Volume 12 Issue 6

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

The Role of Violent Video Games in the Lives of University Students

Authors- Mihir Nighojkar, Professor G. Anburaj

Abstract-The advancements in technology have impacted our lives in a variety of ways. Technology is used in almost all spheres of our daily life and the education sector is not an exception. The use of modern technology in teaching has made students’ lives easier and technology has undoubtedly helped to improve the quality of education. But technology is like a ‘double – edged sword’, though it has made education friendly and easy but it has created a lot of negative impacts on the lives of students. Over the past few decades, violent video games have gained immense popularity among students, raising concerns about their influence on students’ emotional and mental state, interpersonal relationships, social skills and their vision towards society. This study focuses on studying the influence of violent video games on the daily lives of university students, discussing about behavioural changes, effects on academic performance, and the gradual reduction in social interaction. Today, it is extremely essential to identify the possible reasons behind the addictive nature of these games and help the addicted students to get rid of this habit. Later, the key findings of this paper strongly emphasize the urgent need for educational institutions and parents to restrict the exposure of young kids to these violent video games and suggest some ways that can aid in curbing this addiction among youngsters.

DOI: /10.61463/ijset.vol.12.issue6.306

Examining the Efficacy of Project Based Learning in University Level Education

Authors- Yuvaraj Hiremath, Assistant Professor G. Anburaj

Abstract-This research explores the integration of Project-Based Learning in higher education with implications on involvement, applicability in real-life conditions, collaborative work, and critical thinking. Analysis shows that in terms of involvement, students are engrossed more than any lecture. More and better learning about realistic scenarios is established and, therefore, more active and hands-on learning experience. While considering the activities of PBL, the students claimed to have had much more interest and willingness and, due to which it has the opportunity to put theoretical knowledge into real action.There has been a finding that collaborative projects improve peer interactions and communication skills although some complained about repetitive tasks during it. However, in that situation, projects should be designed in diversification so that the interest is not dwindled among them with the passage of time. Besides that, PBL having proven value as a preparation tool for the real world application still creates voids to ensure that the projects align with industry standards, and that signals the possible need for stronger collaborations between academia and industry. And at this stage, the study further concludes mixed perceptions over developing skills critical skills where some perceived PBL as less effective than traditional methods. The variations in workload perception also reflect the essential necessity in project demands to be manageable. The study further urges potential research studies in the long-term impacts of PBL and how there is interdependence between project diversity and engagement, as well as educators being trained in implementing PBL. PBL is, in general, perceived as a really powerful and innovative approach to education but still requires continuous improvement for the highest possible benefits as well as better preparation of students for their future careers.

DOI: /10.61463/ijset.vol.12.issue6.307

Examining the Role of Student-Faculty Interaction in Determining Course Completion and Graduation

Authors- T.M.B Cidambaram, Assistant Professor G. Anburaj

Abstract-This study investigates the impact of student-faculty interaction on student course Completion and graduation. Past studies have shown that positive engagement between students and their faculty members significantly contributes to their academic success, retention, and college experience. In fact, such interactions with faculty members provide students with support, guidance, and a sense of belonging necessary for maintaining motivation and overcoming academic challenges. Using data from multiple higher education institutions, this research study explores how mentorship, access to faculty, and the quality of faculty feedback influence the likelihood that students will complete courses and degrees. Variation in interaction effects by student demographic groups such as first-generation college students and those from underrepresented backgrounds is examined to understand how such interactions might help close gaps in achievement. The general indication is that the experience fosters both academic self-confidence and student persistence on an ongoing basis, again depending somewhat on how the interaction addresses the special challenges each has. These findings relate specifically to improving institutional settings toward faculty access and programs aimed at increasing support and supportiveness in their communities in hopes of fostering more fruitful faculty relationships with students to result in better retention and higher graduation rates.

DOI: /10.61463/ijset.vol.12.issue6.308

Closed-Loop Control Systems in Automobiles

Authors- Mazin Mohamed Adam Ibrahim

Abstract-Control is the process of maintaining working conditions inside a prescribed region around a predetermined set point, with a required degree of accuracy and speed. In most control systems, it is necessary to compare the difference of the controlled variable from the set point value (the actual value of the controlled system has been constantly monitored from the set point). Thus, the need to know the change of the controlled system in respect to time, i.e., the differential equation of the control system, is essential. Generally, it may be the first order, second order, or even the nth order system; however, the modelling of the control system has to be determined before one could synthesize an effective control. In the automobile industry, most of the controlled systems used are closed-loop systems, in which the difference between the actuating input and the output of the control system is minimized by feedback control. The feedback control will determine the error between the actual system and the set point, and based on this error, it will try to minimize the impact of disturbance over the system by feeding the output back and comparing the same to the set point. Once the difference has been minimized, the controlled system is said to be stabilized. For any application of the control system in the automobile industry, the following desired characteristics are essential.

DOI: /10.61463/ijset.vol.12.issue6.309

How Career Counselling Can Influence the Lives of University Learners

Authors- Uganeshwaran, Assistant Professor G. Anburaj

Abstract-B.The university students face the challenges regarding the decision making about their future and that is where career counselling services intervene by advising them on how best to approach their academic as well as professional aspirations. This document discusses the aspects of career counselling and its effects on university students’ overall function. Aspects like career clarity, job preparation, and career satisfaction for a duration of several years in the field has been focused on. Based on the literature review as well as the investigation of the developments, the research recognized the spheres which career counselling aids most encourage positive achievement, such as improvement of skills, mental health and career after studies. In addition, it also pointed out some limitations in the current research like the need for further time frame studies, effectiveness of online counselling services and segregation of the counselling service to different types of students. This study seeks to understand how career counselling can be relevantly improved in order to meet the emerging demands of university students. The study draws conclusions that can be used to improve career support services and promote better career outcomes for students.

DOI: /10.61463/ijset.vol.12.issue6.310

The Impact of Class Size on Learning

Authors- S. Kawinganesh, Assistant Professor G. Anburaj

Abstract-This paper looks into the effects of class size on student learning outcomes. Learning outcomes include academic performance, peer interactions, and teaching effectiveness. Data analysis of the responses given in the questionnaire indicates that smaller classes enhance student engagement and academic success. It provides teachers an opportunity to give one-to-one attention to every student. Some students perform well irrespective of their class size, therefore implying the impact of teaching quality as well as individual learning styles. The paper deals with logistics in “smaller classes” while arguing for a multifaceted approach aiming at integrating class size reduction with high-quality instruction and adequate resources toward the best learning outcomes possible.

DOI: /10.61463/ijset.vol.12.issue6.311

What Qualities a Modern-Day Professor Should Possess

Authors- Pasumarthi.Sanath, Assistant Professor G. Anburaj

Abstract-The five characteristics that define what a professor ought to possess in order to do his/her job in a modern institution, adaptability, emotional intelligence, cultural competency, and communication skills. These five aspects are critically reviewed in light of their applicability to the trend of greater inclusion found in digital tool use, and interdisciplinary research. Given that there is a need for teacher thinking to be agile enough to incorporate the newer, more inclusive student-centred learning practices, educators must be adaptable and more interested in learning about complex digital systems. It is also believed that emotional intelligence is highly indispensable for teachers who want to establish healthy relationships with their students and be able to cope with the academic and student pressures. Cultural competence in dealing with the diversity of the modern classroom is a very critical aspect of teachers’ practice for one to become effective teachers. To make teaching strategies equitable and democratic, teachers must familiarise themselves with culture diversity and individual worldviews. In the final analysis, the effectiveness of a teacher is said to depend on how effectively he or she can communicate, teach content, relate with students, and interact with the overall academy.

DOI: /10.61463/ijset.vol.12.issue6.312

The Impact of E-commerce on Small-Scale Farmers

Authors- Sparsh Hedau, Sujal Dwivedi, Siddharth Audichya

Abstract-This paper explores the effects of e-commerce on small-scale farmers. By analyzing recent studies and conducting a small survey among farmers, the research investigates how online platforms have influenced their sales, market reach, and profitability. The findings suggest that while e-commerce offers significant benefits, such as broader markets and reduced costs, many farmers still face challenges related to technology access and platform management.

DOI: /10.61463/ijset.vol.12.issue6.313

Multi-Objective Optimization in Highway Pavement Maintenance and Rehabilitation Project Selection and Scheduling

Authors- Sandip Sampat More, Assistant Professor Shashikant B.Dhobale

Abstract-The expansion and quality increase of road infrastructures in developed countries during the last decades is shifting the attention towards their preservation rather than to new construction. Pavements are the most costly road asset. Therefore, their preservation management optimization is important in order to meet quality and safety demands within available budgets that are becoming increasingly limited. More recently, environmental aspects related to the pavements life-cycle have been raising concerns that must be addressed. The present Synopsis describes the development of an optimization methodology that intends to be a decision support tool for road administrations. In fact, this work emerged within the scope of a highway administration related project, so it has a character of applied research.

