosting (Xg boost), to distinguish between authentic and fraudulent profiles more accurately. Furthermore, a web page with a WSGI server is built to help recognize and flag these bogus profiles. Our proposed method gives an accuracy of 99%. The urgent problem of phony profiles and material on social networking sites is being ad-dressed by this strategy.

Analyzing and Designing a Full-Text Enterprise Search Engine for Data-Intensive Applications

Sourabh Sethi, Sarah Panda, Ravi Karmuru, Tarun Tayal

Abstract- -The process of designing and constructing an Enterprise Search Engine comes with numerous challenges. One major hurdle is creating a search feature capable of efficiently navigating through an extensive volume of generated data, a task that SQL databases have struggled with over the past decade. In SQL, the conventional approach involves constructing a B+ tree encompassing all posts. However, this method is effective primarily when dealing with smaller posts (single or double words). For larger text, the need to traverse each tree node becomes time-consuming. NoSQL databases encounter a similar issue; in key-value or column-family stores, searching for matching values or columns involves navigating through every row, a process that can be quite prolonged. The same holds true for document stores. In this research article, we delve into the challenges associated with implementing an enterprise search engine and propose potential solutions.

DOI: /10.61463/ijset.vol.11.issue6.107

Sales Prediction and Inventory Restocking Using Machine Learning

Associate Prof. Rashmi Amardeep, Prakhar Ananth, Priya B Gunali, Riya Raikar, S Deepak Dhore Reddy

Abstract- – In the dynamic landscape of retail, accurate demand forecasting stands as a cornerstone for optimizing inventory, enhancing customer satisfaction, and maximizing profitability. This research addresses the intricate challenges associated with demand forecasting in unorganized retail settings, with a specific focus on super shops. Leveraging machine learning and deep learning techniques, the study explores innovative algorithms to predict future product demand. The methodology begins with comprehensive data collection, overcoming hurdles inherent in unorganized retail data accessibility. Key attributes, including temporal factors (time, month, occasion, and location), promotional activities, and environmental influences, form the basis of the analysis. A statistical approach transforms raw data into a processed dataset, setting the stage for the implementation of three prominent machine learning algorithms—K Nearest Neighbor (KNN), Gaussian Naive Bayes, and Decision Tree Classifier. Results highlight the distinct strengths of each algorithm in capturing local patterns, probabilistic classification, and handling complex decision-making scenarios. Through a meticulous evaluation of performance metrics, K-Nearest Neighbor emerges as the most effective model for demand forecasting in unorganized retail. The research identifies critical factors influencing demand, including temporal variations, promotional impacts, weekend and holiday fluctuations, festival-induced demand shifts, weather conditions, product and market dynamics, shelf availability, brand category influence, and consumption rate dynamics. Past data proves invaluable, serving as a foundation for understanding non-linear demand patterns for individual products. In conclusion, the study contributes to the evolving discourse on demand forecasting in unorganized retail, offering insights into the multifaceted challenges faced by super shops. The identified best model, K-Nearest Neighbor, holds promise for retailers seeking to navigate the complexities of unorganized retail, enabling precise anticipation of consumer demands and informed decision-making. The implications extend to inventory optimization, resource allocation, and strategic planning in the ever-evolving retail sector.

The Significance of IT Hardware Networking in the IT Industry

Manthan S. Jagtap

Abstract- – This research paper delves into the dynamic landscape of information technology (IT) hardware networking, tracing its historical evolution, examining its current significance, and forecasting future trends. In an era where technology is ubiquitous across industries, the role of IT hardware networking is pivotal. This study emphasizes the indispensable nature of physical components and devices that facilitate communication and data exchange within networks. The paper highlights the crucial role of IT hardware networking in supporting modern IT infrastructure, recognizing the professionals who play a vital role in ensuring its functionality.

DOI: /10.61463/ijset.vol.11.issue6.108

Diagnosis of Alzheimer’s Disease Using Deep Learning

Assistant Professor Sriram Parabrahma Chari, Naveen Narri,
K.Prashanth, K.Varshitha, K.Rishitha

Abstract- – Alzheimer’s disease is the most frequent cause of dementia in adults 65 years of age and older. It is a neurological condition that progresses and cannot be reversed. Prodromal Alzheimer’s disease detection is crucial because it can stop the patient’s brain from suffering significant damage. In this study, a Deep Learning methodology is suggested for a technique to identify Alzheimer’s disease from MRI. In the proposed approach, the Hippocampus region’s texture and shape features are extracted from MRI scans and used as multi-class classifiers to identify different phases of Alzheimer’s disease. The proposed strategy is being put into practice and is anticipated to provide greater accuracy when compared to traditional strategies. A degenerative brain condition that cannot be repaired, Alzheimer’s disease. Someone in the globe gets an Alzheimer’s disease diagnosis every four seconds. The outcome is fatal since it causes death. Therefore, it’s critical to identify the illness as soon as possible. The most common cause of dementia is Alzheimer’s. Dementia impairs people’s capacity for autonomous functioning through reducing their capacity for reasoning and interpersonal coping. In the beginning, the patient will forget earlier events. As the condition worsens, individuals will eventually lose memory of entire incidents. The disease must be identified as soon as feasible. This study suggests a model that may determine whether a person has mild, moderate, or no Alzheimer’s disease based on brain MRI sample pictures as input. For this categorization, we compare the resnet50 designs and indicate which one exhibits the most promising outcomes.

An Overview of Mathematical Modeling Across Disciplines

Professor Pankaj Kumar Dwivedi & Professor Dhiraj Dwivedi

Abstract- – Mathematical modeling is the process of using mathematical representations of real-world issues to both simplify and predict future events. Throughout the world, mathematical modeling has been utilized in educational research primarily to forecast students’ academic performance and to pinpoint critical elements that influence students’ learning. Mathematical modeling is a thorough and significant methodology that can be applied to a wide range of complex phenomena analysis and prediction. It is emphasized how common mathematical models are in many scientific areas and how effective they are at isolating complicated dynamics in disciplines like physics, biology, economics, engineering, and environmental science. An integrative review was done to document these research in the current study. An overview of mathematical modeling’s application in educational research is the aim of this article. Ten earlier pieces that had been published were found. Applications in physics, biology, economic modeling, engineering, control systems, etc. were covered in these English-language articles. This review guides readers through the complex world of mathematical modeling by providing an overview of the field’s growing trends, numerous applications, and underlying concepts.

