Volume 12 Issue 5

Design, Modelling and Simulation of an Improved Manual Tyre- Changer Machine

Authors- Emomotimi Obonika Waratimi, Alexander N. Okpala,
Godfrey Ayeabu Sibete, Philip Yemi Olisa

Abstract-The conceptual designs for the manual tyre-changer machine (MTCM) was generated, detail design of the MTCM as well as proper material selection for the MTCM was carried out, and ultimately, the performance of the designed and fabricated MTCM was modeled and simulated. The results from the analysis showed that the design of improved manual tyre changer machine has mechanical advantage of bead breaker arm of 8.67, wall thickness of bead breaker arm of 4.8738mm, allowable bending stress of 80 N/mm2, allowable shear stress 40 N/mm2, shank diameter of bolt at A 10.84949mm, shank diameter of bolt at B 11.3743mm, height/thickness of nut at A and B 9.6mm, length of a side of the outer square section of insertion/extraction arm based on bending stress is 46.296mm, length of a side of the inner square section of mount/demount arm based on bending stress 38.58mm, angle of twist of a 60mm × 60mm × 5mm insertion/extraction arm while inserting/extracting a tyre on 16” × 7” rim was selected due to its lower angle of twist of 3.6848×10-9 rad. The circular pitch for the pinion gear was determined as 9.426mm.The circular pitch for the driven gear is determined as 6.284mm.The module for the pinion gear was determined as 3mm.The module for the driven gear was determined as 2.4mm.The number of teeth for pinion gear is determined as 20.The number of teeth for driven gear was determined as 30. The torque on driven gear is 4340.02N-mm and the force acting on gear teeth was determined as 144. 67N. The contact stress for the pinion gear was determined as 4.00N/m2. The ANSYS simulation results also showed that the maximum value of equivalent stress for the MTCM roller bearing was 3.2406Pa, the maximum value of equivalent elastic strain for the MTCM roller bearing was 7.8388 x 10-10m/m and the maximum value of total deformation for the MTCM roller bearing was 5.7215 x 10-8m. Conclusion and recommendations were made that ANSYS simulation tool applied in this study have shown that it is possible to predict how the machine components will behave in a real case and allows the engineer to see where the stresses, strains and total deformations will be the greatest and how the machine will behave with such occurrence to provide better reference for redesign of the machine.

Improvement of Electrical Power Load Demand and Stability in Electrical Power System with Incorporation of Wind Power Generation

Authors- Abass Balogun, Isaiah Gbadegeshin Adebayo

Abstract-Nowadays, the penetration level of wind generation in power system is one of the most evolving among renewable generation (solar and hydro generation) with a least cost of installation to support the power system expansion planning. Thus, this study aimed to improve the Nigerian power system stability and meet the load demand from customers during contingency with application of wind power generators. The new generation location was obtained by converting one of the load buses in Nigerian 31-bus power system to generator bus and a new generator admittance matrix using Y bus matrix was formed. Swing equation was employed and the behavior of generator during contingency was determined. Then, the L-VSI for each new addition generation was obtained and used to optimally identify new location for the placement of the wind generator in the power system. Simulation was done in MATLAB R2023a. The generator damping ratio, total active power losses and total cost of the generator were determined. It was revealed that the result verified the accuracy of effective placement of wind generator in the power system to meet the load growth.

DOI: /10.61463/ijset.vol.12.issue5.240

Awareness through Planned Teaching Program on Selected Reproductive Health Behavior, Reproductive Illness, and Utilization of Reproductive Health Care Services among Adolescent Girls: A Review Based on Available Literatures

Authors- Professor Mrs.Kalaivani

Abstract-India has realized and emphasized sexual and reproductive health rights while sexual and reproduction is the main concern in the country due to the restrictions, cultural, taboos, and beliefs. Delivering sexual and reproductive health care within the concept of public health policy, health of reproductive has been identified mainly, whereas, sexual health has remained largely given insufficient attention. The government health sector does not add the health of services of sexual and education of sexuality is not given in educational institutes like schools and colleges in way of comprehensive. This poor attention is because ill health of sexual is not fatal, does not end in debility, death or place the over burden on the public sector. This perception further stems from the fact that there are hardly any research studies or surveys of evidence-based that measure the cost of sexual ill-health, there is no link of sexual ill-health with other kind of diseases
Methods: A search strategy done from Pub-Med and Google scholar
Results: The findings of these research studies supply evidence for nurses to focus more on teenage population and emphasis on their reproductive health promotion initiatives.

DOI: /10.61463/ijset.vol.12.issue5.241

Weed Flora of Small Tea Gardens of North Lakhimpur District, Assam and its Traditional Medicinal Demands

Authors- Jitumoni Saikia

Abstract-Tea is a commercial crop so every tea grower is trying to complete eradication of unwanted weeds from his garden area for his economic benefit. To control these weeds, different kinds of herbicides are using by tea growers as per their requirements but when these unwanted plant species get favourable environmental conditions and negligence of tea growers they grow in large numbers in a very short period. Amongst these weeds, some are pharmacologically very important because these are rich in plant secondary metabolites. Some Indigenous community, peoples of North Lakhimpur district of Assam using these unwanted plants in better health services, traditionally. In tea garden, I have identified 71 weed species belonging to 36 families by observation method and some of them are using as leafy vegetables as well as traditional herbal medicines by indigenous community peoples of these tea garden area of North Lakhimpur district of Assam.15 species are found traditionally very high demandable medicinal plants.

DOI: /10.61463/ijset.vol.12.issue5.242

Proximate Composition and Mineral Contents of Raw and Cooked Vigna Unguiculata L. and Phaseolus Lunatus L. Procured From an Open Market in South East, Nigeria

Authors- Olife, Ifeyinwa Chidiogo, Ayatse, James O.I, Ega, RAI, Anajekwu, Benedette Azuka

Abstract-Encouraging consumption of indigenous legumes has great potential in ensuring adequate nutrient and energy intake by women of child bearing age, infants and children in poor settings. Therefore, research attention is being focused on better utilization of legumes in addressing protein energy malnutrition and food security issues in the developing countries. The objective of this study was to evaluate the proximate composition and Mineral contents of unprocessed and processed Vigna unguiculata L. and Phaseolus lunatus L. procured from an open market in South East Nigeria. The proximate analysis was carried out using standard procedure while the mineral contents were determined using Atomic Absorption Spectrophotometer (AAS). Moisture content of cooked samples ranged from 13-20% while that of raw samples ranged from 10-15%. Protein content of cooked samples ranged from 23.19- 28% while that of raw samples ranged from 20-28%. Also, carbohydrate was in the range of 49.8-53.9% and 51-62.97% for cooked and raw samples respectively. The dehusked raw lima beans (d/r) had the highest Zn content of 0.6506 mg/kg while the whole cooked cowpea (w/c) had the highest Fe value of 4.0949 mg/kg. The whole raw Cowpea (w/r) recorded the highest Mg and Cu values of 9.7008 mg/kg and 1.5080 mg/kg respectively. Whole cooked lima beans (w/c) recorded the highest Na value of 3.9859 mg/kg while dehusked cooked cowpea (d/c) had the highest value of 3.8572 mg/kg for K. The result showed that both legumes are rich sources of nutrient and energy. However, apart from dehusked raw cowpea (d/r), all samples of cowpea recorded higher values of protein than their lima beans counterpart and are therefore better protein source compared to lima beans.

DOI: /10.61463/ijset.vol.12.issue5.243

Development Along with Design of an Independent Riding Reaper Device

Authors- Berisso Woyessa Bekele

Abstract-Harvesting is the collection of mature crops from the field. A harvesting procedure involves cutting, installation, collecting, transferring, and then stacking the cut produce. Mechanization of harvesting operations is critical to lowering harvesting costs, crop production costs, crop loss, turnaround time, weather risk, and increasing benefit through proper technology. To achieve the desired purpose, a manually operated reaper machine is created and developed to assist farmers by lowering the cost of crop cutting and collection in the field while achieving the best efficiency possible within the limits. Research technique is a set of acts or procedures that must be completed in order to do research properly, as well as the intended sequence of these steps. Identify the problem statement. Studying the current design of the reaper machine Review the literature. Product development general design process steps for reaper harvester’s machine include planning, concept development, system-level design, detail design, testing and refining, and product ramp-up. In this step, the material properties are defined based on design. To simulate the reaper machine using Catia software, Prototyping, testing for validation, Interpret and report. Material is chosen based on the strength requirements of the different parts of the Reaper machine. The machine is built with locally accessible materials. This self-propelled reaper moves at a forward speed of 2.54 m/s. The cutting bar measures 120 cm in length and the bundling mechanism is dropped. The field capacity is 0.306 ha/hour, respectively. The needed manpower for harvesting one hectare of wheat and rice reaper harvesting field operation is two man-hours per ha. The amount of fuel required to completely fill the tank after harvesting the plot was measured to determine the amount of fuel consumed for reaping the test plot, which was 1.17 l/h.

DOI: /10.61463/ijset.vol.12.issue5.244

Fault Detection and Location in Power Systems: Advances and Techniques

Authors- Professor Neha Singh

Abstract-Accurate fault detection and location are critical for maintaining the reliability and stability of power systems. This paper reviews the latest advancements and techniques in fault detection and location, focusing on innovations in algorithms, technologies, and methodologies. We analyze traditional and modern approaches, including digital relays, machine learning, and communication-based techniques. The paper concludes with a discussion on emerging trends and future research directions in the field.

Advanced Design and Efficiency in Plate Type Heat Exchangers: Principles and Innovations

Authors- V.A.Bhosale, Pranav Patil, P.B.Dehankar

Abstract-This paper provides a comprehensive study of plate-type heat exchangers, highlighting their crucial applications across various industries. It delves into the fundamental principles of heat transfer—advection, conduction, convection, and radiation—and their specific roles in enhancing the efficiency and functionality of these systems. The paper also explores advanced design methodologies aimed at maximizing thermal performance while balancing cost-efficiency and sustainability. Furthermore, it evaluates performance metrics, particularly focusing on pressure drop and energy savings, to optimize heat exchanger operations. Innovations such as additive manufacturing and the development of polymer heat exchangers are discussed, showcasing their potential to revolutionize the design and production of these devices. The study’s insights contribute significantly to advancing thermal management technology, promoting energy-efficient and sustainable engineering practices. This paper provides a comprehensive study of plate –type heat exchanger, highlighting their crucial application across various industries. It delves into the fundamental principles of heat transfer-advection, conduction, convection and radiation and their specific roles in enhancing the efficiency and functionality of these system.

DOI: /10.61463/ijset.vol.12.issue5.245

IoT Based Capnography Device

Authors- E Gowtham

Abstract-The Capnography design which is hand held has a great demand due to its practical applications and effective usefulness in resuscitations of cardiac arrest, this is according to the American heart association. This proposed handheld capnography device which can used in a clinical place and also in any home environment is reported. The device which is will be developed with an infrared CO2 sensor, a very high resolution display. In addition to that, there are two rechargeable batteries of 7.6v, 0.99A, it is also attached with secure digital card having a capacity of 16GB which is incorporated to increase the usability and also portability of the device. A LCD monitors the CO2 values.

Smart Voting System Using Fingerprint Module

Authors- Ramya M, Veni Vijayan

Abstract-The design and development of a biometric information-based electronic voting machine authentication system is the subject of this article. Fingerprint devices are used in electronic voting systems to verify votes cast. The finger print based electronic voting system offers several benefits, such as eliminating the need for ID cards and simplifying the process by simply placing your finger on the sensor. Data is sent to the controlling unit of verification when a finger is put on the finger-print sensor. The controller retrieves the information from the storage device and compares it with the data of the current user. Users are permitted to cast ballots if their finger prints match those that have already been saved. You can cast your votes by simply following the directions that are all provided on the LCD.

DOI: /10.61463/ijset.vol.12.issue5.246

The Role of Green Chemistry in Reducing Environmental Pollution: A Sustainable Approach

Authors- Amit Parashar, Yogendra Kumar Saraswat, Vishal Goswami

Abstract-Green chemistry provides a long-term framework for minimizing environmental degradation by utilizing novel, environmentally friendly chemical processes. This study delves into the ideas of green chemistry, which emphasize waste minimization, energy efficiency, and the utilization of renewable resources in order to create safer, more sustainable chemical operations. Biodegradable materials, green solvents, and energy-efficient reactions are all important applications that decrease toxic wastes and promote cleaner industrial processes. Case examples in medicines, agrochemicals, and materials science demonstrate the practical advantages of green chemistry, such as reduced environmental impact, resource conservation, and cost savings. Despite hurdles such as high initial prices and industrial reluctance, green chemistry is evolving and offering realistic answers to global environmental crises. Green chemistry plays a critical part in the shift of industries to greener technology.