High Performance Concrete Design in Highway Construction with Soil Stabilization Using Heavy Compaction Test

Authors- Deepak Jyoti Sen, Professor Jitendra Chouhan

Abstract-Soil stabilization is a process which improves the physical properties of the soil, such as increasing in shear strength, bearing capacity etc. Which can be done by the use of controlled compaction or addition of suitable mixtures like cement, lime, and waste materials like fly ash, coconut shell etc. The cost of introducing these additives has also increased in recent years which opened the door widely for the other kinds of soil additives such as plastic, bamboo etc. This new technique of soil stabilization can be effectively used to meet the challenges of the society to reduce the quantities of waste, producing useful stabilization from plastic waste. Use of plastic products such as polythene bags, bottles etc is increasing day by day leading to various environmental concerns. Therefore, the disposal of plastic wastes without causing any ecological hazards has become a real challenge. Thus, using plastic as soil stabilizer is an ecological utilization since there is scarcity of good soil for construction. This project involves the study on the possible use of waste plastic. The results of CBR test have been done on Aspect Ratio of plastic strips and percentage of plastic.

Analysis Of Recycled Aggregates From Construction And Demolition Wastes As Alternative Filling Materials For Highway Construction In Indian Domain

Authors- Pradeep Kumar Verma, Mr. Shashikant B. Dhobale

Abstract-highway construction waste aggregate leads to disasters, and the solution for that consists of 5 steps. for one, bring an end of being a part of causing waste by prevention. on the other hand, waste can be managed by recycling, reusing, recovering, and last option is to clearance or disposal. also, other factors such as economical and marketing are considered to be effective answers. review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. to identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. this study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field. these wastes are heavy, having high density, often bulky and occupy considerable storage space either on the road or communal waste bin/container. it is not uncommon to see huge piles of such waste, which is heavy as well, stacked on roads especially in large projects, resulting in traffic congestion and disruption. waste from small generators like individual house construction or demolition, find its way into the nearby municipal bin/vat/waste storage depots, making the municipal waste heavy and degrading its quality for further treatment like composting or energy recovery. often it finds its way into surface drains, choking them.

Implementation of Hardware for Voice-to-Voice Chat GPT

Authors- D. Triveni, V. Kusmitha Devi, P.V.S. Ramanjaneyulu, L.Upendra, Y. Bhanu Prasad, P. Ravi Kumar

Abstract-This study suggests a hardware design approach that makes use of ChatGPT to improve technology accessibility for people with visual impairments. Using ChatGPT as an inference engine, the system transforms auditory inputs into insightful responses, creating a seamless and intuitive interaction environment. The interface has adaptive audio processing for noise reduction, personalized voice recognition, and real-time language translation to meet the needs of various users. A microcontroller-based hardware architecture enables efficient, low-latency connection with ChatGPT, ensuring prompt and contextually relevant responses. Additionally, by allowing communication with existing assistive technologies, the system design promotes device adaptability across several platforms. Evaluations show how well the interface works to empower visually impaired individuals by facilitating independent information access, real-time decision assistance, and usability.

DOI: /10.61463/ijset.vol.12.issue6.314

Industrial Production Productivity Analysis With Respect to Labors

Authors- Research Scholar Sachin Kachhi, Assistant Professor Ranjeet Singh Thakur

Abstract-Low productivity of workers is the most significant factor behind delivery slippages in manufacturing industries. As manufacturing is a laborer predominant industrial sector, this paper focuses on worker output and their efficiency in the manufacturing sector. It covers the definitions of productivity, efficiency of the workers, its perspectives and the factors influencing the productivity. Proposed ANOVA method optimize performance of productivity and worker production parameters. Also observed more sensible case to increase production productivity.

Analyzing the Impact of Feedback on Student Development

Authors- Prateepa.N, Assistant Professor G. Anburaj

Abstract-It depicts that seeking feedback is a critical aspect of developing a learner as the impact will come to bear both academically and in life. Self-regulation even goes to the extent of how feedback can determine the learning process by learners and their motivational disposition, ability in specific. Therefore, to serve the purpose, the study draws on various scholarly literature and case studies that will help determine how prompt, helpful, and targeted feedback incites deeper learning and raises student engagement. It supports providing constructive and reflective criticism towards students in such a way that students are kept up-to-date with their strengths and weaknesses. Result A lot of effective feedbacks often being well-managed will highly help in the cognitive, emotional as well as social developments in kids. However, cognizance is also given to the facts like too much feedback creates too much interaction between a teacher and a student by regarding individualized methods are important so that it actually pushes forward each development. Therefore, at the end, this study reveals giving feedback as important in guiding modern education and training students to be lifelong learners in an ever-changing learning environment.

DOI: /10.61463/ijset.vol.12.issue6.315

Students’ Perceptions of Formative Assessments in Malaysian Secondary Education: A Gender-Based Analysis

Authors- Noraziah Bidin, Zulkifli Bahrom, Nanthini Maree Velayutham

Abstract-This study examines Malaysian secondary school students’ perceptions of formative assessments, focusing on gender-based differences. A sample of 424 students (268 male, 156 female) from various form levels in a Malaysian secondary school completed the Students’ Perceptions of Formative Assessments (SPFA) questionnaire. The SPFA, adapted from Dorman and Knightley (2006) and translated into Bahasa Malaysia, measures five dimensions: congruence, authenticity, consultation, transparency, and accommodation, using a 4-point Likert scale. Results revealed high levels of perceived congruence (85-88% positive responses) and transparency (92-95% positive responses). However, authenticity perceptions were low (15-21% positive responses), with significant gender differences (χ2 = 9.87, p < 0.05). Consultation emerged as the weakest dimension, with most students disagreeing about their involvement in the assessment process. Accommodation showed mixed results, with gender differences in fairness perceptions (χ2 = 7.23, p < 0.05). Overall, students expressed positive attitudes towards formative assessments, with 100% appreciating their use and finding feedback helpful. Based on these findings, several suggestions are proposed to improve formative assessment practices in Malaysian secondary education. Educators should focus on enhancing assessment authenticity by incorporating real-world applications, which can increase student engagement and understanding. Efforts should be made to increase student involvement in the assessment process, thereby improving consultation and fostering a sense of ownership in their learning. It is also crucial to address the observed gender differences in perceptions of authenticity and fairness to ensure equitable assessment practices. While maintaining the strengths in congruence and transparency, educators should strive to balance assessment difficulty and frequency to avoid overwhelming students. These recommendations aim to align with ongoing educational reforms and promote student-centred learning, ultimately improving the effectiveness of formative assessments in Malaysian secondary schools.

DOI: /10.61463/ijset.vol.12.issue6.316

Effect of Online Education on Student’s Motivation

Authors- Shradha Pandey, Professor G. Anburaj

Abstract-This paper focuses on analysing how the online setting of studying influences student motivation and what role it plays in creating an effective virtual learning environment that engages, supports, and boosts productivity and well-being in students. In order for students to shift to a form of distance education, methods of learning have had to adapt to a more pliable and open access near-endless resources; still, this kind of a learning environment poses problems of weak interaction, screen fatigue, and self-discipline problems that may adversely impact students regarding motivation. In this report, the mixed impacts of online learning about students’ motivation are shown, along with the difficulties in learners’ motivation. This paper offers a general view based on the trend in students’ motivation that informs better ways of digital classroom development to foster student academic performance. According to findings from the current research, much room remains for potential realization regarding the application of online education. This calls for much consideration of motivational issues and design with support to a learning environment. With the influence of online learning on student motivation, it can then help educators and institutions come up with a more defined program to fit diverse needs, ensuring the longevity of engagement and performance among the students in the online context.

DOI: /10.61463/ijset.vol.12.issue6.317

Ominous Dose: Understanding Emotional Dynamics and Performance Cycles in Management

Authors- Arjita Jaiswal, Manish Chaudhary

Abstract-This theory suggests that if an individual is in an ominous mode, they won’t tend to smile. In other words, if someone doesn’t wish to smile, they won’t smile at any cost. The theory presents an interesting twist: there is a “boom after every inflation.” However, a disadvantage of the theory is the potential for a “deep fall” after a period of depression or inflation. Keeping creative destruction in mind, everything has its loophole to be breached. Although the answer may be yes or no, there always exists a condition of if/situation and but/exception.