The Phygital Paradox: Challenges and Innovations in Contemporary Business Marketing

Dr. Sagar Onkarrao Manjare

Abstract- – In the evolving Phygital era, where physical and digital realms converge, this research, guided by experts like Chen, Patel, and Johnson, explores the transformative changes in marketing strategies. We initiate by contextualizing the Phygital era, emphasizing its impact on consumer engagement and market dynamics. Our study delves into the complexities and potentials of Phygital marketing, aiming to critically examine challenges, propose creative solutions, and synthesize key insights for practical application. While the Phygital blend poses challenges, it simultaneously unlocks new business opportunities. Our holistic approach addresses difficulties, proposes innovative solutions, and discusses the marketing implications in this revolutionary paradigm. The findings underscore that Phygital marketing is reshaping customer interactions, brand narratives, and corporate strategies. This report serves as a guide for leveraging Phygital techniques, ushering in a new era of marketing innovation as organizations adapt to the changing landscape.

Synthesis, Characteriozation and Biological Studies of Some Transition Metal Complexes of a-Benzilmonoximethiosemicarbohydrazide-O-Chlorobenzaldehyde

Sandip Thube and Dr. M. A. Badgujar

Abstract- – Some thiosemicarbohydrazide derived α – benzilmonoximethiosemicarbohydrazie – o chlorobenzaldehyde (HBMTSoCB) and its Fe(II), Ni(II), Cu(II), Zn(II), Hg(II), and Co(II) complexes have been synthesized and analyzed. These analytical (elemental analysis, physical conductivity, and magnetic susceptibility measurements) and spectral (PMR, FT(IR), and electronic absorption spectra) properties were used to infer the complex structures and bonding in nature. All prepared trivalent metal complexes have octahedral geometries. Antibacterial activities against two gram-positive species, S. aureus (MCC 2408) and B. subtilis (MCC 2010, and two gram-negative species, P. aeruginosa (MCC 2080) and E. coli (MCC 2412), as well as in vitro antifungal activities against Candida albicans (MCC 1439), and Saccharomyces cerevisiae (MCC 1039).

Application of MS Project in Project Management

Research Scholar M. N. Balakrishna, Asst. Prof. G.N. Chetan

Abstract- – Building Information Modeling is known by the abbreviation BIM. Using a collaborative process, architects, engineers, developers of real estate, contractors, manufacturers, and construction experts can plan, design, and build a project using a single 3D model. By using the Autodesk Revit program to create a 3D model of the (G+4) Residential building, the current research study aimed to create various floor elevations. A research study was conducted to determine the project’s overall schedule and the best way to utilize the available resources. The scheduling, monitoring, and optimization of numerous construction tasks, as well as the usage of MSP software, were all made possible thanks to this initiative. It is possible to examine the number of building floors, which will reduce the time and expense. MSP is a project management tool that aids in resolving issues that arise with conventional. MSP provides proper project planning, allowing management to set up the resources as needed for the project. MSP software can be used to calculate the time required (942 days) for a residential building that will begin on Thursday, May 2, 2019, and end on Wednesday, May 4, 2019, and which will cost around Rs 28,463,555. According to the work overview chart, the baseline cumulative working hours (80,000 hours) were on 11/3/19 and continued to decrease until they reached zero on 11/29/20. They then stabilized until 4/17/22. The figure also shows that 99 percent of the work has been accomplished, 125 hours of work remain, and 201,119.93 hours have been put in. Real working hours range from 201,040 to 201,120 whereas the remaining working hours are between these two numbers.

Industry 4.0: Impact of Blockchain based IOT on Banking and Financial service

Research scholar Nalini G V S, Professor. Dr Sindhu

Abstract- – The first industrial revolution during the end of 18th and the beginning of 19th century, was marked by Mechanical production, driven by hydraulic and steam engines, mass production based on division of labor in the second, the third revolution was based on automated production supported by computer technologies, and today the world is witnessing industrial revolution 4 which is based on the virtualization and interconnection of technologies called Industry 4.0, the development of information technologies developed new instruments and products, the major players are, Big data, supply chain, IOT and AI. This paper is focused on application of IOT-Blockchain in Banking and financial services.The origin of Blockchain Technology is crypto currencies, where initially this Technology was used as a public ledger to record the transactions of crypto currencies. This Technology is based on three pillars, as Decentralization, Immutability and Transparency.

Drug Target and Delivery for Cardiac Arrest: A Review

Prof. N. Venugopal Reddy, Dr. N. krishna Priya, Assisant professr P.M.Ravi Kumar

Abstract- – Bio in formatic analysis can speed up the identification of therapeutic targets, the screening of drug candidates, and the refinement of those candidates. It can also make it easier to characterize side effects and anticipate drug resistance. Genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data, among other high-throughput data, have all contributed significantly to mechanism-based drug discovery and medication repurposing. Large structure databases of small compounds and metabolites, along with the accumulation of protein and RNA structures, homology modelling, and protein structure simulation, cleared the door for more accurate protein-ligand docking investigations and more insightful virtual screening.