DOI: /10.61463/ijset.vol.12.issue5.247

A Performances Evaluation and Modelling of Solar and Wind Hybrid Power Generation Source

Authors- Dharmendra Malviya, Neha Singh

Abstract-The recent upsurge in the demand of PV and wind systems is due to the fact that they produce electric power without hampering the environment by directly converting the solar radiation into electric power. However the solar radiation, wind never remains constant. It keeps on varying throughout the day. The need of the hour is to deliver a constant voltage to the grid irrespective of the variation in temperatures, wind pressure and solar isolation. We have designed a circuit such that it delivers constant and stepped up dc voltage to the load. We have studied the open loop characteristics of the PV array and wind system with variation in temperature and irradiation levels. Then we coupled the PV array and wind system with the boost converter in such a way that with variation in load, the varying input current and voltage to the converter follows the open circuit characteristic of the PV array and wind system closely. At various isolation levels, the load is varied and the corresponding variation in the input voltage and current to the boost converter is noted. It is noted that the changing input voltage and current follows the open circuit characteristics of the PV array and wind system closely.

Microgrid Modelling and its Performance Identification Using Matlab Simulink

Authors- Bharat Lal Yadav, Neha Singh

Abstract-In this work, a Microgrid (MG) test model based on the 14-busbar IEEE distribution system is proposed. This model can constitute an important research tool for the analysis of electrical grids in its transition to Smart Grids (SG). The benchmark is used as a base case for power flow analysis and quality variables related with SG and holds distributed resources. The proposed MG consists of DC and AC buses with different types of loads and distributed generation at two voltage levels. A complete model of this MG has been simulated using the MATLAB/Simulink environmental simulation platform. The proposed electrical system will provide a base case for other studies such as: reactive power compensation, stability and inertia analysis, reliability, demand response studies, hierarchical control, fault tolerant control, optimization and energy storage strategies.

Assessing the Impact of Financial Inclusion on Agricultural Productivity in India

Authors- K.L.N.D. Harshitha, Assistant Professor Md Abusaad

Abstract-India’s agricultural sector, a cornerstone of its economy and a major source of employment, grapples with various obstacles, including inadequate access to financial services among farmers. While financial inclusion is seen as a potential driver for agricultural advancement, its effects on productivity remain unclear. The research aims to bridge the gap by evaluating how financial inclusion impacts agricultural productivity in India. This study also trying to discuss the extent of financial inclusion in agriculture sector in India. This study has used secondary data taken from the Reserve Bank of India and World Development Indicators from 1970 to 2020. The study employs an ARDL (Autoregressive Distributed Lag) model to examine the relationship between financial inclusion and agricultural productivity. The analysis uncovers both immediate and enduring influences of financial factors on agricultural output. Long-run results suggest that although expansive monetary policies may impede agricultural expansion, enhanced access to credit and increased fertilizer application stimulate productivity. In the near term, fluctuations in broad money supply and easing of financial limitations positively correlate with agricultural growth. Conversely, rising domestic credit growth and prior financial constraints have adverse effects. By shedding light on the intricate interplay between financial inclusion and agricultural productivity in India, this study provides valuable insights for policymakers. It highlights the varied impacts of different financial tools and policies over time, guiding the development of precise strategies to promote financial inclusion in ways that bolster sustainable agricultural development.

DOI: /10.61463/ijset.vol.12.issue5.248

Assessment of Safety Measures in Selected Science Laboratories across the Six Geopolitical Zones in Nigeria

Authors- Paul Sunday Mike, Sani Sambo Datsugwai Mohammed, Gbadegesin Yemi Hezekiah

Abstract-Science laboratory is central to scientific advancement and innovation. Laboratory safety aimed at preventing laboratory risks, hazards and protecting laboratory personnel from hazardous material exposure. Controlling a hazard at its source is the best way to protect students, employees, and visitors. This study was conducted to evaluate science laboratories’ safety measures across the six Geo-political zone of Nigeria. A total of one hundred and eighty (180) laboratories were engaged in the studies comprising thirty (30) laboratories from each of the geopolitical zones of the Country. The use of a structured questionnaire via Google form link was employed. The questionnaire covered administrative and procedural control, emergency procedures, waste disposal, training, personal protective equipment, engineering control, hazard identification, risk assessment, and laser and radiation safety measures. The study provides comprehensive results for the level of compliance in the use of safety measures in the University, Polytechnic, Post-basic school, and General science laboratories- laboratory services. The study revealed that there was no significant difference between the mean of various types of laboratories in the institutions (F (5, 175) = .289, p > 0.05) and across the six geopolitical zones of Nigeria (F (4, 176) = .116, p > 0.05). The total average for safety measures used range from 20.6% (Engineering Control Safety Measures) to 69.4% (Personal Protective Equipment Safety Measures) across the zones against the ideal standard of 100%. Conclusively, the low level of safety measure is a setback to science laboratory advancement and all laboratory personnel must strictly follow laboratory safety standard.

Driver Drowsiness Detection System

Authors- Hari Nivetha S, Sangeetha N

Abstract-Several traffic accidents occur every hour around the world, some of which are caused by drunk driving, lack of sleep, lack of attention behind the wheel and many other reasons that can be dangerous for both passengers and road users. The most common situation is insomnia, which can make the driver careless at the wheel, these things cannot be ignored. To avoid such situations, the driver’s drowsiness detection system is very effective in detecting drowsiness by calculating and estimating the driver’s blinking and eye size using a camera and corresponding software. The driver drowsiness detection system is based on the CNN machine learning algorithm, which is implemented completely offline and can alert with an alarm when the driver feels sleepy.

MedPredict Solutions: Medical Insurance Cost Prediction Using Machine Learning Algorithms

Authors- Assistant Professor Miss Shilpa Tripathi, Achintya Nivsarkar, Aditya Dubey, Ajay Shrivas, Vijay Shrivas

Abstract-This paper explores MedPredict Solutions, a machine learning system designed to predict health insurance prices more accurately and personally. By considering factors like age, BMI, smoking habits, pre-existing conditions etc, the model helps individuals get clearer estimates to better plan their finances. Insurance companies can also use this data-driven approach to price their products more fairly, leading to greater transparency in pricing. A number of machine learning models, namely Linear Regression, Decision Trees, Random Forest, and Gradient Boosting Machines, were adopted towards this prediction task. Each was tested on such performance metrics of Mean Squared Error and R- squared. Nonlinear models – Random Forest and GBM were the first indicators that performed far better than the more traditional linear models used because of their higher ability to identify the nonlinear relationships between health factors and insurance costs. The best result was achieved with the GBM model that was able to achieve the lowest MSE of 158.32 and R² score of 0.87, which signified the model’s ability to understand the intricacies characteristic of health care data. Beyond the insurance pricing problem, the present study may have wide applicability. For the individual, it offers the ability to make better financial planning based on personal health risks. It promotes fairness and transparency in pricing for insurers, while providing data-driven insights that can improve healthcare policies and lead to more equitable financing of the healthcare system.

DOI: /10.61463/ijset.vol.12.issue5.249

Automated Medicine Reminder Box

Authors- Assistant Professor Ms. Prajakta Upadhye, Mr. Aryan Pathak, Mr. Aryan Gomase, Mr. Atharva Gandhe, Mr. Ayush Dongre, Mr. Om Kondekar, Mr. Prajwal Bhakare

Abstract-The Automated Smart Medicine Box is an innovative healthcare solution designed to enhance medication management for patients, particularly the elderly and those with chronic conditions. This device integrates advanced technology, including Wi-fi connectivity, mobile applications, and reminder systems i.e LED and buzzer to ensure timely medication adherence. The smart box features compartmentalized storage for various medications equipped with sensors that track pill inventory and consumption. Users receive real-time alerts on their smartphones for upcoming doses, missed medications. Additionally, caregivers can monitor adherence remotely, improving patient outcomes and reducing the risk of complications from missed doses. By promoting better medication management, the Automated Medicine Reminder Box aims to improve overall health and quality of life for users while easing the burden on healthcare systems.

DOI: /10.61463/ijset.vol.12.issue5.250

Sustainable Applications in Transportation Engineering

Authors- Assistant Professor Venigalla Hema

Abstract-The transportation sector is one of the largest contributors to greenhouse gas emissions, accounting for approximately 23% of global emissions. As concerns about climate change and environmental sustainability continue to grow, the need for sustainable solutions in transportation engineering has become increasingly important. This paper reviews various sustainable applications in transportation engineering, including alternative fuels, electric and hybrid vehicles, green infrastructure, and smart transportation systems. The benefits and challenges of these applications are discussed, and case studies are presented to demonstrate successful implementations.

Market Efficiency and Nonlinearity: An Empirical Examination of the BET Index on the Bucharest Stock Exchange

Authors- Genia-Iulia Tabără

Abstract-This study investigates the efficiency of the Bucharest Stock Exchange (BSE) by analyzing the behavior of the BET Index and its Log Return, through various statistical and econometric tests. The research pri- marily focuses on evaluating the market efficiency of the BET index and its return, utilizing tools such as Run Test, Portmanteau (Ljung-Box) Test, BDS Test, Lo’s Modified R/S Test, and the Hurst-Mandelbrot Classical R/S Test. These methods are employed to examine the random walk hypothesis and detect any nonlinear dependencies within the BET index and return’s series, which are critical indicators of market efficiency. Descriptive statistics and distributional analyses are conducted to understand the underlying characteristics of the index and returns, including normality and the presence of fat tails. The findings reveal significant insights into the behavior of the BET index, contributing to the ongoing debate regarding the efficiency of frontier and emerging markets. The results suggest that there are noticeable deviations that indicate potential inefficiencies, particularly in the form of autocorrelation and nonlinear dependen- cies. This paper provides a comprehensive analysis of the BET index and its return, offering valuable implications for investors and policymakers regarding the efficiency of the Romanian stock market. The study’s findings underscore the complexity of market dynamics in emerging economies and suggest ave- nues for further research into the factors contributing to these inefficiencies.

DOI: /10.61463/ijset.vol.12.issue5.251

Sign Language to Speech Converter

Authors- Associate Professor Udhyami M B, Yeshwanth Naidu M, Joel Sibi

Abstract-This thesis introduces a novel firefighting robot designed with Arduino to enhance fire detection and extinguishment in hazardous environments. Utilizing two flame sensors, a water dispenser, and Bluetooth control, the robot autonomously navigates obstacles while scanning for fires. It can move in multiple directions and stops when encountering barriers, resuming once cleared. The robot’s microcontroller processes data to detect flames, activate extinguishing mechanisms, and provide notifications. This system aims to improve safety for engineers in industrial settings by swiftly addressing fire incidents, ultimately minimizing property damage and protecting lives in dangerous situations.

Ensuring Multidisciplinary and Interdisciplinary Higher Education in the Nation (NEP 2020): A Comprehensive Analysis

Authors- Professor G Vasanti, Assistant Professor B Divya

Abstract-National Education Policy (NEP) 2020 of India, through the promotion of interdisciplinary and multidisciplinary learning, aims to revolutionize the higher education system in the nation. The goals and tactics of NEP 2020 to guarantee a comprehensive, adaptable, and inclusive approach in higher education are thoroughly analyzed in this paper. In order to promote the development of well-rounded people with critical thinking, creativity, and problem-solving abilities, the policy seeks to dissolve the traditional barriers between the arts, sciences, and vocational studies through cross-disciplinary collaboration. Examining how these reforms affect curriculum design, teacher preparation, institutional frameworks, and the use of technology to support interdisciplinary learning are some themes covered in the paper. It also discusses the benefits and difficulties of putting such a bold strategy into practice, including the need for infrastructure, the distribution of resources, and the change in academic culture. In the end, this essay demonstrates how the NEP 2020 fits in with worldwide trends in higher learning and prepares the ground for a knowledge-driven economy that can deal with challenging social concerns.

Optimized PIFA with Improved Bandwidth and Gain for Bluetooth Devices and Wearable Appilications

Authors- Associate Professor Tvs Divakar, T Varshasri, T Gopala Krishna, Sk Gouse

Abstract-The designed antenna is proposed by Bluetooth energy communication which is commonly used in modern devices at 2.44 GHz. Considering it is to be used for a wearable device, it must be small in size and resistant to mechanical and temperature variations. An optimized planar inverted-F antenna (PIFA) with parasitic element is used to broaden the antenna bandwidth. The parasitic element’s end is shorted to reduce the patch size to 22 × 7.25 mm2, making it suitable for wearable devices such as Smartwatches. Also, the ground layer of antenna allows it to radiate well externally while interfering minimum with the internal medium behind it. As a result, the reflection coefficient is not affected by the changes in the internal medium. The maximum gain of the proposed antenna is 13.4 dBi, and its impedance bandwidth ranges from 1-2.7 GHz. Finally, the antenna can be fabricated into a wearable electronic device.