DOI: /10.61463/ijset.vol.12.issue6.318

Theoretical Education

Authors- T. Ranjitkumar, Assistant Professor G. Anburaj

Abstract-This file explores the pedagogical theories relevance within the current learning environments: primarily, digital and blended models redefine the classroom. Academic models founded by some of the pioneering thinkers- Piaget, Skinner, Vygotsky, and Bruner-inclined towards constructivism, behaviorism, cognitivism, and connectivism respectively, however, their application in different technology-based settings becomes an open-ended question. Our look probes the strength of these applications at primary, secondary, and tertiary levels, with an astonishing frequency of methods in online schooling, where traditional approaches, as its focus on outcomes is even more well- suited with research-backed programs. The results are that despite the merits the classical models give, as far as lessons learnt for learning are concerned, a direct appeal should have had these models be fashioned to answer the special needs of the virtual hybrid class. The paper affirms modeling model flexibility to entail both technological integration and student diversity. Future research should emphasize the long-term implications of mixed-media results on pupil learning and consider factors related to socioeconomic and culture differences in developing adaptive context- sensitive instructional strategies that would be encouraged.

DOI: /10.61463/ijset.vol.12.issue6.319

Analyzing the Efficiency of Blended Learning in Higher Education

Authors- V. Naveen Kumar, Assistant Professor G. Anburaj

Abstract-This research explores the learner’s preference, challenges and behavior towards the blended learning environment. This study aims at understanding how the various instructional methods and technologies impact the learning experience of the learners. Through this report it is found that the learners are more overwhelmed with the physical interaction than the online platforms. The flexibility of accessing the course materials especially the interactive course module is also highlighted and then the challenges faced such as mainly technological difficulties, navigation issues in blended learning is also discussed and also the project formats and assessments method to improve the knowledge of the learners is also discussed in this research paper. The results suggest that there is a need for the blended learning models that balance the physical and technical platform together. These findings help improve the blended learning with help of further research in fields of enhancing the online engagement with the help of virtual reality.

DOI: /10.61463/ijset.vol.12.issue6.320

Survey on Sitting Posture Detection and Correction

Authors- Mr. Siddhant Bhalerao, Mr. Karan Birwadkar, Mr. Vedant Jadhav, Dr. Sunil Chavan

Abstract-Prolonged poor sitting posture can lead to health issues, highlighting the need for systems that encourage healthy habits. This project presents a human sitting posture detection and correction system using the Yolov5 model for real-time posture monitoring via a webcam. The system classifies user postures based on ergonomic criteria and provides corrective alerts when bad posture is detected. Preprocessing techniques, such as scaling and augmentation, are applied to the dataset to ensure model robustness. A user-defined timer is also integrated to remind users to take breaks. The trained Yolov5 model is evaluated for accuracy and implemented in a real- time detection system, with practical applications in promoting better sitting habits in home and office environments.

DOI: /10.61463/ijset.vol.12.issue6.321

AI Trainer for Fitness: A Virtual Personal Trainer Using Computer Vision

Authors- Ansari Zahoor, Ansari Zaid, Ansari Abdullah

Abstract-In the contemporary fitness landscape, artificial intelligence (AI) is revolutionizing the way individuals approach personal training and exercise. This review examines the transformative potential of AI-driven fitness trainers, particularly those utilizing computer vision, to provide personalized workout experiences tailored to individual needs. By analyzing recent studies on AI applications in fitness, we highlight how these technologies enhance user engagement, deliver customized feedback, and foster better exercise outcomes through real-time interaction. However, challenges such as data privacy, algorithmic bias, and the need for robust user interfaces limit widespread adoption. This review underscores AI’s capacity to transform fitness training, converting traditional exercise routines into personalized, engaging journeys.

DOI: /10.61463/ijset.vol.12.issue6.322

Evaluating the Effectiveness of Blockchain Technology in Mitigating Cybersecurity Crimes in Electronic Health Management Systems within Public Hospitals in Kenya

Authors- Margaret Afwande, Samuel Barasa, Jane Kabo

Abstract-The digital transformation of healthcare fundamentally enhanced the management and accessibility of patient data through the implementation of Electronic Health Management Systems (EHMS). In Kenya, public hospitals increasingly integrated EHMS to optimize healthcare service delivery and improve patient care outcomes. However, this transition introduced significant cybersecurity vulnerabilities, including data breaches and unauthorized access, jeopardizing the confidentiality and integrity of sensitive patient information. This study evaluated the effectiveness of blockchain technology in mitigating these cybersecurity threats within EHMS in public hospitals in Kenya. The research identified that 78% of IT specialists reported data breaches as the most prevalent cyber threat and 64% cited frequent incidents of unauthorized access. Furthermore, 82% of respondents indicated that existing cybersecurity strategies were insufficient to address emerging threats. Despite the recognized limitations of current security measures, 87% of experts expressed confidence in blockchain’s ability to enhance EHMS security. The decentralized and immutable nature of blockchain was perceived to significantly mitigate unauthorized access and data tampering, with 90% of respondents agreeing that it could reduce the risks of data manipulation. The qualitative interviews with healthcare professionals revealed concerns about privacy violations and mistrust in the current EHMS. The findings underscored the urgent need for innovative cybersecurity measures, with blockchain emerging as a promising solution. This study contributed valuable insights into the potential advantages and limitations of blockchain technology, establishing a framework for enhancing the security of EHMS in Kenya and informing the development of more secure healthcare information systems.

DOI: /10.61463/ijset.vol.12.issue6.323

Enhancing Student’s Learning Via Optimized Classroom Layout

Authors- R.J. Nitish Manikandan, Assistant Professor G. Anburaj

Abstract-It examines the kind of relationship existing between classroom layout and the engagement and learning of students in regard to contemporary teaching settings. It stresses that there has been a shift from the traditional, more rigid designs of the class room to more dynamic and adaptive arrangements as different styles and methodologies of learning are met. This reminds us that a furniture-occupied central space and teacher’s desk placement configuration can significantly affect the students cognitively, socially, and emotionally. Therefore, the optimum best optimized configuration produces teamwork, concentration, and active learning, but flexible configurations would have integration with technology and many group activities, though it is overcrowding that encourages creativity and inhibits unused spaces for creativity. On the other hand, it demonstrates that the digital tools need to be integrated into the learning procedure hence organizing better. As such, the paper presents itself as a clarion call for the classroom designing procedure that balances somehow flexibility and structure towards the various demands of both educators and students in the contemporary educational paradigm.

DOI: /10.61463/ijset.vol.12.issue6.324

Evaluating the Impact of YouTube Educational Videos in Teaching Pollination Concepts among Secondary School Biology Students in Sabon Gari, Kaduna State

Authors- Lawal Saadatu Bagiwa, Najmuddeen Alhassan, Nuru Asabe Ramatu

Abstract-This study evaluated the Impact of YouTube Educational Videos in Teaching Pollination Concepts among Biology Students in Sabon Gari. A quasi-experimental design was adopted. The population comprised of 94 students from the selected two schools with two intact classes from the study area, in which one school served as an experimental and the other served as a control group. The experimental group students were given instruction utilising YouTube while the control group students were instructed utilising the conventional method. A data collection instrument titled the Pollination Achievement Test (PAT) was employed for the study. It was validated and reliability tested by using test-retest and PPMC was used for analysis to determine 0.71 as the coefficient value. The study found that there is a significant difference in the mean scores of students taught pollination concepts using YouTube and those taught using conventional teaching methods with a t-value of 4.37 and p-value of 0.00. Also, there is no significant difference in the mean scores of both male and female students when taught pollination using YouTube with a t-value of 1.12 and p-value of 0.26.The study among others recommended use of YouTube in teaching the biology concept of pollination in secondary school should be encouraged as it is empirically established that it enhances academic performance among biology students while teaching them the concept of pollination, teachers and schools should be equipped with modern facilities and instructional packages to enable the teachers/tutors to carry out their lessons via the YouTube instructional strategy method.

DOI: /10.61463/ijset.vol.12.issue6.325

Development of an Intrinsic Fiber Optic Sensor for Antioxidant Levels in Blueberries

Authors- Mauli K. Pandhare, Assistant Professor Shrikant M. Maske, Kapil Shinde, Monika D. Salunkhe

Abstract-In this work, an intrinsic fiber optic sensor (IFOS) for identifying antioxidants in Blueberry (Vaccinium corymbosum) extract is developed. Blueberries are well known for having a lot of antioxidants, especially anthocyanins, which are important for human health. The sensor uses fiber optic technology, which benefits from its high sensitivity and quick reaction time, to offer a non-invasive and effective method of antioxidant detection. We describe the sensor’s architecture, functionality, and testing process in detail. High sensitivity and accuracy were demonstrated when the sensor was calibrated using known antioxidant concentrations in Blueberry extracts. Examining bioactive chemicals in agricultural products and quickly evaluating food quality may benefit greatly from this innovative method of antioxidant detection.