BIM in Project Management-A Review

Research Scholar M. N. Balakrishna+, Assistant Professor G.N. Chetan

Abstract- – Project managers can utilize BIM to enhance cooperation, coordination, and communication on projects. They can ultimately promote the usage of a Common Data Environment and BIM Collaboration Format to boost project teams’ operational effectiveness in their capacities as “integrators.” Development of construction documents/assistance with conceptual design, and pre-project planning services were the three main application areas for BIM. With a similar contract structure, the usage of BIM reduced overall risk. BIM combines data from various disciplines to produce intricate digital renderings that are controlled in an open cloud environment for real-time collaboration. Using BIM improves decision-making, offers more sustainable solutions, and reduces costs for AEC projects. The success of early adopters makes it wise to investigate space management and asset management as places to start when utilizing BIM for the operations and maintenance of a building. In addition, several specialized uses, such as infection control, are being investigated. BIM, which is currently used primarily for making various assessments and analyses, can be defined as a technique that uses a three-dimensional parametric modeling technique to consolidate the information generated during the construction phase into a database in order to facilitate connections between data points. BIM technology can be utilized to dramatically minimize future building maintenance costs during the construction stage, increase construction quality, lower construction expenses, and speed up the construction process. Building information modeling (BIM), which enables us to involve clients, collaborators, and other stakeholders in the design and creation of built environments from the very beginning stages, has grown in importance as a tool for architects. This helps ensure the finished product while also saving time and money. Over the past few years, BIM (Building Information Modeling) has completely changed the AEC sector’s technical and financial viability. The field of project management is the one that can most effectively take advantage of the benefits of BIM’s long-term implementation. A genuine cradle-to-grave cycle requires processing vast volumes of data, a lot of responsibilities, and other things for which BIM is the one-stop solution. The requirement for asset management is a constant process. For effective project management, project managers must embrace BIM, analyze the BIM data, and capitalize on all of its advantages. BIM application was generally regarded as being between somewhat beneficial and extremely useful in project management knowledge areas. Professionals’ resistance to changing long-standing work habits, absence of qualified professionals the price of BIM technology Project managers’ lack of awareness, insufficient IT infrastructure, The price of BIM training Uncertain Return on Investment, BIM technology’s lack of interoperability, other similarly competitive developments, lack of support from upper management, risks connected to ownership and intellectual property, professionals’ lack of IT literacy, risks related to product liability and authenticity lack of customer demand, Absence of supply chain support.

Analyzing International Case Studies to Foster the Significance of a Public Square

Susan Mya Çaksın, H. Semih Eryıldı

Abstract- – The preservation and enhancement of urban green spaces have become more significant in the face of rapid urbanization and environmental challenges. This article investigates the historically distinctive and significant squares and the critical role that they play as a focal point of a city. This study also delves into design principles, and community engagement strategies that underpin the success of a square in urban context. Through a comprehensive analysis of case studies on Naqsh-e Jahan Square in Isfahan, Iran; Tiananmen Square from Beijing, China; Tahrir Square from Cairo, Egypt; Red Square in Moscow, Russia; Sultanahmet Square of Istanbul, Turkey and Plaza Mayor of Madrid, Spain; this article aims to provide important perspectives for professionals and stakeholders engaged in revitalization projects. It intends to give insights in creating thriving, culturally rich public spaces that contribute to the rejuvenation of the urban fabric.

A Review on CFD Analysis of Ejector Use in Refrigeration System

M.Tech Scholar Nawed Aqbal Khan, Professor Dr. Ajay Singh, Nitin Barodia

Abstract- – The refrigeration system plays an important role in various applications from households to industries. An ejector refrigeration system is one of the thermal driven refrigeration systems. The cycle of this type of refrigeration system is properly described by the previous works. The thermal driven refrigeration systems are commonly used to reduce fossil fuel usage in electricity production and resolve the global warming problem of carbon dioxide (CO2) production. An ejector refrigeration system has several advantages, including simple design with non-moving parts, low cost of operation, and ease of construction and maintenance. Note that the installation cost of the steam ejector is lower than that of a modern refrigerant ejector. Moreover, water (steam) is the most environmentally friendly substance and cheapest working fluid.

Fabrication of Smart Ventilation Blocks Enhanced with Mixed TiO2 Nanoparticles for Improved Compressive Strength

N. Vattanaprateep, P. Wongthong, P.Thunyamaneelertsakul

Abstract- – This research investigates the enhancement of mechanical properties, specifically compressive strength and density, in ventilation blocks through the integration of titanium dioxide (TiO2) nanoparticles. The study focuses on optimizing the mixture of TiO2 with the aim of improving the performance of these blocks. The experimental approach involved using a Universal Testing Machine (UTM-CY-6040A12) for compressive strength analysis. Four distinct samples with varying TiO2 percentages (N, Ti-02, Ti-04, and Ti-06) were fabricated and subjected to comprehensive analyses. The study reveals that Ti-06, with a 6% TiO2 concentration, achieves the highest density of 2.680 g/cm³, indicating effective void filling within the material matrix. Conversely, Ti-02 stands out for its exceptional compressive strength, reaching 322.60 ksc. These findings underscore the nuanced impact of TiO2 concentrations on material performance, offering valuable insights for the development of high-performance construction materials with tailored properties. The study contributes to the discourse on smart materials, highlighting the potential of TiO2-enhanced ventilation blocks in advancing sustainable and resilient construction practices by providing a pathway for enhancing both compressive strength and reducing the density of ventilation blocks for improved overall performance in construction applications.

DOI: /10.61463/ijset.vol.11.issue6.109

Automatic Timetable Generator

Associate Prof. Veena R S, Prashantha K K ,Punith M , Yashaswini K K

Abstract- – Professional colleges have exclusive streams of courses and each has its syllabus which includes numerous topics. In those colleges, colleges are teaching one-of-a-kind subjects in distinct semesters and additionally, inside identical semesters schools are dealing with two extraordinary topics. The important mission is that the timetable table is required to agenda in line with the college furnished time slots in which timetables are organized in this type of way so that faculty timings do not overlap. The time desk does not overlap with their different schedules and these timetables are effectively used by faculty In this painting, we develop the software of a time desk that may robotically produce a time desk following faculty to have time slots This system affords blessings to the college need no longer worry for time clashes; a human does no longer need to carry out permutation and combination and they can concentrate on other sports rather than wasting time through generating Time-Table. This gadget offers an efficient timetable generated in keeping with expert university requirements.