The Role of Circular Economy in Sustainable Construction in Thailand

Authors- P. Wongthong, P. Hathaipichitchai, P. Thunyamaneelertsakul, N. Vattanaprateep

Abstract-This study investigates the adoption and impact of circular economy practices in Thailand’s construction industry, focusing on perceived benefits, sources of construction and demolition waste, and the outcomes of various circular economy strategies. A survey of 400 industry professionals revealed that cost savings from material reuse (36%) and enhanced corporate image (22%) were the top perceived benefits. The demolition of old buildings (35%) was identified as the primary source of construction waste. Among the circular economy strategies, the use of recycled aggregates led to a 28% reduction in raw material costs, while on-site waste sorting and recycling resulted in a 20% increase in material reuse. Although less frequently implemented, modular construction and closed-loop water recycling also contributed positively. The findings underscore the need to expand and strengthen circular economy practices to promote sustainable development within Thailand’s construction industry.

DOI: /10.61463/ijset.vol.12.issue5.252

Automated Retail Billing: Streamlining Checkout with QR Codes and Object Tracking Using YOLOv8 and DeepSORT

Authors- Bishwambhar Dahal, Sirjana Bhatta, Sujana Acharya, Apsara Shrestha, Praches Acharya

Abstract-In the contemporary retail landscape, long checkout queues and the issuance of expired products present significant challenges to operational efficiency. To address these issues and enhance the billing process, we propose an innovative solution that automates billing while effectively managing sales data. Our system features a conveyor belt mechanism activated by a touch sensor, where products, each with unique QR codes, are placed. A camera captures live video of the conveyor belt, enabling real-time detection and decoding of these QR codes, along with immediate alerts for any expired products identified. The system generates a comprehensive bill detailing product names, IDs, and prices, while securely storing scanned data in a database for in-depth sales and profit analysis, complemented by graphical visualizations. Registered customers receive a PDF copy of their bill via email through the Simple Mail Transfer Protocol (SMTP), enhancing their overall experience. By employing the You Only Look Once version 8 (YOLOv8) model alongside the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm, the system ensures precise object tracking and accurate scanning of each product. The Raspberry Pi serves as the core component of the system, managing the integration of advanced hardware and software. This solution significantly improves the efficiency and accuracy of the billing process, offering a holistic approach to modern retail management.

DOI: /10.61463/ijset.vol.12.issue5.253

Job and Career Recommendation System using Dynamically Stabilized Recurrent Neural Network Optimized with Secretary Bird Optimization Algorithm

Authors- Minimol V, Assistant Professor Monika Verma, Associate Professor Pawan Kumar Patnaik

Abstract-The pressure to find employment for college graduates is growing as a result of the ongoing expansion of college and university enrolment scales. This emphasizes the inadequacies in college students’ employability, which can be strengthened and improved by political also ideological education’s function. In this manuscript, Job and Career Recommendation system using Dynamically Stabilized Recurrent Neural Network Optimized with Secretary Bird Optimization Algorithm (JCRS-DSRNN-SBOA) is projected. The input information is collected via Real time information set. The pre-processing segment then receives the information. In pre- processing segment, it removes unwanted information also replaces the missing values using Strong Tracking Variational Bayesian Adaptive Kalman Filter (STVBAKF). A preprocessed data is given to Synchro-Transient-Extracting Transform (STET) in order to extract the attributes of enterprise description and pupil conduct. Then extracted attributes are fed into Dynamically Stabilized Recurrent Neural Network (DSRNN) for job and career recommendation. Generally speaking, DSRNN does not disclose the application of optimization techniques to ascertain the ideal parameters for ensuring an accurate career and job recommendation system. Hence, Secretary Bird Optimization Algorithm (SBOA) is suggested here to optimize DSRNN, which precisely construct the job and career recommendation system. The proposed JCRS-DSRNN-SBOA method is implemented and the performance measures like Accuracy, Precision and Root Mean Square Error (RMSE) are evaluated. Proposed JCRS-DSRNN-SBOA method attains 21.19%, 23.82% and 21.98% higher accuracy, 23.54%, 22.65% and 23.18% higher precision are analyzed with existing techniques like Employment Management for College Students based on Deep Learning and Big Data (EMCS-DL-BD), C3-IoC: A career guidance system for assessing student skills using machine learning and network visualization (CGS-ASS-ML), and Enhanced Deep Semantic Structure Modelling method for job recommendation (EDSSM-JR) respectively.

DOI: /10.61463/ijset.vol.12.issue5.254

Risk Assessment of Chemical (H3po3) Plant and Storage Tank Hazards

Authors- Rutuja Kekare, S.V.Anekar, M.A.Patil

Abstract-Consequence analysis is a critical technique used to systematically evaluate the potential outcomes of hazardous events in industrial settings, such as chemical plants and storage tanks. This method involves predicting the magnitude and scope of consequences resulting from hazardous events, as well as estimating the likelihood or frequency of such occurrences. By grouping similar hazardous events, analysts can assess representative or bounding scenarios, providing a more comprehensive understanding of potential risks. Consequence analysis is typically carried out using specialized third-party software for consequence modeling and frequency assessment, although spreadsheets are often employed for certain calculations. This approach plays a pivotal role in ensuring safety, optimizing risk management strategies, and enhancing decision-making processes in industries dealing with hazardous materials.

DOI: /10.61463/ijset.vol.12.issue5.255

An Improved Big Data-Based Electronic Health Record (EHR)

Authors- Nwozor, Blessing. U, Asheshemi Nelson Oghenekevwe

Abstract-The application of electronic health record management (EHR) system proved to be a viable replacement for the manual system for handling medical records. However, there has been concerns over data confidentiality and security in the healthcare sector, given the need for immediate review of the traditional centralized database systems that has so failed to meet this expectation. This project therefore proposed the development an improved blockchain based electronic health record (EHR) management system using a patient-centered approach, blockchain- based system that efficiently manages and safeguards health-related data of an individual. The proposed system was implemented using the object-oriented analysis and design methodology (OOADM) which applies object orientation for the analysis and design of a software project development. The system was developed visual studio code (VS Code) hypertext preprocessor (PHP), making use of the Ethereum network and tools like web3.js and Ganache, adopting an approach that overcomes the constraints of the existing systems. A key component of blockchain technology, smart contracts provide the foundation for decentralized patient data processing and storing. These smart contracts ensure patient privacy and data security by conducting transactions in a safe manner. Especially, any changes made to a transaction can be validated and spread throughout the whole distributed network, improving data integrity. To enhance this system, a cryptocurrency wallet such as MetaMask can be used, which offers a centrally managed repository where medical records are securely accessible to authorized users, such as patients and doctors, at any time. With a robust framework for securely storing data with customized access permissions and facilitating the safe transfer of patient medical records, the new system outperforms the old one in terms of efficiency, credibility, and accessibility within the healthcare domain.

DOI: /10.61463/ijset.vol.12.issue5.256

The Relationship between Mental Illness and Career Burnout in High-Stress Industries in Australia: A Systematic Review

Authors- Hardik Patel

Abstract-This dissertation presents an attempt to understand through a systematic review the level of association between mental illness and career burnout experienced by professionals employed in high-stress jobs in Australia. High-stress industries, including healthcare, finance, and law enforcement, are particularly susceptible to the detrimental effects of mental health issues and burnout, which significantly impact employee well-being and organizational productivity. This study aims to determine the relationship between mental illness and burnout and examine the effectiveness of organizational strategies in providing solutions to minimize such risks. A systematic review of the literature was conducted with different databases, using predefined inclusion and exclusion criteria. Moreover, based on selected studies, the data were critically appraised for extraction in regard to the prevalence, etiology, and consequences of such mental illness and/or burnout in highly stressful occupations. An overview of organizational interventions designed to prevent mental illness and reduce burnout was also carried out. Key findings include the fact that mental disorders strongly associate with burnout at work and common stressors are work load, organizational culture, and lack of support. The established effective strategies include maintenance of an overall positive working environment, access to mental health resources, and implementation of flexible work policies. These improve the employees’ well-being and also benefit the overall organizational performance. This dissertation will contribute to this knowledge about the problems experienced in the mental health of workers within high-stress industries and also provide evidence-based recommendations to organizations toward fostering healthy work environments. The study highlighted effective preventive measures for managing the negative impacts of mental illness and burnout, which in the end benefit both the employees and the employers.

DOI: /10.61463/ijset.vol.12.issue5.257

Web Page Recommendation Using KNN Model and Genetic Algorithm

Authors- Phd Scholar Sumit Sharma, Associate Professor Dr. Pritaj Yadav

Abstract-Recommender systems are very helpful on the internet that suggest things personalized just for you. They’re great because they make it easier to find what you want without being overwhelmed by too much information. This paper is all about recommending web pages by looking at how people use websites and the content on those pages. They used a smart model called K-nearest neighbors to figure out which pages you might like based on what other people with similar interests have viewed. Then, they made these suggestions even better by using a clever algorithm called the elephant herd optimization. This work tested method on real website data to see if it actually works well. Since people’s internet habits are always changing, it’s important to have a recommendation system that can keep up. The results show that approach, called Elephant Herd-based Web page Recommendation (EHWPP), really makes things work better. In simpler terms, it’s like having a smarter system that helps you find the web pages visitor want in a way that suits their interests.

Implementing Reinforcement Learning in a Financial Decision Support System for Stock Market Investment

Authors- Ratnesh Kumar Sharma, Professor (Dr) Satya Singh

Abstract-The dynamic and complex nature of stock markets necessitates advanced tools for effective investment decision-making. Traditional methods often fall short in capturing market intricacies and adapting to rapid changes. This paper explores the implementation of reinforcement learning (RL) in a financial decision support system (FDSS) designed for stock market investment. Reinforcement learning, which allows agents to learn optimal strategies through trial and error, is particularly suited for the unpredictable environment of financial markets. We propose an RL-based model that leverages historical stock data and key financial indicators to develop and refine investment strategies. Our system employs advanced RL algorithms, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to maximize returns and manage risks. Through extensive testing on benchmark datasets, our model demonstrates superior performance compared to traditional investment strategies, achieving higher cumulative returns and better risk-adjusted outcomes. The results indicate that incorporating RL into financial decision-making processes can significantly enhance investment strategies, offering a promising avenue for future research and application in automated trading systems. This paper explores the application of reinforcement learning (RL) in developing a financial decision support system (FDSS) for stock market investment. Reinforcement learning, an area of machine learning where agents learn optimal actions through trial and error, offers significant potential in the dynamic and complex environment of stock markets. We propose an RL-based model that leverages historical stock data and financial indicators to optimize investment strategies. The system’s performance is evaluated against traditional investment strategies, demonstrating superior returns and risk management capabilities. This research highlights the benefits and challenges of implementing RL in financial decision-making and suggests directions for future enhancements.

DOI: /10.61463/ijset.vol.12.issue5.258

Eco-Tourism and Conservation in Uttarakhand: Opportunities and Challenges in Eco-Sensitive Zones

Authors- Associate Professor Dr.Nirmala Lohani

Abstract-In the biodiversity-rich and naturally beautiful state of Uttarakhand, ecotourism is now a crucial component of sustainable development. The state has designated a number of Eco-Sensitive Zones (ESZs), such as Nandhaur Wildlife Sanctuary, Bhagirathi ESZ, Nanda Devi National Park ESZ, and others, providing a framework for protecting fragile ecosystems and promoting environmentally conscious tourism. The study looks at the intricate relationships that these protected areas have with ecotourism and conservation. The goal of the study is to provide insights into how Uttarakhand might reconcile the development of tourism with ecological preservation by examining the potential, challenges, and policies related to eco-tourism within ESZs. The study also highlights the need for community involvement, appropriate planning, and sustainability for ecotourism in these sensitive areas.