DOI: /10.61463/ijset.vol.12.issue6.326

Impact of School Directors’ Leadership Behaviors on Teachers’ Moral

Authors- Dr. Prak Polla, Dr. IN Channdy, Dr. LIM Sothea

Abstract-This study aims to identify effective leadership behaviors that Secondary Resources School (SRS) school directors can employ to improve teacher morale in order to meet the parameters of current reforms including student achievement. The research objectives of this study were: 1) to explore the SRS school directors’ leadership behaviors as perceived by the SRS teachers, 2) to investigate the degree of the morale level of teachers as perceived by the SRS teachers, 3) to find out the relationship between SRS school directors’ leadership behaviors and teachers’ perceptions of their morale practices, and 4) to identify the SRS directors’ leadership behaviors contribute to teachers’ morale. The present study selected the methods of exploratory sequential design, combining qualitative and quantitative approaches. A total of 452 SRS teachers from different demographic data participated in this study, derived through 10% of total teachers from 50 SRSs and simple random sampling by drawing lots. After the teachers completed the questionnaire, 16 SRS teachers and 8 SRS school directors were recruited for the semi-structured interviews. Collected data were analyzed using descriptive (frequency, percentage, mean, S.D.) and inferential statistics (t-test, One-way ANOVA, Correlation and Regression Analyses) through computer program procedures.

DOI: /10.61463/ijset.vol.12.issue6.327

A Study on Employee Branding at Fly Realty Bengaluru

Authors- Assistant Professor Dr. Rekha N Patil, Shivaputrappa R Pagadi

Abstract-The method of developing an manager make that highlights a company’s distinctive culture, values, and goal of drawing in, retaining, and engaging top personnel is known as employee branding. As businesses realize how important it is to stand out in a crowded job market, this idea has attracted a lot of attention lately. A variety of academic fields, including marketing, psychology, organizational behaviour, as well as person store management, provide the notional basis for employee branding. A company’s employer brand, which encompasses the attitudes, experiences, and views of both present and budding workers, is an essential part of its overall brand identity. Aligning the manager product with the organization’s overarching goal, vision, and standards communicating this identity consistently across a variety of touchpoints, such as the corporate website, social media, and other channels, are important components of effective employee branding. Strong employer brands have been linked to better hiring practices, higher employee engagement levels, and more work happiness, all of which boost a company’s ability to compete and perform well in the market. Effective employee branding strategies may be developed and implemented in an organized manner with the help of theoretical frameworks like the Employee Value Proposition (EVP) framework and the Employer Branding Model. Moreover, in the digital era, employees are acting as brand ambassadors for their companies and enhancing The employer’s reputation as an employer whole. This makes The notion of personal branding—in which workers develop and market their own professional brands—more pertinent. Businesses may create an employer brand that appeals to their target market and ultimately drives corporate success and sustainability by learning the theoretical underpinnings of employee branding.

DOI: /10.61463/ijset.vol.12.issue6.328

Examining How Digital Tools Can Improve Educational Outcomes

Authors- Hari Krishna K, Assistant Professor G. Anburaj

Abstract-This research has attempted to investigate the impact of digital tools on learning outcomes for students in a blended, hybrid learning setting combining the convenience of technology with the physical classroom. One articulation of student preferences can be derived by watching students who meet with technological barriers while completing online interfaces and many online interactions as compared to face-to-face meetings. Digital tools enable course material access and also new ways of making interactive learning modules. The research also looks into the possibility of whether a project-based evaluation and teaching approach can enhance students’ engagement and retention. Outcomes suggest that although students welcome the availability of digital resources, models need to be more balanced so that both physical and technical platforms are combined. This type of research focuses more on digital methods, such as integrating virtual reality, to build more conducive learning settings for better educational results

DOI: /10.61463/ijset.vol.12.issue6.329

Increasing Equality in Student Learning Results Across Socioeconomic Lines

Authors- Ms Aadhira M, Assistant Professor G. Anburaj

Abstract-This is the study that concerns itself with the efforts made to address the gaps in the levels of educational attainment among students from different Socioeconomic backgrounds and the conditional inequality in learning. One of the major barriers for those wanting to obtain a degree and engage in studies is a low economic status. Such research, it should be mentioned, explores various equity-enhancement strategies such as providing the proper amount of resources, giving the help to those with specific needs, or applying computer technologies in the educational process. The study also gives importance to providing low-income children with specific, community- oriented, and most of all – healthcare within the education policies as a means to improving academic performance. Some results are shown regarding the adoption of such measures as organizational interventions that work both within and outside the classroom. Furthermore, when it comes to lowering the barriers to learning, our research supports the notion that mental health and family involvement are important factors in assisting disadvantaged adolescents to achieve their educational potential. This observation indicates that common assessment measures like standardised tests and provision of a uniform textbook and national educational programmes do not offer any significant solutions to the issues facing people from different social classes. Rather, the highest level of learning outcomes is provided by the most effective assistance, which is targeted at poor schools, the provision of electronic resources, and initiatives by the local population. Educational systems can seek to narrow achievement disparities and ensure an equal opportunity for educational success for all children, irrespective of their social class by implementing these remedies. Education alone cannot help achieve a fairer society, this intervention also fosters social mobility.

The Future of E-Learning

Authors- V. Sesha Gokul, Assistant Professor G. Anburaj

Abstract-This research investigates the future of e-knowledge, focusing on approachability challenges that learners face with digital principles. The study aims to accept the suggestions of elearning approachability on pupil consequences, participation, and date and labels the key obstacles that obstruct an impartial approach to online instruction. The judgments display that while technology can considerably reinforce learning knowledge, many learners face challenges to a degree restricted access to instruments, uncertain internet relatedness, and a lack of mathematical proficiency. The study highlights the significance of crafty allembracing e-learning floors that address these approachability barriers to conceive a more impartial education environment. The research further suggests potential answers, such as reconstructing the foundation, enhancing mathematical knowledge, and leveraging useful technologies, to support learners’ braid approachability challenges.

Recent Advances in Optical Coherence Tomography: Innovations in Physics, Technology, and Their Medical Imaging Applications

Authors- Eenas Mawloud Abdulqadir Waleed

Abstract-Optical Coherence Tomography (OCT) has emerged as a powerful non-invasive imaging modality that offers high-resolution, cross-sectional images of biological tissues, revolutionizing medical diagnostics. This review highlights the recent advancements in OCT technology, including innovations in light sources, detection methods, and imaging algorithms, which have significantly enhanced imaging capabilities and broadened clinical applications. We explore the diverse applications of OCT in various medical fields, particularly ophthalmology, cardiology, dermatology, and oncology, emphasizing its role in improving diagnostic accuracy and patient outcomes. Despite its numerous advantages, challenges like penetration depth limitations, motion artifacts, and regulatory hurdles remain. Future perspectives on OCT technology, including integrating artificial intelligence and developing portable devices, are discussed. This review aims to provide a comprehensive overview of the current state of OCT, emphasizing its impact on medical imaging and potential future developments.

DOI: /10.61463/ijset.vol.12.issue6.330

Comparative Study of Hydrogen Generation Technologies

Authors- Assistant Professor Vinay Shrimali, Professor (Dr.) Mukesh Shrimali

Abstract-Hydrogen is a clean energy carrier with applications in various sectors such as transportation, industry, and power generation. As the world shifts toward decarbonization, multiple technologies for hydrogen production have emerged. These technologies differ in terms of feedstock, energy source, efficiency, cost, scalability, and environmental impact. This research compares the leading hydrogen generation technologies, highlighting their advantages, challenges, and potential for future applications.