Parameter Optimization of Electric Discharge Machining on AISI M2 High Speed Steel

Gulshan Kumar, Jatinder Kumar, Tanvir Singh

Abstract- – In this investigation, electric discharge machining was carried out for the AISI M2 high speed steel. The aim of this research was to evaluate the impact of process parameters viz. gap voltage, discharge current and duty cycle on the process performance measures in the form of material removal rate, surface roughness and electrode wear. The machining was carried out according to Taguchi L27 orthogonal array followed by analysis with the help of Taguchi approach. The consequences of this study revealed that ANOVA results revealed that input parameters such as discharge current, gap voltage and duty cycle are significant parameters for each response. For surface roughness, duty cycle and gap voltage are most contributing factors. However, discharge current indicates the highest contribution for minimum tool wear followed by duty cycle. Moreover, duty cycle is the largest contributing to achieve maximum MRR followed by discharge current and gap voltage. According to Taguchi approach, the suggested best combination of the controllable input parameters for Ra, EWR and MRR are v3f1a1, v1f1a1 and v3f3a3 by employing Taguchi approach Confirmatory experiments have been conducted and significant improvement is observed.

Harmful Algal Blooms in Ocean Verses Lake and its Impact on Fishery Industry: A Review

Assistant Professor Kajal, Assistant Professor Sangeeta

Abstract- – Harmful algal blooms (HABs) represent natural incidents that can also be exacerbated by human-induced pressures on aquatic ecosystems. Certain HABs adversely affect aquatic fauna, including both wild and cultivated fish, as well as their habitats, resulting in subsequent impacts on human well-being. Other HABs are instigated by species that naturally produce toxins, leading to human health issues upon ingestion of contaminated seafood, direct contact with water, or inhalation of aerosolized toxins. The aim of this review article is to examine the multifaceted consequences of algal blooms, with a particular focus on their economic impact, effects on human health, implications for commercial fisheries, and consequences for tourism and recreation. It also provides an in-depth analysis of the various impacts of algal blooms on ocean and lake ecosystems, highlighting the importance of proactive management and mitigation strategies to safeguard both environmental and economic interests. Understanding these far-reaching consequences is crucial for policymakers, researchers, and industries to develop effective strategies for minimizing the negative effects of algal blooms on our ecosystems and societies.

Live Migration of Virtual Machines Using Mirroring Technique

Moorthy M, Poovarasan S, Sathish V, Professor Sasikala K, Assistant Professor Deebak S J

Abstract- – The escalating prevalence of virtualization technologies has underscored the imperative for efficient live migration of virtual machines (VMs) to ensure dynamic resource allocation, load balancing, and system maintenance. This paper introduces a novel approach for live VM migration leveraging a Mirroring Technique. The proposed technique involves real-time replication of VM state and content, enabling seamless migration while minimizing downtime and resource overhead. Our method integrates a sophisticated mirroring module within the migration process, facilitating swift and robust transfers between source and destination hosts.

Deep Learning Based Sentiment Analysis at the Sentence Level for Afaan Oromoo Text from Social Media

Lelisa Bushu Teso, T.Gopi Krishna, Mohamed Abdeldaiem Mahboub

Abstract- -This study aims to implement deep learning models for sentence-level sentiment analysis in Afaan Oromoo text, investigating the impact of integrating Emojis into the labeling dataset and model performance. As a crucial aspect of natural language processing (NLP), sentiment analysis involves classifying emotions into positive and negative sentiments. The unstructured nature of data obtained from social media, comprising comments and feedback, posed challenges for real-time customer sentiment analysis. To overcome these obstacles, diverse techniques were applied from data collection to model development. Data, sourced from various Facebook pages and YouTube channels, underwent preprocessing with NLP techniques, forming a dataset of Afaan Oromo text enriched with Emojis. CNN, LSTM, and BiLSTM models were constructed with word2Vec feature extractions. Experimental results revealed a decline in performance—LSTM, BiLSTM, and CNN models dropped from 73.34% to 72.03%, 72.88% to 71.88%, and 74.58% to 72.66%, respectively, when Emojis were incorporated. Evaluation on a binary dataset demonstrated accuracies of 89.60% (LSTM), 88.32% (BiLSTM), and 87.52% (CNN) with skip-gram, and 87.68% (LSTM), 87.36% (BiLSTM), and 88.64% (CNN) with CBOW. The CNN model exhibited superior performance with 74.58% accuracy on multi-class datasets using skip-gram, leading to its selection for sentence-level sentiment analysis in Afaan Oromo text

An Experimental Study on Mechanical and Durable Properties of Self Curing Concrete by Using Polyethylene Glycol 600 and Light Weight Fine Aggregate

Aditya Joshi, Assistant Professor Kishore Patil

Abstract- – In the present day’s concrete is one of the most rapidly used construction materials in civil engineering due to its high-quality durability and its strength. The durability and strength of concrete will be fulfilled only if it is properly cured. For curing of the concrete large amount of water is required so, in recent year’s new technique developed known as self-curing in which cure of concrete done by itself by retaining moisture content in the concrete. This paper represents the methods of self-curing concrete and past work done so far in this area. It was found that various chemical admixtures such as (PEG), (PEA), (PVA), (SAP), etc and naturally available material like lightweight aggregate, light expanded clay, wood powder, etc. were used as a self-curing agent. Hence this paper focuses on chemicals used, physical and mechanical properties such as (Compression strength; Tensile strength; workability; durability) of self-curing concrete. Literature reviewed shows the different techniques used for self-curing concrete. Keywords— self-curing concrete; mechanical properties; physical properties; lightweight aggregate (LWA), (PEG), (PEA), (PVA), (SAP).

Patent analysis in Biodiesel Research: A Scope for Mathematical Methods

Research Scholar Nandini G. C, Professor Asha Saraswathi B.