DOI: /10.61463/ijset.vol.12.issue5.259

How do Impairments in Social Cognition Affect Interpersonal Relationships and Professional Interactions in Adults Diagnosed with ADHD

Authors- Hardik Patel

Abstract-ADHD, a neurodevelopmental condition, is characterized by obliviousness, hyperactivity, and impulsivity. Its symptoms include controlling impulses, maintaining attention, and regulating activity levels. Social cognition, which involves understanding and dealing with feelings, relating to others, and having broad social information, is crucial for personal and professional growth and exploring social situations. Distinguishing feelings can be challenging for adults with ADHD, leading to mistaken assumptions and conflicts in friendly relationships. The theory of the mind, which involves assigning mental states to oneself and others, can be challenging for adults with ADHD to comprehend and expect the activities of everyone around them. This can result in social misunderstandings and inadequate communication. Sympathy is essential for satisfying connections, but adults with ADHD may experience stress or shallow connections due to decreased empathy reactions. One of the foundations of social perception is understanding social signs and standards. An absence of social comprehension is a typical side effect of ADHD, resulting in awkward or unseemly conduct in various social settings. Confinement or social dismissal may follow, impacting both individual and professional relationships. In relational relationships, issues with communication can be prevalent, making it difficult for adults with ADHD to participate without squirming or forgetting about what others said. This can exacerbate problems, colleagues, and relatives, leading to conflicts and harming relationships. Impulsivity and dysregulation are common in ADHD, making it difficult for individuals in administrative roles to motivate their groups and resist them. In conclusion, ADHD affects adults’ social cognizance, leading to difficulties in interpersonal relationships and work environments. Understanding these effects is essential for developing tailored interventions and emotionally supportive networks for adults with ADHD.

DOI: /10.61463/ijset.vol.12.issue5.260

Experimental Study of Stone Matrix Asphalt in Pavement

Authors- Shivpal Singh, Prof. Jitendra Chouhan

Abstract-New technologies such as Stone Matrix Asphalt are now being adapted to provide long lasting roads and are having a capability of improvement in roads by using stabilizing additives such as Natural material in a Stone Matrix Asphalt Mixture. To verify the performance of Stone Matrix Asphalt various material are selected. For this analysis Characteristic, Volumetric properties and Test evaluation are to be done. Here in synopsis, various literature related to this have been presented. Methodology for performance evaluation for Stability Test and Volumetric properties is also being suggested. In this synopsis the steps carried out for the study of Stability Test as regards to guidelines, Test study to be carried out using (AASHTO T305).

Modelling and Estimation of Volatility Using ARCH Models in India’s Stock Market of Amazon

Authors- Archana Yadav

Abstract-This study investigates the volatility of Amazon`s inventory charge withinside the Indian market. It unearths that volatility isn’t random, however as an alternative cluster together, with durations of excessive volatility accompanied with the aid of using excessive volatility and vice versa. This is a not unusual place sample in monetary markets and is captured with the aid of using ARCH (Autoregressive Conditional Heteroskedasticity) models. The take a look at unearths that the GARCH(1,1) version is only at shooting Amazon’s inventory charge volatility. This manner that beyond volatility and beyond shocks each drastically have an effect on how risky the inventory might be withinside the destiny. The studies concludes that expertise those volatility dynamics can assist traders and investors make knowledgeable decisions. Investors can use this records to control threat and expand buying and selling strategies. The take a look at additionally shows that destiny studies ought to discover how different factors, inclusive of geopolitical occasions and technological advancements, can have an effect on Amazon’s inventory charge volatility.

DOI: /10.61463/ijset.vol.12.issue5.261

Dynamic Analysis of Bridge using Techniques of FEM

Authors- Research Scholar Sombrat Arjariya, Assistant Professor Rahul Sharma

Abstract-This research focuses on the free vibration analysis of bridges utilizing the powerful Finite Element Analysis (FEA) simulation software, ANSYS. Bridges play a critical role in transportation infrastructure, and understanding their dynamic behavior under free vibrations is essential for ensuring structural integrity and safety. The study employs ANSYS to model and simulate the vibrational response of a bridge structure subjected to natural excitations, such as wind or seismic events. The investigation aims to characterize the fundamental modes of vibration, eigen frequencies, and mode shapes of the bridge, providing valuable insights into its dynamic behavior. The results obtained from the ANSYS simulations contribute to optimizing bridge design, enhancing resilience against dynamic forces, and ultimately advancing the field of structural engineering. This research underscores the significance of FEA simulations in comprehensively assessing the dynamic performance of bridges, with potential applications in improving structural design practices and ensuring the longevity and safety of these critical infrastructure elements.

Applying Convolutional Neural Networks (CNN) to Job Recommendation Systems

Authors- M. Tech Scholar Reena Tiwari, Assistant Professor Mrs.Vaishali Upadhyay

Abstract-This research proposes a Convolutional Neural Network (CNN) model for job recommendations, comparing its performance with existing methods like Random Forest, Linear and Logistic Regression, Decision Tree, Naive Bayes, AdaBoost, and Gradient Boosting. The CNN leverages word embeddings to capture semantic meaning and contextual information from job descriptions, aiming to enhance recommendation accuracy. Various CNN architectures, including different convolutional layers, filter sizes, and pooling layers, are explored. The study also examines hybrid approaches and transfer learning using pre-trained models to further improve performance. Regularization techniques, such as dropout and L1/L2 regularization, prevent overfitting. Hyperparameters are tuned using grid search or Bayesian optimization. The model’s effectiveness is evaluated using metrics like accuracy, precision, recall, and F1-score.

Comparison of Tableau and Microsoft Excel: An Analytical Overview

Authors- Ms. Mayuri Nikam, Mr. Dattatray Bhosale

Abstract-Today everyone relies on powerful tools to analyze, visualize, and interpret data. Tableau and Microsoft Excel are two of the most popular tools for data analysis, each offering unique capabilities. This paper aims to compare Tableau and Excel in terms of functionality, usability, data visualization, and practical applications. We provide a detailed examination of both tools using two examples to demonstrate their strengths and limitations in different scenarios. By understanding the key differences, users can make decisions based on their specific analytical requirements.

DOI: /10.61463/ijset.vol.12.issue5.262

Big Data in the Oil & Gas Industry

Authors- Matthew N. O. Sadiku, Chandra M. M. Kotteti, Janet O. Sadiku

Abstract-With the recent introduction of data recording sensors in exploration, drilling, and production processes, the oil and gas industry has transformed into a massively data-intensive industry. These data can come from sensors, data recording devices, spatial and GPS coordinates, weather services, and seismic data. Since the data recording devices and sensors are different in types, the generated data can be in different sizes and formats. The vast quantity of data is challenging to be handled due to storage, sustainability, and analysis issues. The main application of big data is to provide processing and analysis tools for the increasing amounts of data. Big data analyzes huge data sets to reveal the underlying trends and help the engineers forecast the potential issues. This paper reviews the utilization of big data and data analytics in the oil and gas industry.

DOI: /10.61463/ijset.vol.12.issue5.263

Comparitive Study of Practicle Education with Theoretical Education

Authors- Vaishnav P. Khorate, Assistance Professor G.Anburaj

Abstract-It emphasizes the importance of both forms of learning, noting how theoretical education provides the foundational knowledge while practical experiences enhance understanding by offering hands- on learning. The discussion on teacher training highlights that some courses provide clear relevance to future teaching, while others seem disconnected. The findings from poststructuralist and social constructionist theories suggest that the contrast between theory and practice can affect education quality. In the case of agricultural education, students gain practical experience through hands-on activities like farming, which teaches them essential skills such as ecological thinking and sustainable practices. This blend of theory and practice prepares them not only for academic success but also for real-world challenges, fostering a more holistic educational experience.

DOI: /10.61463/ijset.vol.12.issue5.264

Proactive approach to Enhancing Tunnel Safety: A Comprehensive Analysis of Risk Factors and Mitigation Strategies

Authors- Rajeev Prasad, Kailash Thakur

Abstract-In worldwide, Tunnelling is an essential component of modern infrastructure development, poses significant occupational health and safety risks and construction activities are rapidly growing in parallel with the increase in urbanization, industry, trade and transportation necessity in the world. This study aims to identify and analyse the primary hazards associated with tunnelling construction activities, including drilling-blasting and mechanized excavation methods. A thorough literature review and statistical data analysis were conducted to understand the prevailing risks and explore effective mitigation measures. The findings highlight the importance of implementing robust safety protocols, proper ventilation systems, regular inspections, and worker training to minimize accidents and ensure a safe working environment.

DOI: /10.61463/ijset.vol.12.issue5.265

College Placement Management System

Authors- Assistant Professor Ramya Kanagaraj, Moin Naik, Neeraj Kumar, Rafat Muskan Shaikh, Saquib Patel

Abstract-The College Placement Management System (CPMS) is an advanced, web-based application developed using the MERN stack – MongoDB, ExpressJS, ReactJS, and NodeJS. Aimed at revolutionizing the placement process within educational institutions. Designed to centralize and automate various placement activities, CPMS addresses the needs of three distinct user roles: Students, Placement Department Admin, and College Management Admin, offering tailored functionalities to each. Students can easily register and log in, explore and apply for job opportunities tailored to their qualifications, track the real-time status of their applications, access placement policies, and receive notifications regarding new openings and updates. The Placement Department Admin is empowered with robust tools for managing company profiles, creating detailed job postings, filtering and searching student data by various criteria, updating placement statuses, uploading offer letters, and generating comprehensive placement statistics and analytical reports to assess and improve placement performance. Meanwhile, the College Management Admin can access and analyse overall placement data, visualize trends and insights through interactive graphical representations, filter and review student details, monitor the effectiveness of the placement strategy, and track off-campus placement activities to ensure the institution’s broader placement goals are met. CPMS not only simplifies and accelerates the placement process but also enhances transparency, communication, and data-driven decision-making among all stakeholders, ensuring a seamless, efficient, and user-friendly experience that aligns with the strategic objectives of educational institutions.

DOI: /10.61463/ijset.vol.12.issue5.266

A Survey on Techniques of IOT Intrusion Detection and Feature Optimization

Authors- Shivani Meena, Professor Rani Kushwaha, Professor Jayshree Boaddh

Abstract-Internet of Things (IOT) brings flexibility and control in unfavorable conditions. IOT is adapted by various businesses to provide solutions for communication. But flexibility in computer networks increases the chance of attack. Hence security of data is highly desirable to build trust in the nodes, as many solutions collect data for research, monitoring purposes. This paper summarize sophisticated techniques, including machine learning, to detect potential threats at an early stage in the IOT network. The effectiveness of IDS is closely depends on input data so optimization of the features used for detecting intrusions. Techniques such as Principal Component Analysis (PCA), Genetic Algorithms (GA), and various dimensionality reduction methods are essential for improving detection accuracy while reducing computational demands. This paper has summarize various work done by the researcher to increase the network attack detection strength of the IOT.

A Survey on Cooperative Spectrum Sensing Techniques and Requirements

Authors- Swati Jat, Professor Rani Kushwaha, Professor Jayshree Boaddh

Abstract-The advent of cloud computing, powered by advancements in virtualization technologies, provides significant opportunities for cost-efficient hosting of virtual resources, eliminating the need for physical infrastructure ownership. Cloud data centers often employ a wide array of heterogeneous commodity servers to support numerous IoT devices with varying specifications and dynamic resource demands. This diversity can lead to imbalanced resource utilization across servers, potentially causing performance degradation and violations of service level agreements (SLAs). Edge computing has emerged as a viable solution to these challenges by redistributing workloads, thereby improving overall system performance. This paper discusses the necessity of edge computing, detailing its architectural framework. Additionally, it explores various load balancing techniques crucial for maintaining balanced resource utilization. The paper also reviews several models proposed by researchers aimed at enhancing edge network performance. Lastly, it outlines key evaluation metrics used to compare different load balancing models.

A Low-Power IoT Architecture for Real-Time Detection and Notification of Unauthorized Tree Poaching

Authors- Kapalik Khanal, Sudip Rana, Umesh Kanta Ghimire, Amul Neupane

Abstract-Unauthorized tree poaching is one of the major threats to several trees and herbs that have a significant ecological and/or economic value. Several trees such as Rosewood (Dalbergia spp.), Ebony (Diospyros spp.), Sandalwood (Santalum spp.), and Bodhichitta (Ziziphus budhensis) have a great economic value and are prone to poaching. Several conservational measures and legal approaches have been adopted over the years to stop such activities. However, traditional methods alone have been proven to be insufficient for conservation. Use of advanced technological solutions that include Internet of Things (IoT), Artificial Intelligence (AI), Remote Sensing, and geospatial data analysis can significantly enhance the effectiveness of conservation efforts. A low-power IoT system has been proposed in this paper that can be used for the detection and notification of illegal tree poaching in real time. The proposed system uses the ESP32-S3 as the main controller, along with the SX1278 LoRa transmission module and other electronic components. The system leverages several sensors that include vibration sensors that detect strange movements; microphones that detect the sound of axes or machinery tools; temperature sensors to detect burning or sudden temperature changes and a magnetometer to detect the metal objects. These components are selected based on their high efficiency, low power consumption and small dimensions, so the device can be easily concealed on the tree. The system was designed and implemented specifically for the conservation of Bodhichitta trees in the Timal region of Nepal.