DOI: /10.61463/ijset.vol.12.issue6.331

Information Sharing Framework using Resource Oriented Architecture (REST) to Manage Quality of Service: A Comprehensive Review and Evaluation

Authors- E.J.A.P.V.Shashikala, W.M.J.I.Wijayanayake

Abstract-The research paper aims to identify the most important characteristics of Representational State Transfer (REST) that affect the quality and quality requirements of web services. A systematic literature review was conducted to identify the most frequently mentioned REST characteristics, which were then verified by industry experts in Sri Lanka. Based on the verified variables, a survey questionnaire was developed using a conceptual model. The survey was distributed to 33 developers and 34 consumers of RESTful web services, and their responses were analysed using Partial Least Square Structural Equation Modeling (PLS-SEM) in SmartPLS3 Version 3.1.6 to determine the linkages between REST characteristics and Quality of Service (QoS). Importance-Performance Matrix Analysis was executed to identify the REST characteristics with higher importance and performance towards QoS. Based on the findings, a framework was developed with REST characteristics that could improve QoS in the early development stages of RESTful web services. This framework is useful for service providers and developers to develop and implement high-quality RESTful web services that meet the QoS requirements of their customers.

DOI: /10.61463/ijset.vol.12.issue6.332

Impact of Technology Exposure on Student Learning Skills

Authors- Ravuru Vijitha, Assistant Professor G. Anburaj

Abstract-This study is an examination on the effects of technology exposure and its impact on students ability on problem solving specifically, how digital tools and resources relate to cognitive and critical thinking within the academic environment. You have access to technological equipment any time you want, which has been used in modern education for decades and provides a greater degree of exposure for students while also allowing them the opportunity to analyse and think creatively so they can arrive at the generation of solutions. Its impact on learning abilities is two-fold, while it provides a lot of opportunities as well as some challenges. The development in technology definitely has changed student education in terms of how they learn and use skills. This research examines how technology exposure affects the capacity of students to handle problems and tries to determine what positive versus negative impacts communication technologies have on critical thought, analytical skills, and adaptability. As technology evolves to include expressively diverse and creative educational tools, manifested through interactive software and online resources, students now have unparalleled access to information and learning aids for academic objectives which has changed the paradigm of systematic problem solving. Technology in education is not only a means to gain knowledge, it motivates self-learning, participates in group learning and develops an environment where students can work together to solve new problems creatively. However, the study provides a counterbalance for potential drawbacks: “challenges of over-reliance on technology and reduced face-to-face.

DOI: /10.61463/ijset.vol.12.issue6.333

Mercury Dynamics in Aquatic Systems: A Critical Review of Methylation, Bioaccumulation, and Global Health Risks

Authors- Vibha Chauhan

Abstract-Mercury is highly toxic environmental pollutant of serious concern to human and fish populations in this world we live in. The function of microbial methylation to convert inorganic mercury to the highly toxic and bioaccumulative form of methyl mercury (MeHg) in aquatic ecosystems is explored in this review. The bioaccumulation and cumulative nature of the methylmercury significantly impair nervous and developmental systems. It is also in high amounts in the tissue of animals higher on the food chain like fish consumed by humans. Here, we discuss the possible impacts of DOM, SO 4 2-, and microbial consortia on the methylation and bioavailability of Hg. The review also focuses on how toxic levels of mercury impact the wellbeing of the wild life and food chains among other ecological impacts. We assess the effectiveness of community-based mercury level monitoring programs and the current methods of managing mercury containing products like the Minamata convention. Perceived research gaps present the need for further research and development of remediative technologies to closure. Some of these gaps are about identifying not merely the disposal of mercury in sediment and the behaviour of mercury under global warming and climatic alteration. The findings bear witness on how imperative it is for countries to come together in order to reduce the extent of the mercury’s impacts on environment and people.

DOI: /10.61463/ijset.vol.12.issue6.334

Critical Success Factors in Enterprise Resource Planning (ERP) Implementation: A Comprehensive Review

Authors- Mohan Kunkulagunta, Vedaprada Raghunath

Abstract-This review explores the critical success factors (CSFs) that impact the implementation and management of Enterprise Resource Planning (ERP) systems in various organizational contexts. Key factors include top management support, effective communication, user involvement, and change management strategies. Challenges include resource constraints, cultural barriers, and technical issues, necessitating careful planning and stakeholder engagement. Emerging technologies like artificial intelligence and cloud computing add complexity to ERP systems, necessitating further exploration. The paper emphasizes the importance of multi-method research approaches to understand ERP implementation challenges and provide practical guidance. Future research directions aim to refine CSF frameworks and address evolving dynamics in diverse organizational settings.

DOI: /10.61463/ijset.vol.12.issue6.335

Smart Stethoscope Real-Time Health Monitoring

Authors- Mahboob Ansari, Md. Nasir Alam, Harish Khan, Trilok Bisen, Mayank Kumar, Professor Sanjay Khadagade

Abstract-Stethoscopes are normally used by doctors to monitor sounds of internal organs. Common man can’t understand them, therefore whenever need arise, we need to visit doctor. But sometimes at emergency we are unable to meet a doctor. Therefore, the need for an IoT based Stethoscope is necessary. We are making a Smart Stethoscope which eliminates the need for the medical practitioner to be physically present with patient during emergency. This paper presents the design and implementation of a digital stethoscope utilizing an electret microphone and an ESP32. The major objective of this project is to enhance remote patient monitoring by allowing healthcare professionals to listen to heart and lung sounds from any remote location.

DOI: /10.61463/ijset.vol.12.issue6.336

Bioremediation Uncovered: A systematic Review on how Nature Fights Pollution

Authors- Mr. Saransh Nigam, Dr. Adarsh Keshari, Dr. Hansika Rajoria, Ms. Mahin Sajid, Mr. Srinivas Pedapolu, Mr. Abhishek Katiyar

Abstract-Bioremediation is a promising approach for the remediation of polluted environments, utilizing the natural abilities of microorganisms to degrade or transform toxic pollutants into less harmful or inert substances. This review provides an overview of bioremediation, including its types, microorganisms relevant to bioremediation, advantages and disadvantages, and conclusion. Bioremediation can be categorized into two main types: in situ and ex situ. In situ bioremediation involves treating contaminated soil and groundwater at the site of pollution, while ex situ bioremediation involves treating contaminated materials in a controlled environment outside the site of pollution. Different microorganisms, such as bacteria, fungi, and algae, have been utilized in bioremediation for the degradation of various pollutants, including hydrocarbons, heavy metals, and pesticides. The advantages of bioremediation include its cost-effectiveness, environmental friendliness, and the potential for complete degradation of pollutants. However, some of the disadvantages of bioremediation include the need for suitable environmental conditions, limited availability of microorganisms for some pollutants, and the potential for the formation of harmful by-products during degradation. In conclusion, bioremediation is a promising approach for the remediation of polluted environments, offering several advantages over conventional remediation methods. However, further research is needed to optimize bioremediation strategies, increase the availability of microorganisms for various pollutants, and address potential drawbacks and limitations of the technology.

DOI: /10.61463/ijset.vol.12.issue6.337

Transmission Gear NVH Analysis & Prognosis at end of Line Facility

Authors- Pranay Subhedar, Nitin Tawhare

Abstract-This study focuses on the novel approach in prognosis of NVH defects in transmission at the end of line facility at the transmission assembly stage. Basically by conducting digital comparisons of deviation curves, it becomes possible to differentiate between noisy and quieter gears. The root causes defects in gears are identified in terms of ripple orders and hence making it easier for prognosis. This methodology aids in pinpointing the origins of ripples and suggests production modifications. In summary, this article highlights the gear micro-geometry defects which are induced due to manufacturing defects in process or assembly issues which lead to damage, thus reducing root cause analysis time and improving transmission delivery quality in mass production.

DOI: /10.61463/ijset.vol.12.issue6.338

Review of Passive Cooling Techniques and Introduction of Artificial Intelligence for Optimising Passive Cooling of Buildings

Authors- Shashank R, Professor Dr. M. Rajagopal

Abstract-A Comfortable home with low energy consumption are the dream of ordinary people, governments and researchers. First, researchers are interested in lowering energy costs and reducing fossil fuel use. In the current state of energy and the environment, there is no need to demonstrate the necessity of reducing energy consumption by installing air conditioning. However, the motivation has now changed from these goals to reducing carbon dioxide emission from the environmental perspective. The internal temperature of the building depends on local conditions (outside temperature, wind speed, solar radiation, etc.), the geometry of the building (wall thickness, window-to-wall ratio) and the thermo physical properties of the building materials (thermal conductivity, specific heat capacity of the materials, etc.), indoor thermal load, air exchange rate. Passive cooling techniques can also be used to reduce the internal temperature of the building to ensure thermal comfort. Passive cooling techniques uses cautious microclimate design, shading and thermal capacitance to help to reduce cooling load requirement. Heat sinks such as air, ocean and earth surface are needed to dissipate the excess heat in the building to natural sink through convection, evaporation, radiation and also by earth cooling. The integration of artificial intelligence (AI) in passive cooling for buildings represents an innovative approach to enhancing energy efficiency and occupant comfort.