Abstract- – Biodiesel, a renewable fuel derived from both edible and non-edible sources, poses a potential challenge to traditional diesel. This research examines biodiesel-related patents from 2003 to 2018, sourced from the International Patent Database. The study comprises five sections: biodiesel overview, feedstock-based generation, catalyst development, recent production advances, and reactor technology. 2nd gen biodiesel, derived from trash due to its cost-effectiveness, garners attention. Large-scale biodiesel production utilizes reactor technology, with the continuous stirred tank reactor deemed a simple and viable option. Various parameters impact biodiesel yield, engine performance, and emission characteristics. Mathematical tools like Design of Experiments, Artificial Neural Networks, and Metaheuristic algorithms aid in experimentation, analysis, predictions, and optimization. A robust mathematical framework could enhance biodiesel research, ensuring cost-effectiveness, sustainability, and quality.

Review Paper on Microwave Aid Transesterification Process Parameters and Optimization Methods Applied

Research Scholar Nandini G. C, Professor Asha Saraswathi B.

Abstract- – This review explores microwave-assisted biodiesel production, emphasizing advantages like direct energy transfer and shortened reaction times. Catalyst types, microwave power, reaction time, free fatty acid content, and temperature are discussed. The impact of different catalysts and optimal microwave power ranges for enhanced production are highlighted. Challenges related to free fatty acid content are addressed, and innovative methods are explored. The review emphasizes the crucial role of temperature in biodiesel production and its influence on reaction rates and yield. The impact of biodiesel on engine performance and emissions is investigated, focusing on studies involving waste cooking oil biodiesel in diesel engines. Optimization through Design of Experiments and response surface methodology is discussed, with a suggestion to integrate AI tools, particularly artificial neural networks and deep learning, for enhanced optimization and prediction accuracy. In conclusion, the review underscores microwave-assisted biodiesel production as efficient and environmentally friendly. The integration of AI tools is recommended for a more streamlined and data-driven approach in optimizing biodiesel synthesis parameters.

Digital Transformation in Finance: Challenges and Future in India

Research Scholar Mr. Amar D. Pandya, Associate Professor & PhD Guide Dr. Mayuri Rathod

Abstract- – Digitization is the future. We can’t imagine a day without technology. How can you imagine a business surviving without the internet and technology? From the time we wake up to the time we go to bed at night, we are surrounded by technology all day long. Therefore, it is unimaginable to survive as an entrepreneur without making the decision to go digital. To survive in the face of competition and dynamics, companies must change the way they work. Artificial intelligence and automation are the future, and businesses must adapt not only to survive, but to compete successfully. The financial sector is no exception. And to operate successfully and cost-effectively, you need to keep pace with technology.

Project “DRISHTI” to Enhance Site Safety by Integration of AI, VA and PA with CCTV VMS

Surodh Dey, Prince Kumar Singh, Ankit Anand

Abstract- Digitalization of safety round by integration of CCTV Video Management System, Video Analytics and Public Annunciation system which will proactively identify unsafe act as well as Unsafe condition. Implementation of Artificial Intelligence and Video Analytics in CCTV system for early detection of Safety hazards including unsafe conditions as Fire and Smoke, collapse of person, manpower in hazardous zone as well as unsafe Acts as Violation of Safety Helmet, Safety jacket or Safety Harness at height work. Mentioned hazards are very common reasons of major and minor accidents, early detection of these issues can save human, equipment & environment. Along with alarming the operator for any detected violation, public announcement speaker is installed at various areas throughout the plant, from which the operator can announce at the particular area or all areas as per requirement for necessary action.

A Review on Development of Production Layout Model to Improve Production Efficiency

M.Tech Scholar Shubham Gondey, Professor Shyam Barode

Abstract- With rapid increasing of demand in production, industrial factories need to increase their potentials in production and effectiveness to compete against their market rivals. At the same time, the production process needs to be equipped with the ability to have lower cost with higher effectiveness. Therefore, the way to solve the problem about the production is very important. There are many ways i.e. quality control, total quality management, standard time, plant layout to solve the problems concerning productivity. Companies which currently intend to remain competitive should always seek improvements to achieve excellence in quality through the improvement of its processes and products, and also always target the reduction of production costs by improving production efficiency and rationalization of production resources. Thus, the development of production makes that organizations have to evolve and develop organizational and operational improvements, constantly reviewing procedures and management approach as well as the processes and products in an attempt to tailor them to the needs of market.

Application of Quarry Dust and Fly Ash in the Concrete – A Review

P. G. Scholar Deshmukh Parag Chandrakant, Assistant Professor C. M. Deshmukh, Assistant Professor Y. P. Pawar

Abstract- The application of quarry dust and fly ash in concrete is a topic of interest in the field of civil engineering and construction materials. The use of these materials as partial replacements for traditional cement and aggregates can offer several benefits, including environmental sustainability, improved mechanical properties, and cost-effectiveness. The literature review furnishes essential foundational insights into concrete technology, encompassing both the materials employed in concrete production and placing particular emphasis on the substitution of sand with quarry dust. The attributes of sand and coarse aggregates are critically appraised, while the assessment methodologies applicable to concrete aggregates are also surveyed. Additionally, an exploration of the fundamental engineering characteristics of conventional concrete as well as concrete incorporating quarry dust is presented. This document underscores the context for seeking sand alternatives in concrete and the incorporation of quarry dust. Furthermore, a succinct overview of existing published literature concerning the utilization of quarry rock dust is included.

Research of the Geometric Influence of the Tip of the Wing Blade on the Aerodynamic Characteristics of the Main Rotary Wing of the Mi-8 Helicopter

MSc Thanh Chung Le, Ph.D Truong Thanh Nguyen, MSc The Son Nguyen, MSc Thanh Cuong Nguyen

Abstract- The paper presents the results of calculations of aerodynamic characteristics of three types of ends on the relative efficiency of the propeller. The calculations were performed by the method of numerical modeling of hydrodynamic processes (CFD) by programs ANSYS FLUENT on the computer. The results of calculations of aerodynamic characteristics of three types of blade tips and their comparison with the results of experiments conducted on models in wind tunnels of Air Force Officer’s College are given. With the help of this method, it is possible to obtain quantitative estimates of the results of propeller geometry modification at the stage of helicopter design.