DOI: /10.61463/ijset.vol.12.issue5.267

Agriculture and Sustainable Food Systems: An Integrated Approach for the Future

Authors- Payal Taver

Abstract-Sustainable agriculture and food systems are critical to addressing global challenges such as food security, environmental degradation, and climate change. This research examines the transition toward sustainable agricultural practices, analyzing both environmental and socio-economic impacts. The findings highlight the potential of agroecology, organic farming, and conservation agriculture to improve productivity while reducing negative environmental impacts, such as soil degradation and biodiversity loss. However, significant challenges remain, particularly in terms of scalability, market incentives, and policy support. The analysis of case studies reveals that regions with strong institutional frameworks and access to technology are better positioned to implement sustainable practices, while areas with limited resources face barriers to adoption. Additionally, this research identifies the critical role of global and local policies, market-based incentives, and community engagement in accelerating the shift towards sustainable food systems. The study concludes that a coordinated effort across governments, NGOs, and the private sector is essential to ensure the success of these practices on a larger scale, making sustainable agriculture a viable solution for the future of global food production. This research contributes to the growing body of knowledge on sustainable agriculture by offering practical insights and recommendations for policymakers and practitioners aiming to transition towards more resilient and environmentally-friendly food systems.

DOI: /10.61463/ijset.vol.12.issue5.268

ImmerseMe: Review of a VR Language Learning Tool

Authors- Oussema Dhieb

Abstract-This paper is a review of an online language learning tool called ImmerseMe. It opens with an overview of the technology involved such as Virtual Reality and Automated Speech Recognition, as well as the content available on the platform. Followingly, an evaluation is provided for the design, and it addresses methodological foundations, audience, and user experience accompanied with feedback and suggestions for improvement. In conclusion, ImmerseMe harnesses VR and ASR to offer realism and authenticity which are support language learning. Thanks to its rich content and its user-friendly interface, it can be integrated into language curriculums for learners with basic IT skills and various proficiency levels.

DOI: /10.61463/ijset.vol.12.issue5.269

Optimization of Portfolio using Markowitz’s Modern Portfolio Theory and Computational Techniques Using Python

Authors- Sandesh Ravindra Bhat

Abstract-This research paper applies Markowitz’s Modern Portfolio Theory (MPT) to optimize investment portfolios by achieving an optimal balance between risk and return through computational techniques. Utilizing Python, we develop and test a portfolio model using real-world stock data, focusing on mean-variance optimization. The model generates random portfolios with varying asset allocations and employs quadratic programming to explore the efficient frontier—a representation of the best possible risk-return trade-offs. Through this approach, we demonstrate how computational methods can enhance portfolio management by identifying portfolios that either minimize risk for a given return or maximize return for a given risk level.

DOI: /10.61463/ijset.vol.12.issue5.270

Etch Chamber Performance and Influence of Turbo Pump Conductance Path

Authors- Deepak Doddabelavangala Srikantaiah

Abstract-Etch Chamber which use Silane Nitride Seasoning during Process has a common issue of Drift in Chamber pressure over a period of time resulting in Chamber failure. Apart from that, there are issues like Chamber to Chamber matching. Normally, the solution for this issue is to increase the foreline temperature, but after detailed study of turbo and its internal feature, it was identified that the internal features of Turbo Pump is Critical. The effect of Turbo pump on chamber performance can be resolved by changing the turbo operating temperature and its testing method

DOI: /10.61463/ijset.vol.12.issue5.271

Effect of Irrigation Frequency and Mulching Materials on Soil Properties, Growth and Yield of French Beans under Drylands: Rwamagana-Nyirabidibili case

Authors- Bimenyimana Isaac, Dr. Adrien Turamyenyirijuru, Dr. Hamoud Rukangantambara, Rose Niyonkuru

Abstract-The study examined the effect of irrigation frequency and mulching materials on French beans’ soil properties, growth, and yield using a randomized block design with three blocks consisted of sixteen plots per block. Different Mulching materials and irrigation intervals were used and once watered as planned, 30mm of water (≈38.4 liters per plot) was added. After harvesting, soil samples were collected at 20cm depth and analyzed. On soil properties, the soil texture was sandy clay loam, and irrigation frequency, mulching material, and interaction did not significantly affect texture, aggregate stability, soil pH, CEC and Organic matter. The soil pH ranged between 5.1-5.5which is slightly acidic the soil CEC ranged between 40.2 to 51.7Cmol/kg, and organic matter ranged between 3.43 and 4.47% which is moderately mummified. The impact of irrigation frequency and mulching material on plant height, number of leaves, leaf length, number of pods, length of pod and pod width while the interaction become not significant with an increase from 6.32-31cm in 15 days after sawing, 7.77-33.11 cm after 21days of sawing and 9.21-41.56 cm after 40 days of sawing in plant height, with an increase from 4.67-10 leaves within 32 days after sawing and 6.56-12.56 leaves after 50 days of sawing in number of leaves with an increase from 4.11-10.89 cm at 21 day of sawing and 6.56-12.56 cm after 36 days of sawing in leaf length and an increase in pods number from 7.33-36.22 pods after 67 days of sawing, 5.50-14.67cm in pods length after 68 days of sawing and 1.33-2.71cm in pods width (diameter) after 65 days of sawing. Mulching material significantly affected the yield of French beans, with irrigation frequency having a significant impact and the interaction effect of irrigation frequency and mulching material significantly affect yield of French beans with an increase from 1.52-10.61 T/Ha. Therefore the Rwandan savanna region is largely plagued by drought, which can be mitigated through mulching and improved water usage efficiency.

DOI: /10.61463/ijset.vol.12.issue5.272

The Design and Analysis of circular Patch Antennas with Defective Ground Structures (DGS) for C and X-Band Applications

Authors- Puripanda Harini, Rajana Bharath Chandra, Valle saikumar, Yenimireddy Hemalatha, Vinod Pyla, Assistant Professor Nagandla Prasad

Abstract-The design and analysis of a small circular patch antenna with a defective ground structure (DGS) for C and X-band applications are presented in this study. The antenna has a s11 39.2 dB magnitude when it resonates at 8.52 GHz. We used the EM modeling tool CST-STUDIO to optimize the radiation parameters and developed the antenna on a 14 x 14 mm2 FR-4 dielectric. We used this tool to run many simulations. The antenna was built with a peak gain of 7.4 dBi at 7 GHz for both C-band (4–8 GHz) and X-band (8–12 GHz) communication systems. Wideband characteristics and high gain of the antenna for wireless communications within the designated frequency bands, radar systems, and satellite communication. Comprehensive modeling and experimental results are provided to assess the effectiveness of the antenna and emphasize its suitability in modern high-frequency communication systems.

DOI: /10.61463/ijset.vol.12.issue5.273

Cost-Benefit Analysis of Solar Power Implementation in off-Grid Regions

Authors- Assistant Manager Asadullah Muhammad Hossain Saad

Abstract-The purpose of this study is to investigate the economic viability and social impact of transitioning to renewable energy sources. This will be accomplished by conducting a cost-benefit analysis of the building of solar power systems in places that are not connected to the mainstream power grid. More specifically, the consequences of making the conversion to solar power will be the primary focus of the study. The research takes into account both the initial investment costs (panels, batteries, inverters, installation) and the ongoing expenses (maintenance, replacement) that are linked with the consumption of solar electricity. These costs are taken into consideration. The benefits that have been examined include increased access to energy, decreased reliance on fossil fuels, the creation of jobs, improvements in education and healthcare, and enhanced chances for economic development. These benefits have been analyzed. Additionally, an analysis of the advantages has been carried out. The primary focus of this investigation is on the unique difficulties and components that are associated with areas that are not connected to the centralized power system. The remoteness of the place, the community’s involvement, and the system’s scalability are some of the characteristics that contribute to what makes this situation unique. The goal of this cost-benefit analysis (CBA) is to give decision-makers critical insights into the viability and overall worth of solar power projects in locations that are not provided with grid power. This is accomplished by assessing and contrasting the costs and advantages of the project. In the end, this will help the development of sustainable behaviors as well as the production of policy decisions that are informed by relevant information.

DOI: /10.61463/ijset.vol.12.issue5.274

Characterization of Flow Dynamics in a Highly Overexpanded CD Nozzle under Varying NPR

Authors- Vishwajith V

Abstract-The characterization of flow dynamics in highly overexpanded convergent-divergent (CD) nozzles under varying nozzle pressure ratios (NPR) is a crucial aspect in understanding shock-induced flow separation and its impact on flow dynamics. This study focuses on the behavior of shock waves in the divergent section of a CD nozzle when subjected to NPR variations, leading to complex flow separation phenomena. The research aims to investigate the variations in flow physics when the nozzle is subjected to NPR variations which causes highly overexpanded cases with shock induced flow separation. Additionally, the study provides insights into the relationships between NPR and key flow features, such as flow deviation location, shock structure, and Mach stem/disc strength. The results highlight that flow properties do not exhibit linear changes across NPR variations; certain cases show abrupt shifts in gradients, while others maintain slightly linear gradients. This comprehensive analysis offers a deeper understanding of the non-linearities in flow dynamics and the performance variations in highly overexpanded CD nozzles.

DOI: /10.61463/ijset.vol.12.issue5.275

Deepfake Detection in Medical Images

Authors- Professor Mr. R.A.Ghadage, Vrushali Anil Zinj, Priya Prashant Sarode

Abstract-The challenge of deepfake detection in medical imaging by leveraging the Mask R-CNN algorithm. Deepfakes, generated using advanced AI, manipulate images and videos, posing significant threats across industries, including healthcare. Altered medical images can lead to misdiagnoses, treatment delays, or inappropriate interventions, putting patients at risk. The ability to identify such manipulations is critical for maintaining trust in medical diagnoses. Hospitals relying on compromised data may experience disruptions, financial losses, and legal complications. This project aims to develop an efficient deep learning-based system to detect these synthetic alterations. By using the Mask R-CNN framework, the proposed solution seeks to accurately locate and flag tampered regions within medical images. The model enhances patient safety by ensuring the reliability of diagnostic data. Ultimately, this approach offers a safeguard for healthcare institutions against the dangers posed by deepfake technology.

DOI: /10.61463/ijset.vol.12.issue5.276

AI and Ml Application in Civil Engineering

Authors- Ram Dhiraj Baniya

Abstract-Artificial Intelligence (AI) and machine learning (ML) have revolutionized civil engineering by transforming traditional processes into new data-driven applications. This article explores various applications of AI and machine learning in civil engineering, focusing on their contributions to healthcare infrastructure, geotechnical analysis, management construction and sustainable infrastructure. AI increases design accuracy, optimizes resources, and facilitates real-time monitoring of healthcare infrastructure by leveraging advanced algorithms and predictive modeling. The integration of AI with Building Information Modeling (BIM), and the emergence of smart technology are paving the way for more efficient and effective construction. Furthermore, the use of machine learning techniques in predicting soil behavior and estimating project costs has shown to play a significant role in reducing risk and improving decision-making. Despite promising progress, challenges remain, such as insufficient data, integration with existing projects, and the need for standardization. This article focuses on the future directions of AI and machine learning in civil engineering and highlights the potential of today’s intelligent and leadership-based approaches.

DOI: /10.61463/ijset.vol.12.issue5.277

Enterprise-Wide Data Security Strategies for Big Data Analytics

Authors- Ramla Suhra

Abstract-With the technological advancement in this era, data is growing at the pace more than ever, so does the value of data in decision making. When data driven decision making has become the strategic priority, organizations want to do collect the data which includes personal information which can help them make better decisions through. Customers on the other end are concerned about the security of their personal information and habits. Moreover, government has started strengthening the regulations on data management. Companies are now forced to be responsible and transparent about their data practices. Hence data privacy has become a crucial part of business operation. The whitepaper reviews the common areas to addressed while dealing with secure data to ensure that the data is protected and compliant. There is also an analysis on the common the risks faced by organizations while storing and processing big data with personal information for further analytics. The paper also explores options to mitigate the risks and suggests opportunities to improve in future. There are also attempts to share some business use cases and examples that can be referred by the readers interested in this topic.