DOI: /10.61463/ijset.vol.12.issue6.339

PixelProof: Uncovering the Truth in Images

Authors- Professor Rinku Badgujar, Suryansh Khandelwal, Shriyog Borse, Nagesh Kannure, Manav Mayyank

Abstract-In the age of sophisticated digital manipulation, deepfake content poses significant threats to privacy, security, and information integrity. PixelProof addresses these challenges with an AI-powered system designed to detect deepfake and forged content in images and videos. Utilizing advanced machine learning and computer vision techniques, it analyzes inconsistencies such as pixel anomalies, lighting discrepancies, and facial distortions. PixelProof combines convolutional neural networks (CNNs) for spatial analysis and recurrent neural networks (RNNs) for evaluating temporal inconsistencies in videos. With real-time detection capabilities and detailed tampering reports, it empowers users to verify the authenticity of digital media. By promoting trust and combating misinformation, PixelProof ensures the reliability of visual content in a rapidly evolving digital world.

DOI: /10.61463/ijset.vol.12.issue6.340

Leveraging IOT-Driven Solutions for Sustainable Farming and Advanced Agricultural Practices to Deploy Smart Applications in Farming for Increased Yield

Authors- Ms Bhavika Patel

Abstract-Modern agriculture has evolved into a data-driven, precise, and intelligent industry. Nations worldwide grapple with food security issues stemming from population growth, scarce renewable resources, shrinking farmland, and erratic climate conditions. In response, the agricultural sector has adopted “smart agriculture,” transitioning from statistical to quantitative methodologies. This shift, powered by Internet of Things technology, aims to improve efficiency and increase yield. These transformations are disrupting conventional farming practices while simultaneously generating new prospects and hurdles. This research seeks to examine recent progress in IoT-based smart applications within agriculture and anticipate potential obstacles in merging this technology with traditional farming methods. The study’s outcomes will offer crucial insights for future IoT research and development, aimed at improving agricultural quality and profitability. Implementing smart application techniques can enable swift, secure, and extensive classification, providing benefits such as instant data access, flexibility, user-friendliness, and accurate spatial resolution. These advantages contribute to increased production while reducing energy consumption and time requirements.

DOI: /10.61463/ijset.vol.12.issue6.341

Drunk Driving Detection for Four-Wheeler Locking System

Authors- Assistant Professor Christyjuliet B, Sasmidha C, Sudhiksha G, Soundarya S, Sasmitha M

Abstract-The incidence of road accidents due to drunk driving continues to be a global concern, contributing to significant loss of life and property. This research paper proposes an advanced system designed to detect alcohol consumption in drivers and prevent vehicle operation if intoxication is detected. By integrating a breath alcohol sensor with a microcontroller and incorporating deep learning algorithms, the system offers real-time detection with enhanced accuracy. The paper discusses the design, implementation, and testing of the system, including the use of a GSM module for emergency alerts. The system’s reliability, affordability, and potential impact on road safety are thoroughly evaluated. This paper includes detailed circuit and block diagrams for a comprehensive understanding of the system architecture.

DOI: /10.61463/ijset.vol.12.issue6.343

Hand Written Signature Verification Using Deep Learning

Authors- Associate Professor S.R. Raja, G. Narmadha

Abstract-Even though people are moving to digital documents with digital signature for authentication, most of the areas such as land records, agreement between parties, legal certificates, identification cards etc., uses only handwritten signature. Verifying signatures is an important step because a fraudulent signature would negatively impact the true owner. Hence, recognizing genuine signatures becomes essential to avoid such frauds. To recognize the signature, deep learning technique is used in this project since it produces highest accuracy and it does not require too much preprocessing. A convolutional neural network (CNN) based deep learning model is mostly used for image processing, classification, and segmentation. Therefore, in this project, we’ll combine CNN, Deep Neural Networks, VGG16, and Image Processing to create a reliable system for recognizing signatures.

DOI: /10.61463/ijset.vol.12.issue6.344

Enhanced Demand-Supply Matching in E-Commerce Using Deep Learning Techniques

Authors- Assistant Professor Dr. Pankaj Malik, Pavitra Singh, Zainab Abbas, Harshita Jain, Madhvi Bhawsar

Abstract-E-commerce companies face ongoing challenges in accurately matching supply with volatile demand, influenced by seasonal trends, promotions, and rapid shifts in consumer behavior. Traditional demand-supply matching methods often fall short in addressing the complexity and scale of e-commerce data, necessitating more sophisticated approaches. This paper proposes a deep learning-based framework for demand-supply matching, leveraging models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Transformer models to capture intricate temporal patterns and dependencies in e-commerce demand. By integrating diverse data sources—historical sales data, customer behavior insights, and external factors—this approach dynamically predicts demand patterns and guides supply allocations with higher accuracy. Our experiments reveal that deep learning models significantly outperform traditional methods in demand forecasting metrics, such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), particularly in handling large-scale e-commerce datasets. Furthermore, the study demonstrates the potential of these models to reduce stockouts, optimize inventory levels, and enhance order fulfillment efficiency. This research provides a foundational step toward applying deep learning for demand-supply matching in e-commerce, highlighting both the performance gains and the challenges associated with model interpretability and real-time deployment. Future work will explore reinforcement learning integration and the use of multimodal data for further improvements.

DOI: /10.61463/ijset.vol.12.issue6.345

Evaluation of Noise Pollution in Campus (Case Study of Chang Jung Christian University)

Authors- Rukia A. Komba, Nizeyimana Ernest

Abstract-Noise pollution significantly impacts the quality of life, academic performance, and well-being of individuals in educational institutions. This study evaluates noise pollution levels at Chang Jung Christian University (CJCU) to determine its extent and effects on the campus community. Using quantitative data from digital sound level meters at eight locations and qualitative insights from 65 survey respondents, the study measures noise levels against the World Health Organization (WHO) and U.S. Environmental Protection Agency (EPA) standards. Results show an average campus noise level of 56 dB(A), which slightly exceeds the recommended 55 dB(A) for educational environments. While noise pollution on campus is generally not at a hazardous level, specific areas like the school restaurant, main gate, and scooter parking exceed 70 dB(A) during peak hours, disrupting concentration and comfort. Recommendations include traffic management, soundproofing measures, and public awareness initiatives to mitigate noise pollution and enhance the campus’s academic environment. The study highlights the importance of addressing noise pollution to support Sustainable Development Goals 3 (Good Health and Well-Being) and 4 (Quality Education).

DOI: /10.61463/ijset.vol.12.issue6.347

Current Trends and Challenges in Machine Learning-Based Renal Segmentation: A Review

Authors- P.G Scholar Akbar Nagani, Assistant Professor Abhay Rewatkar

Abstract-The significance of renal segmentation in medical imaging—especially in contrast-enhanced computed tomography (CT) scans—for the diagnosis and treatment of renal disorders is discussed in this work. This segmentation procedure is now much more accurate and efficient thanks to the quick development of machine learning algorithms. Through an analysis of several machine learning techniques used in renal segmentation, this literature review identifies important discoveries and their consequences. It looks at transfer learning, semi-supervised learning, hybrid and multimodal approaches, deep learning tactics, and traditional machine learning techniques. It also covers the issues at hand as well as potential avenues for future research in this area.

DOI: /10.61463/ijset.vol.12.issue6.348

Block chain-Based Management for Organ Donation and Transplantation

Authors- Assistant Professor Mrs.S.Kalaiselvi, Mrs.J.Sneha

Abstract-Today’s organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end-to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. In this paper, we propose a private Ethereum blockchain-based solution to enable organ donation and transplantation management in a manner that is fully decentralized, secure, traceable, auditable, private, and trustworthy. We develop smart contracts and present six algorithms along with their implementation, testing, and validation details. We evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions.

Regression Testing in Agile Development: Reducing Bugs in High-Velocity Environments

Authors- Assistance Professor Mr Mahesh Kumar Tiwari, Riya Yadav, Vishal Singh

Abstract-Regression testing refers to the type of testing done to ensure that modifications do not adversely affect the existing behavior of the software. As the software evolves, test suites tend to grow and get expensive to execute in their entirety. Several strategies have been followed to maximize the payoff from the accumulated test suite, including priority, selection, and minimization. The main goal of test suite reduction is redundancy elimination, which, in turn, leads to the number of tests executed being reduced. The main goal in selecting test cases is to define the relevant test cases pertinent to a particular set of recent changes. Testing priority is the scheduling of test cases in such an order that reflects the maximum potential for early defect discovery. This essay contains every piece of technique that is associated with minimization, selection, and priorities as well as the open issues and scope for future research. It is the process of re-testing the modified parts of the software and ensuring that know new errors have been introduced into previously tested source code due to this modification therefore regression testing tests the both modified source code and other parts of the source code that may be affected by change.