A Review on Increasing Sale, Profit Rate and Productivity Improvement Using Supply Chain Management

M.Tech. Scholar Jitendra Nagar, Professor Shyam Barode

Abstract- Though the tasks of the supply chain of the firm are linked to the vision, supply chain managing leads to a major advantage as it helps the company work faster. This provides more clarity over the process to provide products and services as per the expectations of customers. Leaders in supply chain management know the importance of the process for a business that is more than the movement of raw materials. There are innovations in the supply chain that can help companies offer the best service with collaborative systems. Supply chain management performs by integrating procurement, suppliers, and facilities of manufacturers, distributors, retailers, and customers while they work together by the production, buying, and sales cycles. This supply chain needs active management since it is impacted by several aspects of the control of the business-like environmental conditions, fuel prices, and so on. While a company is more aware of these aspects, it can effectively manage them. With efficient management of supply chain, production, inventory, distribution, vendor, and sale records are in strict control. The SCM shows the management of expenses at each step and offers products to customers in a quick manner.

Productivity have Proven their Importance in Economic Development Worldwide

Assistant Professor Prashant Sharma, Assistant Professor Vimlesh Kumar Soni

Abstract- This chapter presents an overview of the conceptual framework, design, action plan, and methodology employed in the research. It also describes the phases of research, description of data collection instruments used and the methods for analysis of data to test the hypotheses. The basic theory in this study is to provide the rational and theoretical justification for the methods that were employed. It encompasses the discussion of the research paradigm in terms of the qualitative/quantitative approaches and various research methods. This chapter addresses the development of an appropriate procedure for the research including a description of the process used to develop the survey questionnaire and the final sample selection.

A review on Adversarial Attacks on Deep Neural Networks in Image Classification

Aishwarya Zingade, Nishchay Nilekani, Mohammed Rafi

Abstract- Deep neural networks can be abbreviated as DNN. They are the core component of the many machine learning algorithms. They have recently become so popular and successful due to the introduction of artificial intelligence, which is shortly called as the AI. Deep learning have got the success in machine learning tasks in different domains. In recent years we can see that the DNN models are more prone to vulnerabilities. So it is important to study and research them and take the comprehensive steps to find the solutions. In this survey paper we discuss about the adversarial attacks that effect the DNN and the counter measures against these.

Analysis of Single-Story Building by Etab

Research scholar M.N. Balakrishna, Fouad Mohamad, Robert Evans, M.M. Rahman

Abstract- In civil engineering, structural analysis and structure design come before building construction. The resistance of the building to various loads must be examined. The way that various loads behave affects various structural parts, including beams, slabs, columns, shear walls, and footings. We need to design the members by observing their behavior. Analysis is therefore essential to a building’s structural design. The analysis also aids in the design of a cost-effective and safe construction process for any given project. Extended Three-Dimensional Analysis of Building Systems is referred to as ETABS. Concrete structures, skyscrapers, low- and high-rise buildings, and portal frame structures are all frequently analyzed using ETABS. Summary of a few of the analysis methods that ETABS offers. P-Delta, linear static, modal, response-spectrum, time-history, linear buckling, and nonlinear analyses are among the sorts of analyses that are in turn building any kind of structure. This paper’s case study primarily focuses on the structural behavior of single-story buildings with L-shaped layouts. The ETABS software models single-story R.C.C. framed buildings for study. ETABS characteristics include a strong graphical user interface along with unparalleled analytical, modeling, and design processes that are all connected via a shared database. The most comprehensive software package for building design and structural analysis is the new, cutting-edge ETABS. A wide range of materials can be designed with sophisticated and comprehensive capabilities using ETABS’s unmatched 3D object-based modeling and visualization tools, lightning-fast linear and nonlinear analytical power, and perceptive graphic displays, reports, and schematic drawings that make it simple for users to interpret and comprehend analysis and design outcomes. In this project, the ETAB analyzes the single-story building to determine the details of the reinforcement, deflections, bending moments, and shear forces for the given single-story building. ETABS software has been utilized for the analysis and design of beams, columns, and slabs. M-20 and M-30 concrete as well as Fe-415 have been employed as building materials. Single-story building design and analysis are completed in compliance with IS-Code requirements. The IS 456-2000 standards were followed in the design of the single-story building’s reinforcement and concrete to carry out the structural analysis and design without experiencing any kind of failure; 1. To use Indian Standard Codes to comprehend the fundamentals of construction; 2. To comprehend the limitations of the design for the structural elements of slabs, beams, and columns; 3. To create a detailed analysis and design of the structure’s 3D model using the E-TABS software. Thus, in the present research work, the design and analysis of a single-story building were carried out by using ETAB software and successfully verified as per IS456:2000.

A Review on Advancement of Fog over Big Data Using Load Balancing

M.Tech Scholar Hansraj Sah, Assistant Professor Jayshree Boaddh, Assistant Professor Ashutosh Dixit

Abstract- The fog extends the cloud to be closer to the things that produce and act on IoT data. These devices, called fog nodes, can be deployed anywhere with a network connection: on a factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras. IDC estimates that the amount of data analyzed on devices that are physically close to the Internet of Things is approaching 40 percent.1 there is good reason: analyzing IoT data close to where it is collected minimizes latency. It offloads gigabytes of network traffic from the core network, and it keeps sensitive data inside the network. Analyzing IoT data close to where it is collected minimizes latency. It offloads gigabytes of network traffic from the core network. And it keeps sensitive data inside the network.

Reliability Assessment of Electric Distribution System Based on Weibull Markov Stochastic Model. (A case Study of JEDC Dorowa 33kV Feeder)

Ogaji A. A, Professor U.O. Aliyu, Dr. Sadiq A. A.

Abstract- The research explores the assessment of reliability, encompassing the monitoring of system performance, evaluating reliability deterministically through outages or active failures, and assessing reliability stochastically by employing stochastic failure models. Each of these classifications plays a role in achieving a thorough comprehension of power system reliability, spanning from tracking historical performance to anticipating the system’s reactions to different disruptions. The objective is to guarantee the consistent, uninterrupted, and dependable supply of electricity despite various challenges and uncertainties. This was done using the Weibull Markov model to analyse Dorowa 33kV distribution feeder network. The result showed the feeder network was not reliable and thus required upgrade.