DOI: /10.61463/ijset.vol.12.issue5.278

Management Involvement and Performance of Cloud Computing in Commercial Banks in Nairobi City County, Kenya

Authors- Dennis Kipkemboi Kosgei

Abstract-This Study Examines the Influence Of Management Involvement On Cloud Computing Performance In Commercial Banks Located In Nairobi City County, Kenya. The Research Explores The Roles Of Institutional Policies, Resource Allocation, Organizational Structure, And Strategic Leadership. Using A Descriptive Research Design, Data Were Collected From 40 Licensed Commercial Banks And Analyzed Using Descriptive Statistics And Regression Models. The Findings Show That Strategic Leadership Enhances Cloud Computing Performance Through Innovation, Operational Efficiency, And Effective Strategy Execution. Furthermore, The Study Highlights The Importance Of Aligning Institutional Policies And Resource Allocation To Optimize Cloud Computing Outcomes. It Recommends Prioritizing Leadership Development And Digital Transformation Strategies Within Banking Institutions. Future Research Should Explore Cloud Computing Leadership Across Different Sectors, Particularly In Emerging Economies.

DOI: /10.61463/ijset.vol.12.issue5.279

Design of Area Efficient, Low Power, High Speed 8-Bit Array Multipliers by Using Transmission Gate Logic

Authors- P.G Scholor Seera Reshma, Assistant Professor Velaga Suryakala/span>

Abstract-The multiplier is the most basic unit of an arithmetic circuit which is predominantly used in digital processing units and several integrated circuits. The efficiency of a processing unit is measured by its speed and power consumption. The multiplier circuit involves an extensive use of adders that generally add to its hardware complexity and thus is a major bottleneck to fast processing and also consumes high power. Thus it becomes critical to improve speed and reduce power consumption in the multiplier module. The conventional multipliers implemented using the CMOS and Transmission Gate (TG) technologies and their combination versions, albeit showing improved speed and low power consumption, still suffer from high hardware complexity. This project proposes the design of an 8-bit Array multiplier. The key idea here is to use the power efficient TG technology based 1-bit hybrid full adder within the popularly used array and Wallace tree multipliers to obtain a new multiplier design with fewer transistors and full output voltage swing. The proposed designs are implemented using Tanner EDA with 90nm technology and simulation results show substantial improvement when compared with the state of the art.

DOI: /10.61463/ijset.vol.12.issue5.280

Decoder and Multiplexer Comparison with Mixed Logic and CMOS Logic with 90nm Technology

Authors- P.G Scholor Menda Srikanth, Assistant Professor P. S. N Bhaskar

Abstract-Mixed logic designs take prioritized place in logic design approaches which will give a simplified mechanism for the analysis of digital circuits. Also, a mixed logic implementation gives clear idea with regards to the activity of a circuit. Here in this, introduced mixed logic designs like pass transistor dual value logic (DVL), transmission gate logic (TGL), static CMOS. By using CMOS technology, it requires 20 transistors to design 2:4-line decoder but by using mixed logic we can design the same 2:4-line decoder with the use of 14transistors (14T) only. Introducing mixed logic approach a 4:1 MUX was designed by using 2:4-line decoder of mixed logic design. This new approach gives the better operating speed, low power consumption compared to conventional logic design by reducing the transistors activity and simulations are carried out using tanner EDA tools.

DOI: /10.61463/ijset.vol.12.issue5.281

Design of an Energy-Efficient Approximate 4:2 Compressor by Using Multiplexer Approximate Adder

Authors- P.G Scholor Redrowthu Sujin Anil Kumar, Assistant Professor C Rama Krishna Gayatri, Assistant Professor A Swapna

Abstract-An adder is the basic computational circuit in digital Very Large Scale Integration design. Approximate Adders have been proposed to improve the design metrics of an adder. A multiplexer makes it possible for several input signals to share one device or resource. In this paper 4:2 compressors are designed by using various approximate full adder is proposed for low power. The MAA architecture is build using the inverter-based multiplexer which gives high power efficiency than the other adders, for performance evaluation four types of approximate mirror adders have been referred. On analysis it is found that Multiplexer based approximate full adder is the most power efficient with a considerable propagation delay.

DOI: /10.61463/ijset.vol.12.issue5.282

Estimating DNA Degradation Levels Using Machine Learning: An Innovative Approach in Forensic Science

Authors- Assistant Professor Dr. Pankaj Malik, Ustat Kaur Khanuja, Priyanshi Laddha, Poorva Jain, Mahima jain

Abstract-DNA evidence plays a crucial role in forensic science, yet its reliability can be compromised due to degradation caused by environmental factors and the passage of time. Traditional methods for assessing DNA degradation often involve labor-intensive and time-consuming techniques, which may not provide accurate results under varied conditions. This study explores the application of machine learning to predict DNA degradation levels from a dataset comprising samples subjected to different environmental stressors. Various algorithms, including Random Forest, Support Vector Machines, and Neural Networks, were evaluated for their effectiveness in estimating degradation levels based on input features such as temperature, humidity, and exposure duration. The results demonstrated that machine learning models can significantly enhance the accuracy of DNA degradation estimation compared to conventional methods. By employing metrics such as precision, recall, and mean squared error, our findings indicate that machine learning not only offers a reliable alternative for DNA analysis but also presents a scalable solution for forensic investigations. This research underscores the potential of integrating advanced computational techniques in forensic science to improve the assessment of critical evidence.

DOI: /10.61463/ijset.vol.12.issue5.283

Modèle Urbain Et Architectural Pour Reconstruire Tirknit Au Maroc En Alliant Héritage Agraire Et Résilience Parasismique

Authors- Manal El Faroki, Kaouthar Zair, Mouldi Chaabani

Abstract-Le séisme dévastateur de 2023, qui a touché les villages montagnards du Haut Atlas Marocain, a laissé des douars en ruine en détruisant les habitations traditionnelles qui reflètent des valeurs d’usages spécifiques. A travers ce travail qui a été élaboré dans le cadre d’un mémoire d’architecture à l’école nationale d’architecture et d’urbanisme de Tunis, nous avons proposé de reconstruire le douar Tirknit à travers une approche architecturale et urbaine respectant les normes parasismiques, tout en préservant l’identité culturelle de la communauté berbère qui y vive où tradition et modernité se rencontrent dans son mode de vie actuel.

DOI: /10.61463/ijset.vol.12.issue5.284

Improving the Performance of Asphalt Mixtures Using Nano Silica

Authors- Simpi Kumari, Professor Rajesh Kumar Misra, Professor Dr. Abhay Kumar Jha

Abstract-Black-top asphalts face challenges, particularly cracking and rutting. These issues arise within the asphalts due to deficiencies in mix properties and increasing traffic loads. They occur as a result of insufficient black-top coverage and blend quality. This paper focuses on enhancing the qualities of black-top mixes using Nano Silica (NS). The research involves analyzing the properties of modified mixes with NS at concentrations of 3%, 5%, 7%, 9%, and 11% by weight of bitumen. Marshall tests, retained Marshall tests, Indirect Tensile Strength (ITS) tests, as well as penetration and softening point tests are used to evaluate the properties obtained. Results indicate that the optimal NS content is 7% by weight of bitumen. Modifying the black-top mix with 7% NS increases the Marshall strength by 32.42%, decreases penetration by 7.93%, and raises the softening point by 10.34%, while maintaining similar unit weight and keeping air voids and other mix properties within acceptable limits. Overall, the addition of NS successfully enhances the characteristics of black-top mixes.

DOI: /10.61463/ijset.vol.12.issue5.285

Performance Analogy of Multilevel Inverter with Flipped Sine PWM

Authors- Shara Kandula, Professor Dr.G.Annapurna

Abstract-Multi-level inverters had the potential of producing better outputs, reduce harmonics in output. They find applications in various fields like motor drives, solar energy systems, and adjustable speed drives. Among different types, cascaded multilevel inverters consists of two DC sources are commonly used in medium to high power needs. This paper compares two control strategies, Sine PWM and flipped Sine PWM, for a cascaded H-Bridge MLI. Inverted/Flipped Sine PWM(ISPWM/FSPWM) technique provides better performance when compared to Sine PWM (SPWM). FSPWM significantly lower the total harmonic distortion (THD) and switching losses compared to traditional SPWM. This paper conducts simulations on three-phase three and five level cascaded H-bridge inverters employing both FSPWM and SPWM control strategies through MATLAB/SIMULINK software.

DOI: /10.61463/ijset.vol.12.issue5.286

Minimization of Real Power Losses with Facts with the Application Hybrid MGA and IGWO Optimization Techniques

Authors- J. Sutter, J. Nderu, A. Mutegi

Abstract-This paper work presents a novel individual and Hybrid MGA and IGWO was utilized to develop FACTS-controlled optimization model for reduction of reactive power losses. The algorithm simultaneously solved the objective problem and tunes as it searches for FACTS location and sizes. Objective function constrained optimal power flow (CPF) with FACTS devices for TTC within real and reactive power generation limits, voltage limits, line flow limits, and FACTS devices operation limits. Thyristor-Controlled Series Capacitor (TCSC) parameters has been optimized for the research and the work has been successfully carried on MATLAB platform using IEEE 30-bus test bus systems. Power system processes and parameters can be optimized using artificial intelligence techniques like artificial neural networks and genetic algorithm alongside power electronics based Flexible AC Transmission Systems (FACTS) devices. FACTS normalize voltage or control the power that is either added into or absorbed from the system. They enhance the overall grid capacity and performance. They also increase the dependability and efficiency of power systems. Apart from alleviating power transients, FACTS provide greater system real losses control in interconnected power systems

DOI: /10.61463/ijset.vol.12.issue5.287

Ecological Consequences of Fluoride-Induced Reproductive Changes in Fish Populations

Authors- Assistant Professor Dr. Rajesh Verma, Research Scholar Shravani Verma

Abstract-Aquatic fluoride contamination is rising, endangering fish and the food chain. High fluoride levels may damage fish endocrine functions, producing reproductive issues, poor fertility, and developmental defects. Reproduction may impact fish population dynamics, species composition, and ecological health. Fluoride’s ecological impacts on fish reproduction represent the intricate interactions between individual physiological changes and community- level consequences. Fluoride-induced reproductive deficiencies may skew sex ratios, delay maturity, and diminish spawning success, lowering fish population resilience. Demographic changes in fish populations effect predator-prey interactions, resource competition, and habitat usage. Many fish species rely on zooplankton and benthic invertebrates, which may drop with fish population density. Trophic imbalances affect energy and nutrient flow. Outcompetement or predation by fluoride contamination may reduce fish biodiversity and homogenize the population. The loss of keystone species or those with specialized ecological services may intensify these effects and destabilize ecosystems. The necessity for rigorous environmental regulations to limit industrial, agricultural, and residential fluoride emissions. Bioremediation, artificial wetlands, and advanced water treatment are required to minimize fluoride. Fish populations and ecological services including water purification, flood control, and habitat for diverse flora and animals are preserved by protecting aquatic ecosystems against fluoride poisoning. To prevent fluoride pollution and safeguard aquatic biodiversity and ecosystem function via proactive monitoring and management.

DOI: /10.61463/ijset.vol.12.issue5.288

Intensification of Production of Dry Flour from Roots and Tubers with High Content of Biologically Active Substances

Authors- Malkhazi Mikaberidze

Abstract-The article is devoted to the intensification of the production of dried food additives using the electrophysical method from root and tuber raw materials with the provision of a machine hardware system. A new technological scheme for the production of dried food addtives with high biological activity has been developed; The optimal regime parameters for heat treatment of crushed root and tuber mixture in the field of infrared rays were determined; the main geometric parameters of a drying machine operating on infrared energy were calculated, the energy balance was determined, the main geometric parameters and design diagrams were clarified. A machine for drying a root and tuber mixture using infrared rays is a simple device; occpies a small production area; does not require additional equipment. The process savings (economic effect) when using the experimental machine will be 16,849 US dollars or 0,39 US dollars per 1 kg of product.

Deep Learning Techniques for Enhanced Violence Detection in Surveillance Systems

Authors- M. Tech Scholar Dhirendra Tripathi, HoD Nagendra Patel

Abstract-In the field of video content analysis, accurately distinguishing between ‘Violence’ and ‘NonViolence’ presents a significant challenge due to the dynamic and complex nature of video data. While traditional models like ResNet50 and MobileNetV2 have demonstrated strong performance in image classification, they often fall short in handling the temporal dependencies present in video sequences. To overcome this limitation, we propose a hybrid deep learning approach that leverages the spatial feature extraction capabilities of InceptionV3 along with the temporal pattern recognition strengths of Long Short-Term Memory (LSTM) networks. Our method begins with rigorous data preprocessing, which includes noise reduction and effective feature extraction. This is followed by a systematic model training process that optimally combines the features extracted by InceptionV3 with LSTM’s sequence modeling capabilities. Performance evaluations indicate an impressive accuracy of 99.86% and a validation accuracy of 92.48%, significantly outperforming the other models tested. These results not only affirm the effectiveness of the hybrid model in video classification tasks but also highlight its potential for broader real-world applications that require nuanced content analysis.