DOI: /10.61463/ijset.vol.12.issue6.349

Eyedi: Graphical Authentication Scheme of Estimating Your Encodable Distorted Images to Prevent Screenshot Attacks

Authors- Assistant Professor Mrs.Bhuwaneshwari, Ms. V.Pooja

Abstract-Graphical authentication schemes have the advantage of being more memorable than conventional passwords. Although some image distortion methods have been proposed to prevent the risks of over-the-shoulder attacks (OSAs), these methods cannot prevent camera recording attacks, as the key images are the same each time. In this study, we propose a graphical authentication scheme that generates various distorted images, named Estimating Your Encodable Distorted images (EYEDi). EYEDi generates distorted images by applying several image processing filters to the original images. Moreover, EYEDi estimates the appropriate image processing filter strength based on the authentication data. To measure attack resistance, twenty participants performed three types of attacks (OSA, camera recording attack, and screenshot) 300 times, each using existing methods and EYEDi. The classification error rate of all three types of attacks showed that EYEDi had a lower classification error rate between the legitimate user and attackers. Especially for the screenshot attack, which is the most severe threat model, the existing method was completely broken through, while EYEDi prevented the attacks with a classification error rate of 10%. This result shows that EYEDi can eliminate the screenshot attacker by using the difference in authentication times and a simple improvement in defense performance.

Gesture Controlled Virtual Mouse Using AI

Authors- Assistant Professor Mrs.G.Sangeetha Lakshmi, Ms.S.Hemalatha

Abstract-Gesture-controlled laptops and computers have gained popularity through technologies like Leap Motion, enabling control via hand gestures. This project proposes an alternative to traditional touchscreens and physical mice, offering a Computer Vision-based mouse control system. By using hand gestures tracked by a webcam through HSV color detection, users can navigate and control the system cursor. Operations like left-click, right-click, and double-click are performed with specific gestures. The system uses Python and the OpenCV library for real-time implementation, with the webcam output displayed on the monitor for seamless interaction.

Prediction of Stroke Disease Using Deep Learning Model

Authors- Assistant Professor Mrs. G.Sangeetha Lakshmi, Ms.P.Keerthana

Abstract-Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research reports predictive analytical techniques for stroke diseases using deep learning model applied on heart disease dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke.

Sign Language Recognition and Response Via Virtual Reality

Authors- Assistant Professor Mrs. G. Sangeetha Lakshmi., Ms. S.Devagi

Abstract-American Sign Language (ASL) enables communication in the deaf community, but its limited understanding by the broader population presents challenges. To address this, an ASL recognition system using Convolutional Neural Networks (CNN) is proposed to translate ASL gestures into text in real-time. The system processes video frames and converts them to grayscale images, utilizing a CNN classifier to recognize 26 static gestures representing the English alphabet. This method aims to improve communication by translating signed gestures into text, offering a practical solution for real-time ASL recognition and bridging communication gaps.

YOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios

Authors- Assistant Professor Mrs. R.Bhuvaneswari, Ms. K.Lavanya

Abstract-If there is a single object to be detected in an image, it is known as Image Localization and if there are multiple objects in an image, then it is Object Detection. This detects the semantic objects of a class in digital images and videos. The applications of real time object detection include tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, ball tracking in sports and many more. Convolution Neural Networks is a representative tool of Deep learning to detect objects using OpenCV (Open source Computer Vision), which is a library of programming functions mainly aimed at real time computer vision

Abdominal Multi-Organ Segmentation Via 3d Boundary Constrained Deep Neural Network

Authors- Assistant Professor Mrs. S.Kalaiselvi, Ms. R. Yamunadevi

Abstract-This project presents a novel method for abdominal multi-organ segmentation using a 3D boundary-constrained deep neural network. Accurate segmentation is essential for medical imaging, aiding in diagnosis and treatment planning, yet traditional techniques struggle with closely adjacent organs and varying shapes. The proposed approach enhances segmentation accuracy by leveraging spatial and contextual information, guiding the network to produce precise and coherent boundaries. Evaluated on a diverse abdominal image dataset, it outperformed conventional methods in accuracy and robustness. This work advances medical image analysis, offering an effective tool for organ segmentation with potential applications in automated diagnostics and surgical planning, while addressing complex challenges in medical imaging through boundary constraints.

Secure Storage Based AES Method of Dispersion

Authors- Assistant Professor Mrs.S.Kalaiselvi, Mrs.S.Praveena

Abstract-Cloud storage service has shown its great power and wide popularity which provides fundamental support for rapid development of cloud computing. However, due to management negligence and malicious attack, there still lie enormous security incidents that lead to quantities of sensitive data leakage at cloud storage layer. From the perspective of protecting cloud data confidentiality, this paper proposed a Cloud Secure Storage Mechanism named CSSM. To avoid data breach at the storage layer, CSSM integrated data dispersion and distributed storage to realize encrypted, chucked and distributed storage. In addition, CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage. The experimental results indicate that proposed mechanism is not only suitable for ensuring the data security at storage layer from leakage, but also can store huge amount of cloud data effectively without imposing too much time overhead. For example, when users upl ad/download 5G sized file with CSSM, it only takes 646seconds/269seconds, which is acceptable for users.

Intelligent System for Skin Disease Prediction Using Machine Learning

Authors- Assistant Professor Mrs. G.Sangeetha Lakshmi, Ms. R.Jothesswari

Abstract-Skin is an extraordinary human structure. It frequently suffered from many known and unknown disease. Therefore, diagnosis of human skin diseases is the most uncertain and complicated branch of science. It has been observed that most of the cases remain unnoticed because of the lack of better medical infrastructure and facilities. This paper is devoted to solve this challenge. Therefore, this paper effectively proposed SVM system which combines Convolutional Neural Network with Support Vector Machine classifier to develop a Mobile Android Application. Thus, to evaluate the performance of the proposed system several experiments are conducted on our dataset. This dataset consists around 3000 images which collected from a lot of sources like Beni-Suef University Hospital, Cairo University Hospital and various websites as well to be more accurate and realistic. Acomparative study of applying different Feature extraction algorithms with different classifiers was accomplished. The results obtained showed the adequacy of the proposed SVM system how many skin diseases images have been detected from skin disease dataset. Which lead to detect skin disease and provide the user with the disease name and treatment related prescription with high accuracy.

Optimizing Low Power Consumption in Circuit Design: A Review of Current Models and Techniques

Authors- M.Ishwariya, Professor Dr.M.Malathi

Abstract-Low power design is a critical challenge in the development of Very-Large-Scale Integration (VLSI) circuits, which integrate millions of transistors onto a single chip to enable advanced communication and computing applications. The rapid growth of VLSI technology has led to the integration of numerous functions into compact chips, resulting in increased power density and heat dissipation concerns. As a result, efficient power management techniques have become essential to ensure optimal performance and reliability of modern ICs. This paper reviews recent advancements in VLSI design methodologies focused on power and area optimization, highlighting novel approaches such as adaptive voltage scaling, dynamic power management, and energy-efficient circuit design. Additionally, it explores emerging technologies such as quantum-dot-based transistors, spintronics, and memristor circuits, which offer promising low-power characteristics. The paper also discusses future directions in VLSI design, emphasizing the role of AI-driven design tools, new materials, and hybrid analog-digital architectures in addressing the ever-growing demand for energy-efficient, high-performance circuits. As VLSI technology continues to evolve, minimizing power consumption while maintaining performance will remain a central focus of research and innovation in integrated circuit design.

Plant Pulse – Apple Disease Detection

Authors- Rinku Badgujar, Raj Ranka, Sambhav Kothari, Abhijeet Prasad

Abstract-The agricultural sector is a cornerstone of global food security, with apples being one of the most widely cultivated and consumed fruits. However, apple farming is plagued by diseases such as Black Rot, Apple Scab, and Cedar Apple Rust, which significantly impact crop yield, quality, and profitability. Traditional disease detection methods are largely manual, requiring expert intervention, and are often error-prone, time-consuming, and susceptible to subjective biases. To address these challenges, this paper presents an AI-driven Apple Disease Detection System that leverages deep learning for automated, precise, and scalable disease identification. The system employs the EfficientNetB0 architecture for high-accuracy classification and integrates Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability by visualizing disease-affected regions. Comprehensive evaluations on a diverse dataset reveal that the system achieves an accuracy exceeding 97%, demonstrating its robustness and efficacy. This innovative solution offers farmers and agricultural experts a reliable tool for early disease detection, promoting sustainable farming practices and enhancing productivity.