Comparison of Chloride Ingress in Pre-Conditioned Concrete Cubes-Slabs

M.N. Balakrishna, Robert Evans, Fouad Mohamad, and M.M. Rahman

Abstract- Ice builds up on the top surface of concrete slabs and bridge decks during the winter. De-icing agents are used to try and get rid of the snow and ice. Through tiny cracks in the concrete, these salts penetrate the reinforcing steel. These salts’ chlorides may interact with the reinforcing steel over time, causing the passive layer to dissolve and the steel to corrode. One of the most common reasons for the early demise of concrete infrastructure facilities, is reinforcement corrosion in concrete has significant negative effects on both the economy and public safety. In addition, atmospheric carbon dioxide may slowly seep into the concrete and interact with the alkaline pore solution. Through the pores in the concrete, chloride ions from winter maintenance tasks, the sea environment, or other contaminants might reach the reinforcement’s passive layer and de-passivate it. Thus, steel corrosion in concrete can be caused by chloride penetration, carbonation, and poor concrete cover quality. This results in a build-up of tension in the concrete, which deteriorates and dangerously reduces its structural durability. As a result, it is necessary to measure the chloride concentration in concrete slabs and cubes, which is a crucial element. In the current research, an effort was made to interpret the concrete chloride concentration in order characterizes the various concrete mixtures designed for pre-conditioned concrete slabs and cubes that were salt ponded with chloride solution for approximately 160 days in dry, fully saturated, and partially saturated conditions. Consequently, the goals of this research are, First, this study will look at how different conditions, such as dry, fully or partially saturated conditions, affected the results of chloride concentration in concrete slabs and cubes at different drill depths (30-40-50 mm) with different mixture proportions, in which compressive strength, slump, and w/c ratio value varied with constant slump as in the first case and with constant compressive strength as in the second case. In order to assess the chloride concentration under various exposure conditions, 72 concrete cubes (100 mm3) and 18 concrete slabs (450x450x100 mm) with concrete grades ranging from 25 to 40 N/mm2 were constructed. The findings indicate that the chloride concentration value increased in all planned mixture types in dry/saturated conditioned concrete slabs at lower drill depths as compared to greater drill depths. Comparing DCC/PSC/FSC slabs impregnated with solvent/water to control slabs for both constant higher compressive strength and variable slump value as well as variable compressive strength and constant slump value, it can be shown that the average chloride content was reduced in both cases. The average chloride content at drill depths (30-50 mm) in control concrete cubes was found to be somewhat larger in magnitude as compared to solvent-based and water-based impregnation concrete cubes for higher compressive strength and varied slump value. The average chloride concentration was found to be slightly lower in magnitude in control/solvent/water based impregnated partially/fully saturated concrete cubes at drill depths (30-50) mm as compared to dry conditioned control/solvent/water based impregnated concrete cubes for higher/lower compressive strength and varied/constant slump value. The average chloride concentration at drill depths (30–50 mm) in partially saturated control/solvent/water-based impregnation concrete cubes was found to be slightly higher in magnitude as compared to fully saturated conditioned control/solvent/water-based impregnation concrete cubes for higher/lower compressive strength and varied/constant slump value.

Puzzle Game for Learning Chemistry

Mr. D.D. Shinde, Mr. M.D. Walekar

Abstract- – In this paper we are going to explain the design of puzzle game based on chemistry. In this design of the game includes teaching of basic concepts of chemistry which are related to covalent bonds. The concepts of teaching chemistry to college students or young learners are not developed as of now but for small age students we can use this puzzle for understanding chemistry concepts. Basically these games are combination of fun and education using atoms and molecule. In this paper we are going to focus on the features of chemistry objects which we are going to include in our game. At the time of teaching the basic concept of Chemistry, for example bonds between atoms and how molecules are generated teacher normally gives examples to define the formation of bonds. Hence, the concepts of molecules and atoms are difficult to understand and imagine for the students. So this game Atoms and Molecule i.e. A2M will help to understand the formation of bonds.

Physics in Computer Science – Importance and Uses

Mr. M.D. Walekar, Mr. D.D. Shinde

Abstract- – Physics and Computer Science are associated fields; both fields are handling universe fundamental principles. These principals are used by computer science with help of physics for algorithm development and for computing models. There are lot of physics concepts such as electromagnetism, thermodynamics and mechanics are used in applications of computer science for example machine learning, simulations, modeling etc. In this paper we are going to explain relation of physics and computer science and combination of both are using in Technology and scientific research.

Use of Mathematics in Computer Science

Mrs. Prachi P. Kulkarni, Mrs. Priya A. Bhosale

Abstract- – In Every Computer Application there is important and fundamental part is Mathematics. Every programmer or computer scientist should have some mathematics knowledge. We can say that mathematics in foundation of every computer application and computer science is built on mathematic concepts. There is huge use of mathematics in computer science. If anyone want to make career in computer science then knowledge of mathematics will be helpful for example if anyone have knowledge of arithmetic and programming language then he/she can develop computer or mobile application easily and if he/she want to work on advance level in computer science then obliviously he/she will have to increase knowledge of advanced-level math concepts. So in this paper we are going to explain how mathematics is important in computer science.

Benefits and Drawbacks of Electronic Gadgets in Daily Life

Mrs. Vaijayanti S. Yeole, Miss. Preeti S. Mohare

Abstract- – As we know that in today’s world electronic gadgets are most important in daily routine. In our daily work life, office life, for communication everywhere we are using electronic gadgets for an example smart watch, Mobile phones, Laptops etc. In this paper we are going to discuss advantages and disadvantages of electronic gadgets in our day-to-day life, which will more focus on smart watches. In our daily life, with a special focus on smart watches.