Enhancing Friend Recommendations in Social Media Networks

Authors- M. Tech Scholar Vipin Kumar Singh, HoD Nagendra Patel

Abstract-Friend recommendation is a key feature of social networking platforms, designed to connect users with similar or familiar individuals. This concept, popularized by social networks like Twitter and Facebook, often employs a “friends-of-friends” approach, where users are introduced to connections within their extended social circles. Users generally don’t connect with random individuals; rather, they form connections within their friends’ networks. However, existing recommendation methods are limited in scope and lack efficiency. To address these shortcomings, we propose a new friend recommendation model that enhances accuracy and breadth by utilizing collaborative filtering. This approach allows us to analyze similarities and differences in users’ preferences, activities, and interests to deliver more relevant user-to-user suggestions. Additionally, location-based friend recommendation systems are gaining popularity as they bridge the digital and physical worlds, providing a deeper understanding of user interests and preferences. Our model aims to broaden recommendation options by connecting users with shared interests and nearby locations, creating a more meaningful social experience.

Analysis of Actual Problems of Laser Welding of Stainless Steel Thin Sheets and Search for Solutions

Authors- Yu.V.Yurchenko, A.V.Bernatskyi, O.V. Siora, M.V. Sokolovskyi, V.I. Bondarieva

Abstract-Over the past decade, there has been a rapid development of laser technologies, in particular laser welding, which has become more cost-effective due to a nearly 10-fold reduction in the cost per kilowatt of laser power. The global market for laser welding equipment is estimated at USD 2.5-2.9 billion in 2022-2023, with a projected growth to USD 4 billion by 2032. At the same time, laser welding is only gaining popularity in Ukraine. Therefore, the authors reviewed modern scientific works on laser welding of thin-walled products made of high-alloy corrosion-resistant steels to identify the main problems of welding such products. The review identified the main problems of laser welding of thin-walled products, such as: joining the edges of welded parts, clamping of welded parts, ensuring constant uniform heat removal, providing gas protection of the weld pool and cooling metal of the welded joint from interaction with the surrounding atmosphere, and forming a weld “on the hinge” and on the substrate. Based on the known problems, technological equipment in the form of a clamp and a gas protection system was developed to prepare test welded joints made of thin-walled corrosion-resistant steels in accordance with EN ISO 15614-11:2015. This will improve the quality and level of operational and functional properties of the resulting welded joints and develop technological recommendations for the manufacture of thin-walled products for various industries, taking into account the relevant operational requirements.

DOI: /10.61463/ijset.vol.12.issue5.289

Assessing Data Security of Patient Health Information in EHMS at Kenyan Public Hospitals

Authors- Margaret Afwande, Jane Kabo, Samuel Barasa

Abstract-This study explores the cybersecurity vulnerabilities of Electronic Health Management Systems (EHMS) in Kenya’s public hospitals, revealing significant shortcomings in the protection of sensitive patient health information. Despite the widespread implementation of EHMS, findings indicate that existing security measures are insufficient to address the escalating cyber threats faced by healthcare institutions. Notably, 78% of hospitals rely solely on username and password authentication, while only 34% have adopted multi-factor authentication (MFA), leaving systems exposed to unauthorized access. Furthermore, just 41% of hospitals encrypt data at rest, highlighting a critical gap in data security. The research identifies alarming trends in unauthorized access incidents, with 60% of respondents reporting such breaches primarily due to weak password practices and a lack of staff training in cybersecurity. Additionally, 80% of respondents cite chronic underfunding as a significant barrier to improving EHMS security. The shortage of skilled IT personnel (68%) and inadequate cybersecurity training for healthcare staff (72%) further exacerbate these vulnerabilities, increasing the risk of data breaches and ransomware attacks. To mitigate these challenges, the study recommends adopting a proactive cybersecurity strategy focused on implementing MFA, comprehensive data encryption, and regular system audits. Furthermore, investment in capacity-building initiatives for IT professionals is essential to strengthen the cybersecurity framework within public hospitals. Establishing a national framework for data security is also crucial for standardizing practices and enhancing patient data protection across Kenya’s healthcare sector. Overall, these measures aim to address vulnerabilities and ensure the integrity of sensitive health information in an increasingly digital landscape.

DOI: /10.61463/ijset.vol.12.issue5.290

A Comparative Study of AI Driven UX/UI Vs Traditional UX/UI in E-Commerce Websites and Platforms

Authors- Bariya Ansari, Dr. Rakhi Gupta, Assistant professor Nashrah Gowalkar

Abstract-This research conducts an inclusive comparative study between AI-driven and traditional UX/UI design approaches in e-commerce websites and platforms. It evaluates key performance indicators such as user efficiency, effectiveness, and overall satisfaction across various demographics, including gender, age, and geographic location. By examining real-world daily usage patterns, the study aims to highlight the strengths and weaknesses of each design approach. By analyzing user interactions, the research aims to determine which design approach—AI-driven or traditional—offers a more preferred/ better experience and to identify which method is more likely to enhance user experience. This research claims that a significant amount of youngsters prefer AI Driven UX/UI designs although very close in numbers, the number of people choosing Traditional UX/UI designs is also not low. The age group choosing Traditional UX/UI falls under the category of 31-60. Although preference has declared AI Driven UX/UI designs to be more likely to be chosen, the use of Traditional UX/UI designs still hold significance, especially among older age groups and female respondents, which highlights the importance of a balanced approach.

DOI: /10.61463/ijset.vol.12.issue5.291

Combustion Characteristics of Briquettes Produced from Saw Dust and Kurumthotti Herbal

Authors- Assistant Professor M.Prabhu, Assistant Professor Dr. C. Dharmaraja, Assistant Professor Dr.M.Vijayakumar

Abstract-Briquette density is the primary criterion used to assess briquette quality. From the standpoint of manipulation, burning rate, briquette longevity, etc., briquette density is crucial. The current study examines the physical and combustion properties of Sawdust and Kurumthotti herbal sticks, including their length, diameter, mass, density, moisture content, total ash content, fixed carbon, volatile matter, and gross calorific values. Theoretical assessments of the variables that affect the quality of the briquettes were carried out throughout our investigation. The density and strength characteristics of the produced briquette were ascertained in order to assess its quality.

DOI: /10.61463/ijset.vol.12.issue5.292

Biomass Briquettes: A Sustainable Solution to Control Rice Stubble Burning in India

Authors- Mohit Kumar, Dr. Gurshaminder Singh

Abstract-This paper reviews the role of biomass briquettes in addressing the pressing issue of rice stubble burning in India, particularly in states like Punjab, Haryana, and Uttar Pradesh. The practice of stubble burning, driven by economic and time constraints, contributes significantly to air pollution, soil degradation, and greenhouse gas emissions. Biomass briquetting presents a promising, environmentally friendly, and economically viable alternative for managing crop residues, converting agricultural waste into useful fuel. This paper explores the challenges of stubble burning, the benefits and process of biomass briquetting, and government initiatives aimed at promoting sustainable agricultural practices. The review concludes with recommendations to encourage the adoption of biomass briquettes, with implications for environmental sustainability, public health, and rural development.

DOI: /10.61463/ijset.vol.12.issue5.293

Integral Time Counter Simulations of GARCH for Option Pricing

Authors- Bitakwate Jackila Eliot

Abstract-This paper displays derivation of the univariate and multivariate GARCH(1,1) model. The Stochastic Discount Factor and conditional Esscher transform were used as techniques to de- rive the models. The matrix discretized form of the multivariate equation is also displayed in this paper with a proposed error correction in the normalized and variance matrices. Standard as- sumptions on the parameter were critically assigned and set to ensure convergences and stability for the models. The simulations in the risk-neutral processes shown approximate values as Mont Carlo simulations in Duan (2000) with values of the standard deviation ranging from [0.0999 , 0.5623] and payoff European call options [0.0000, 1.1692]. Using python and R programming tools, simulations showed that the risk neutral processes and the multivariate GARCH(1,1) can be used to predict returns and even price derivatives.

DOI: /10.61463/ijset.vol.12.issue5.294

Colubrid Snakes of Collin County, Texas

Authors- Jerrod G. Tynes

Abstract-zones and has recently become one of the fastest growing residential areas in northeast Texas. This area wide variety of snake species including those of the Colubridae family. This group is known for its ecological variety, and many of these species play vital roles in controlling pest populations. Through a comprehensive look a field guides, a review of historical records, and local photographs we catalog the various species of Colubrids that inhabit Collin County, examining their natural history, appearance, habitat preferences, and behavioral patterns. This review identifies several key species, including the Eastern Yellow-Bellied Racer (Coluber constrictor flaviventris), Texas Rat Snake (Pantherophis obsoletus), the Blotched Watersnake (Nerodia erthrogaster transversa) and the Rough Green Snake (Opheodrys aestivus), among others. Particular attention is given to their physical characteristics for clarity in identification. The paper also highlights conservation concerns, such as habitat destruction and the impact of urbanization on these snakes, as Collin County undergoes rapid development. It is our goal that this research will contribute to the understanding of Colubrid biodiversity in North Texas and provide insights into an understanding of the ecological roles of each species and various conservation challenges they face. By showcasing a detailed inventory of the local Texas Colubrid species, this paper serves as a tool for wildlife enthusiasts, conservationists, researchers, and the general public as it emphasizes the importance of preserving the unique herpetofauna of the area amidst residential expansion.

DOI: /10.61463/ijset.vol.12.issue5.296

Optimizing Space Exploration: A Comprehensive Analysis of AI Integration in Rocket Launch and Landing Systems

Authors- Md Suzon Islam

Abstract-Space exploration has advanced much further chief of all advancements made in rocketry. AI is important in the rocket’s launch and landing in that they are able to improve on the existing systems. This paper provides a critical discussion on the incorporation of AI technologies into these systems with reference to effectiveness, safety, and mission accomplishment. Algorithms become an efficient way to assist decisions in launch phases, to specify trajectories and to tweak auto-landing systems. Modern complex systems can be analyzed by means of machine learning and neural networks, resulting in improved prediction of system health and less maintenance failures. In addition, I believe that it is crucial that AI is used in controlling reusability of rockets so as to remove human factor and at the same time bring in efficiency. The audit shows that AI-powered algorithms have enhanced the launch accuracy up to 15% and minimized system failures up to 10%. AI based autonomous landing systems have reported a 20% improvement in the accuracy of landing thereby reducing lifecycle risks and improving the reusability of rocket stages. It has been marked that integration of AI for diagnosing problems results in 25% overall system reliability therefore the study supports the statement. It has also enhanced the optimization of fuel, which is critical for mission sustainability, through a 12% improvement on fuel efficacies.

DOI: /10.61463/ijset.vol.12.issue5.297

Fake Job Post Detection Website

Authors- Sakshi Samvat, Harshita Patil, Muskan Yadav, Poornima Dubey, Professor Shivangi Sharma

Abstract-The proliferation of fake job postings on online job boards and career websites has become a significant concern, resulting in financial losses and reputational damage for job seekers and employers alike. To combat this issue, we propose JOBSHIELD, a machine learning-based fake job post detection system that leverages natural language processing and ensemble techniques to identify and flag suspicious job postings. JOBSHIELD’s detection algorithm is trained on a large dataset of labelled job postings, featuring a range of features extracted from job descriptions, requirements, and company information. Our system achieves an accuracy of 95% in detecting fake job postings, outperforming existing approaches. The JOBSHIELD website provides a user-friendly interface for job seekers to search for job postings and receive alerts on potential fake job postings. Employers can also utilize our system to verify the authenticity of job postings and protect their brand reputation. By providing a reliable and efficient fake job post detection system, JOBSHIELD aims to promote a safer and more trustworthy online job market, empowering job seekers to make informed decisions and employers to maintain their reputation.

Machine Learning-Driven Anomaly Detection for Quality Assurance in Recycled Fiber Supply Chains

Authors- Assistant Professor Dr. Pankaj Malik, Muskan Singh, Vasundhara Shukla, Tinkesh Barapatre, Vishesh Mishra

Abstract-In recent years, the demand for recycled fibers in textile manufacturing has surged as industries strive to adopt more sustainable practices. However, maintaining consistent quality in recycled fibers presents significant challenges due to variability in supply chain conditions. Anomalies in transportation, storage, and handling, such as temperature fluctuations and prolonged storage times, can negatively impact fiber quality and, ultimately, the quality of the final textile products. This paper proposes a machine learning-driven approach to anomaly detection across the recycled fiber supply chain, aiming to proactively identify and address quality risks. By leveraging data from environmental sensors, transportation records, and storage logs, various machine learning models—including isolation forests, deep autoencoders, and long short-term memory (LSTM) networks—are developed and evaluated for their effectiveness in detecting supply chain anomalies. Experimental results demonstrate the potential of these models to accurately identify anomalies and provide early warnings, which can inform quality control interventions before production. Case studies highlight specific anomaly scenarios, such as temperature spikes and excessive handling, which were successfully flagged by the models. The study underscores the value of machine learning for real-time quality assurance in recycled fiber supply chains, offering a pathway toward greater consistency and sustainability in textile production. This approach also lays the groundwork for future research integrating Internet of Things (IoT) devices and blockchain for enhanced traceability and accountability in sustainable textile supply chains.

DOI: /10.61463/ijset.vol.12.issue5.298

Polyp Detection Using U-Net Deep Learning Method

Authors- Mr. S. Sinimoxon Lee, Professor Arpita Das

Abstract-Polyp detection in colonoscopy images plays a crucial role in the early diagnosis and prevention of colorectal cancer. This project implements a U-Net model to segment polyp regions from colonoscopy frames using the CVC-ClinicDB dataset, which includes annotated ground truth masks. The U-Net architecture, with its encoder-decoder structure and skip connections, is particularly well-suited for medical image segmentation, enabling precise identification of polyp regions. The model’s performance is evaluated using Intersection over Union (IoU) to measure segmentation accuracy, alongside precision-recall curves to assess detection reliability. Training progress is monitored through the visualization of training and validation loss curves, as well as accuracy curves, ensuring the model’s effectiveness and generalization. The results demonstrate that the U-Net model can significantly improve the accuracy of polyp detection, contributing to more reliable colorectal cancer screening.

DOI: /10.61463/ijset.vol.12.issue5.299

Operational Risk Framework: A Comprehensive Overview

Authors- Jay Sampat

Abstract-This paper explores the concept of operational risk, its implications, and the importance of effective risk management. It delves into the key components of an operational risk management framework, including governance, risk identification, assessment, control, monitoring, and reporting. The paper also discusses the challenges and opportunities associated with implementing a robust risk management program in today’s complex business environment. By understanding and addressing operational risks, organizations can protect their assets, reputation, and long-term sustainability.

Emoji-Enhanced Sarcasm Detection: A Comparitive Study of Logistic Regression and LSTM Models

Authors- Aamina Atlaswala, Dr. Rakhi Gupta, Nashrah Gowalker

Abstract-Sarcasm is a form of language defined using words or phrases that express the opposite of their actual meaning, often with a purpose of mocking or criticizing something or someone. Sarcasm detection is crucial for mining, sentiment analysis, detecting cyberbullies, online trolls, and other similar activities. Detecting Sarcasm is a part of Sentimental Analysis. This research focuses on detecting sarcasm in text with emojis using the Logistic Regression Model, a traditional machine learning approach, and the Long Short- Term Memory (LSTM) Model a deep learning technique and provides a comparative study of models from the two branches of Artificial Intelligence. The emojis in the dataset were converted to their text descriptions to infer their actual usage in the text. And then the models were trained on the preprocessed data.

DOI: /10.61463/ijset.vol.12.issue5.300

Fabric Defect Detection and Classification Using Machine Learning

Authors- Assistant Professor Dr. Pankaj Malik, Parth Akhand, Vinayak Pandiya, Krapansh Dubey, Shashwat Kanungo

Abstract-In the textile industry, identifying and classifying fabric defects is critical for maintaining product quality and minimizing waste. Traditional methods of fabric inspection, typically involving manual inspection, are labor-intensive, subjective, and prone to error. This study explores the application of machine learning techniques to automate the detection and classification of fabric defects, aiming to improve accuracy, efficiency, and scalability in textile quality control. We focus on common fabric defects, including holes, stains, misweaves, and pattern irregularities, and evaluate several machine learning models, including Convolutional Neural Networks (CNNs) and Transfer Learning architectures such as ResNet and VGG, for their performance in defect detection. A labeled dataset of fabric images with various defect types was used to train and evaluate the models, with data augmentation techniques applied to enhance robustness. Key performance metrics, including accuracy, precision, recall, and F1-score, were used to assess model efficacy. Results indicate that CNN-based models, particularly those leveraging Transfer Learning, achieve high accuracy in detecting and classifying fabric defects, significantly outperforming traditional approaches. The findings underscore the potential of machine learning to transform textile quality management by enabling real-time, automated defect detection on production lines. This research demonstrates that machine learning-driven systems can improve the speed and consistency of fabric inspection processes, providing valuable insights for developing automated quality control solutions in the textile industry. Future work may explore real-time implementation and advanced architectures for further optimization.

DOI: /10.61463/ijset.vol.12.issue5.301

Ways to Improve Brain-Based Learning Methods at Indian Universities

Authors- Gaurav Thakur, Professor G.Anburaj

Abstract-In the rapidly changing educational arena, enhancing the learning methodology is much more censorious than ever; Indian universities, drenched in rich academic tradition, are finding it challenging to adapt to modern pedagogical approaches which guarantee deeper cognitive engagement and retention It elaborates on the brain-learning method, a neuroscience-informed approach to teaching and learning, and its potential to revolutionize higher education in India. The paper would draw copiously from the cognitive science and educational psychology literature in identifying pertinent techniques, such as active learning, spaced repetition, multisensory engagement, and reflective learning-everything that echoes how the brain actual processes and retains information. Analyzing case studies and comparative analyses with foreign universities, the paper will assess the application of such methods in Indian universities presently and suggest changes that have context applicability. In conclusion, the study considers cultural and infrastructural obstacles to full-scale implementation of using the brain-based strategy in learning and draws recommendations for educational practitioners, policymakers, and institutions in creating an environment more engaging and effective.

DOI: /10.61463/ijset.vol.12.issue5.302

Analyzing the Performance of 5G Network Compared to 4G in Mumbai Region

Authors- Madiha Ansari, Dr. Rakhi Gupta, Assistant Professor Nashrah Gowalker

Abstract-The deployment of the 5G network represents a significant step forward in mobile communications, providing better speeds, less latency and greater capacity compared to 4G. This study aims to compare the efficiencies of 5G and 4G networks in the densely populated city of Mumbai. The study investigates the essential performance indicators such as data transmission rates, latency, network reliability and size. Data were collected from multiple locations throughout Mumbai to measure performance during peak traffic hours and different environmental conditions. 5G delivers an average download and upload speed almost 10 times faster than 4G.5G’s latency has been significantly reduced.5G deployment in the region faces challenges arising from infrastructure development and spectrum availability issues.5G’s ability to improve user experience in urban environments and the necessary steps for widespread adoption are revealed through comparative analysis. This performance can be compared to telecommunications companies such as Jio, Airtel and Vodafone.

DOI: /10.61463/ijset.vol.12.issue5.303

Review of FPGA based Design of Low-Power Router

Authors- Putrevu Voshanavi, Yenni Ajay, Ponnana Triveni, Padala Sri Sai Nandu, Sanapathi V V S S Siddartha, Mohd Rizwan Uddin Shaikh

Abstract-The paper discusses an FPGA-based approach to designing routers for efficient data packet forwarding across networks. It introduces the router’s architecture, which uses Verilog HDL and Universal Verification Methodology (UVM) for design verification. The proposed design focuses on reducing power consumption while maintaining high performance through the use of synchronous FIFO buffers, Finite State Machines (FSM), and a register-based approach for packet handling. The router operates at the network layer, ensuring data integrity through error checking mechanisms such as parity verification. The survey also outlines a verification strategy involving UVM-based test benches and RTL linting to identify functional bugs early, ensuring a robust, low-power router design suitable for Network-on-Chip (NoC) platforms.

DOI: /10.61463/ijset.vol.12.issue5.304

Optimizing Space Exploration: A Comprehensive Analysis of AI Integration in Rocket Launch and Landing Systems

Authors- Md Suzon Islam

Abstract-Space exploration has advanced much further chief of all advancements made in rocketry. AI is important in the rocket’s launch and landing in that they are able to improve on the existing systems. This paper provides a critical discussion on the incorporation of AI technologies into these systems with reference to effectiveness, safety, and mission accomplishment. Algorithms become an efficient way to assist decisions in launch phases, to specify trajectories and to tweak auto-landing systems. Modern complex systems can be analyzed by means of machine learning and neural networks, resulting in improved prediction of system health and less maintenance failures. In addition, I believe that it is crucial that AI is used in controlling reusability of rockets so as to remove human factor and at the same time bring in efficiency. The audit shows that AI-powered algorithms have enhanced the launch accuracy up to 15% and minimized system failures up to 10%. AI based autonomous landing systems have reported a 20% improvement in the accuracy of landing thereby reducing lifecycle risks and improving the reusability of rocket stages. It has been marked that integration of AI for diagnosing problems results in 25% overall system reliability therefore the study supports the statement. It has also enhanced the optimization of fuel, which is critical for mission sustainability, through a 12% improvement on fuel efficacies.

DOI: /10.61463/ijset.vol.12.issue5.305

The Synthesis of Several 7H-Pyrrolo[2,3-d]Pyrimidin-4-amine Derivatives, Derived from Cyanobenzaldehyde, was Achieved Using Ultrasonic Waves

Authors- Sopan Tanaji Yashwantrao

Abstract-Benzaldehydes have been uswed as key intermediates in the synthesis of 7H-pyrrolo[2,3-d]pyrimidin-4-amine (PPA) derivatives. The structural identities of these newly synthesized compounds were thoroughly validated using a range of analytical techniques, including elemental analysis, infrared (IR) spectroscopy, nuclear magnetic resonance (NMR), and ultraviolet (UV) spectral data. To assess their biological activity, all of the synthesized compounds were subjected to in vitro testing to evaluate their cytotoxicity against Artemia salina. Furthermore, the antimicrobial potential of each compound was explored to determine its efficacy in inhibiting microbial growth. The antibacterial activity of these compounds was compared with that of known antibiotics, fluconazole and streptomycin, showing promising results. This study specifically emphasizes the antibacterial properties of these compounds and investigates the relationship between their chemical structure and biological activity, providing valuable insights into structure-activity relationships (SAR).

DOI: /10.61463/ijset.vol.12.issue5.388

Privacy-Preserving Cyber Forensic Analysis Using Encrypted Feature Vectors for Secure Investigations

Authors- Madhudhara.V, Sivapriya. R, Sudharshini. C, D. Suganthi, J Mythili, Dr. N. Prabhu

Abstract-Securing sensitive data while allowing forensic analysis is becoming more important as digital data use rises. Machine learning-based cyber forensic investigations rely on feature vectors, which quantify digital data. However, these vectors typically include important data that must be securely stored. This work uses complex cryptographic methods including AES, RSA, and homomorphic encryption to encrypt feature vectors. Cyber forensics analyse encrypted routes to find abnormalities, track digital footprints, and uncover security breaches while protecting data. This innovation improves cybersecurity by allowing secure investigations without losing secrecy using encrypted forensic analysis. Results show that encrypted feature vectors may be handled privacy-preserving, retaining data integrity and investigative accuracy. In cyber forensics, this method affects malware detection, intrusion detection, and safe digital evidence management.

DOI: /10.61463/ijset.vol.12.issue5.810

Anomaly Detection in IoT Networks Using Deep Learning and Data Mining

Authors- Professor Dr S Murali krishna

Abstract-The proliferation of Internet of Things (IoT) devices has dramatically transformed various sectors by facilitating real-time data collection and analysis. However, this rapid integration has also heightened security vulnerabilities, necessitating efficient anomaly detection methods to safeguard IoT networks. This research explores the application of deep learning techniques combined with data mining for robust anomaly detection in IoT environments. We propose a novel hybrid model, integrating deep neural networks (DNNs), autoencoders, and attention mechanisms to improve detection accuracy. Through extensive experimentation, we demonstrate that the proposed model excels in identifying deviations from normal behavior, achieving high precision and recall rates even under challenging conditions of data contamination. Our findings underscore the importance of deep learning’s ability to extract complex features from high-dimensional IoT data, thereby enhancing anomaly detection frameworks. The study also addresses the potential pitfalls of existing methods and offers a comprehensive comparison with state-of-the-art approaches. Ultimately, this research contributes significantly to the development of effective security measures in IoT networks, promoting resilience against emerging cyber threats while ensuring operational integrity.