A Study of Solid Waste Management in Current Indian Prespective

Dr. Prerna Mittal, Assistant Professor Dr. Jugmaheer Gautam

Abstract- – This exploration paper presents a far reaching survey on strong waste administration from an Indian situation It gives an outline of the ongoing status, difficulties, and open doors in the field of strong waste administration, with an emphasis on ecological manageability and general wellbeing. The review looks at the kinds and structure of strong waste produced, existing waste administration rehearses, and the legitimate and strategy system encompassing waste administration in the country .It likewise investigates the assortment and transportation of strong waste, as well as removal and treatment techniques. The audit recognizes framework and asset limitations, institutional and administration issues, and natural and social effects as significant difficulties. Besides, it features amazing open doors for reasonable waste administration, including coordinated squander the executives draws near, mechanical advancements, and local area commitment. The discoveries of this survey add to the comprehension of strong waste administration in India and give bits of knowledge to policymakers and experts to foster compelling and economical waste administration techniques for the country.

DOI: /10.61463/ijset.vol.11.issue6.667

Strategy of Performance Evaluation-A Review

Assistant Professor Shruti Mittal, Assistant Professor Deepak Chaudhary, Swati Tayal

Abstract- – Performance appraisal systems are utilized in organizations to assess the effectiveness and efficiency of their employees. A performance appraisal system is essential because each employee possesses a unique approach to tackling work. Performance appraisal aims to enhance work performance, clarify communication expectations, identify employee potential, and support employee counselling. In this paper, we provide a review of several well-known performance appraisal techniques, including their advantages and disadvantages. Some performance appraisal techniques include Ranking, Graphic Rating Scale, Critical Incident, Narrative Essays, Management by Objectives, Assessment Centres, BARS, 360 Degree, and 720 Degree.

DOI: /10.61463/ijset.vol.11.issue6.668

Investigating the Impact of Various Factors on the Online Shopping Experience: A Case Study of District Muzaffarnagar, Uttar Pradesh

Associate Professor Dr. Mohd Shadab Khan

Abstract- – In the modern digital era, the pervasive influence of technology has reached unprecedented heights, with the term “online” permeating every aspect of our lives. This phenomenon highlights the profound impact of the internet on our daily routines, particularly in the realm of shopping. This research paper delves into the realm of our routine habits, focusing specifically on the revolutionary impact of internet technology on the act of shopping. The digital revolution has ushered in a wave of time-saving measures, streamlined efficiency, advanced knowledge acquisition, the development of new skills and techniques. This study aims to draw attention to the influential factors related to online shopping that significantly shape individuals’ shopping habits and drive the growing dominance of online retailers over traditional ones. A sample of 536 individuals from different age groups and genders was selected from seven distinct zones in Muzaffarnagar City. By analyzing these data, the research identifies specific factors that exert a significant influence on individuals’ online shopping preferences, shedding light on the shifting dynamics of the retail landscape.

DOI: /10.61463/ijset.vol.11.issue6.669

ECC-Encrypted Secure Cloud Storage with Verifiable Data Sharing Ensures Privacy, Integrity, and Trusted Access Control

Dhanalakshmi V, Keerthana K, Saranya T, Sri Dharshini M

Abstract- – Storage and IT services are expanding so quickly that more data centers and server rooms are necessary for quick processing in the allotted time. Web-based computing is the product of an enormous shift in the way digital services as well as information technologies ( IT ) are provided and acquired. At the moment, there has been a rise in the trend of outsourcing data to distant clouds, where users contract with cloud service providers ( CSPs ) who supply large storage capacities at affordable prices. As a result, users can lessen the strain and upkeep associated with local data storage. In the meantime, they lose control over their data once it is stored in the cloud, which unavoidably results in additional security threats to confidentiality and integrity. Therefore, successful and effective techniques are required to guarantee the confidentiality and integrity of rented data on trusted cloud servers. But in order to use cloud computing, businesses must have faith that a service provider’s platforms are safe and offer enough data confidentiality for their clients. In order to solve these problems, we offer a secure and effective protocol in this work. Elliptic Curve Cryptography and Sobol Sequence (random sampling) form the foundation of our approach. With our approach, a third-party auditor (TPA) can routinely confirm the accuracy of the data kept at CSP without having to access the original data. By sending a modest, consistent quantity of statistics, the challenge-response protocol saves network communication. Most significantly, our protocol is private; malevolent parties never get access to the data contents. While keeping the same extent of security, the recommended approach also takes dynamic data processing at the file level to account. Our scheme is more secure and effective when compared to current schemes.

DOI: /10.61463/ijset.vol.11.issue6.670

Assessing the Mechanical Properties of Banna 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 1 %-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.11.issue6.671

Climate Change and Its Impact on Plant-Pollinator Dynamics: Shifting Interactions and Ecological Consequences

Authors- Assistant Professor Dheerendra Vadiraj

Abstract-The mutualistic relationship between plants and pollinators is essential for the functioning of ecosystems and the provision of critical ecosystem services, including food production. However, climate change is rapidly altering the environmental conditions that shape these interactions. This paper investigates the impact of climate change on plant-pollinator dynamics, focusing on how shifts in temperature, precipitation, and seasonal timing influence flowering patterns, pollinator behavior, and overall ecosystem health. We explore how changes in climate variables may lead to phenological mismatches between plants and their pollinators, which could affect plant reproduction and the abundance of pollinator species. Drawing on data from field surveys, climate models, and recent literature, we analyze observed shifts in flowering times, pollinator activity patterns, and the phenological synchrony between plants and pollinators across diverse ecosystems. Our results suggest that altered climatic conditions are causing earlier plant blooming, with varying effects on pollinator populations, which in turn influence pollination success and biodiversity. Additionally, we discuss the ecological consequences of these shifts, including potential cascading effects on plant community composition, agricultural yields, and broader ecosystem stability. The findings emphasize the urgent need for adaptive conservation strategies to protect plant-pollinator interactions in the face of ongoing climate change. The paper concludes with recommendations for further research, including the exploration of specific plant-pollinator interactions in a changing climate and the potential for adaptive behaviors in both plants and pollinators.

DOI: /10.61463/ijset.vol.11.issue6.672