DOI: /10.61463/ijset.vol.12.issue6.350

A Study on Employee Attrition in Indian steel companies

Authors- Divya Singh, Assistant Professor Kavita Achchalli, Assistant Professor Dr. Janet Jyothi D’Souza

Abstract-Employee attrition in manufacturing firms, including voluntary and involuntary departures, significantly impacts productivity, costs, and performance. It involves resignations, retirements, transfers, layoffs, or terminations and is crucial for evaluating workforce stability, employee satisfaction, and work environment health. This research assesses employee satisfaction with job conditions conducted at JSSL companies using primary data. Findings indicate JSSL has cultivated a positive work environment, emphasizing satisfaction, professional development, and work-life balance, resulting in high job satisfaction and retention. Most employees feel their compensation matches their responsibilities and are content with their roles, showing no inclination to seek other employment. However, opinions on work schedule flexibility were divided, with equal satisfaction and dissatisfaction. Organizations should reassess workload expectations and schedule flexibility to enhance satisfaction and productivity.

DOI: /10.61463/ijset.vol.12.issue6.351

The Effect of Using the Self-Questioning Strategy in Developing Self-Learning Skills among Eighth Grade Students

Authors- Ahmad Adnan Ahmad Albtoosh

Abstract-This study aimed to identify the effect of the self-questioning strategy in developing self-learning skills among eighth grade students. The quasi-experimental approach was used, based on two groups and a pre-post test, from a sample consisting of (65) eighth grade students in Ajloun Governorate for the year. Academic year 2023/2024, distributed into two experimental and control groups. The research tool consisted of a scale and a guide for the teacher. The results of the study found that there were statistically significant differences between the means adjusted for the post-measurement in all dimensions of the self-learning skills scale, which were in favor of the members of the experimental group, and in light of these The results: The study recommended the need to pay attention to applying the self-questioning strategy in learning and teaching the science curriculum for the basic stage.

Local Content Policies in Nigeria’s Oil and Gas Sector: Impact on Drilling and Well Construction Operations

Authors- Chuku Dorathy Elendu Jerry, Professor Adewale Dosunmu

Abstract-This paper titled the “Impact of NOGICD Act on Nigerian Oil and Gas Industry “analyzed that the Thus, the Nigerian oil and the gas sector has witnessed gradual transformation after the enactment of the Nigerian Oil and Gas Industry Content Development (NOGICD) Act of 2010. This legislative structure has sought to rebalance the operations of the oil and gas sector towards increasing the local content in the activities involving drilling and well construction. This review analyses the effects of these local content policies to the sector’s economic, technological and operational contexts. Comparing the analyzed successes and challenges of Nigerian oil and gas industry, this paper illustrates an understanding of how those policies changed the industry’s capabilities regarding drilling and well construction activities. The review integrates past scholarship, government policy and, research findings to assess the progress and remaining deficits in meeting the goals of local content development. The article also provides some policy implications for improving the performance of local content policies in the future.

Experimental and Numerical Deep Drawing Investigations of Pure Copper Sheets

Authors- Abhishek Kosti, Associate Professor B.H. Vadavadagi, Associate Professor H. V. Bhujle

Abstract-The present work is aimed at studying the deep drawing process of pure copper sheet both experimentally and by finite element analysis technique. This is because pure copper is being considered as a promising alternative for high strength steel and aluminium within many industrial applications because of its high density, high specific strength and material renowned for its excellent electrical and thermal conductivity, as well as its ductility and malleability. The thickness of the as received pure copper sheets is 1mm. In the present investigation, circular blanks of 85 mm, 88mm, 91 mm and 94 mm diameters are deep drawn at room temperature using 40 ton hydraulic deep drawing machine. Force-displacement curves were determined for all blanks using the integrated software. Formability of the metallic sheet is assessed by determing the limiting drawing ratio. LDR is defined as the ratio of the maximum blank diameter to the diameter of the punch. The diameter of blank just before the fracture occurs in the drawn cup is the maximum diameter of the blank. Higher the LDR more will be the formability. To understand the formability behavior of copper, deep drawing experiments have been conducted on a double action hydraulic deep drawing press and the drawability has been measured in terms of limiting drawing ratio (LDR). 3D Finite element models identical to experimental set up tools were developed for the simulation of circular cup deep drawing and simulations were carried out. The experimental and simulation results were found to be in good agreement.

DOI: /10.61463/ijset.vol.12.issue6.352

Drawability Assessment of ASS 304 Sheets Used in Dairy Industry in Terms of Limiting Drawing Ratio (LDR)

Authors- Associate Professor Dr. H. V. Bhujle, Associate Professor B.H. Vadavadagi

Abstract-The current trend shows a significant increase in the application and use of sheet metal in manufacturing processes. Stainless steels are selected for dairy applications because they are resistant to corrosion, inert, easily cleaned and sterilized without loss of properties, and can be fabricated by a variety of techniques into robust structures. For this study, Austenitic Stainless Steel (ASS) 304 material was selected and cut into circular shapes of varying diameters but with a constant thickness. These circular cut materials are referred to as blanks. Before testing, a lubricant (grease) is applied to the blanks. The blanks are then subjected to a swift cup drawing test using a hydraulic deep drawing press to determine the limiting drawing ratio (LDR). During the deep drawing process, the cup is formed by the punch force. At a certain blank diameter, the bottom of the cup may fracture due to the punch force. The diameter of the blank just before the fracture occurs represents the maximum diameter of the blank. The addition of lubricant helps to analyze the impact of friction between the blank and the punch during the deep drawing process. The drawability of sheet metals is measured in terms of the LDR, which indicates the maximum deformation a cup can undergo without failure using a hydraulic press. The LDR is defined as the ratio of the maximum diameter of the blank to the diameter of the punch. Experiments were conducted on ASS 304 sheets using a hydraulic press deep drawing setup, and load-displacement curves were generated. The LDR value for 304 sheets was determined.

DOI: /10.61463/ijset.vol.12.issue6.353

Preprocessing of Hyperspectral Imaging

Authors- Associate Professor H. V. Bhujle, Associate Professor B.H. Vadavadagi

Abstract-In this study, we focus on preprocessing hyperspectral images for precision agriculture based on the Non-Local Means (NLM) technique. Hyperspectral data snaps earth objects by measuring a continuous spectrum for each pixel in a image. A hyperspectral cube contains thousands of images, each providing complementary information. Preprocessing of hyperspectral data is essential to enhance the accuracy and efficiency of subsequent analysis by minimizing various distortions. To enable comprehensive analysis, these images need to be fused into a single image that retains all critical information. Real-time sensing through ground-based remote sensors, combined with satellite remote sensing, can help bridge this time gap and enable applications across larger areas. This integration can also support the development of variable-rate input technologies for site-specific crop management, enhancing resource use efficiency and profitability. Such strategies allow for the implementation of both preventive measures and targeted field interventions. Currently, crop production and yield estimation rely on two main approaches: statistical methods and crop-growth models; Crop-growth models simulate and monitor crop growth by analyzing factors like planting date, seeding rate, crop density, variety, and weather conditions (solar radiation, precipitation, and temperature). Soil characteristics and nutrient availability are also critical inputs. We propose a hierarchical fusion model utilizing nonlocal means (NLM) filtering, known for its ability to preserve edges and structural details effectively. After image fusion, hyperspectral filtering is performed. Since noise can interfere with hyperspectral image analysis, particularly for crop classification, we introduce a noise removal method based on a multiresolution framework. Both qualitative and quantitative evaluations indicate that the proposed preprocessing techniques outperform existing methods.

DOI: /10.61463/ijset.vol.12.issue6.354

Edge Computing in Telecommunications Industry

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

Abstract-Edge computing represents a shift from traditional centralized data processing to a distributed architecture, allowing for data processing at the network edge, closer to the devices and sensors generating the data. This approach offers reduced latency, improved scalability, and enhanced security. Edge computing is a strategic opportunity for telcos. Its most significant advantage in telecom is the drastic reduction in latency. Adopting edge computing is a high priority for many telecommunications service providers as they modernize their networks. The integration of edge computing into telecom networks represents a paradigm shift that promises to revolutionize the way we connect, communicate, and collaborate in the digital age. Edge computing is poised to shape the future of telecommunications and usher in a new era of connectivity and innovation. In this paper, we explore the evolution and applications of edge computing in the telecommunications industry.

DOI: /10.61463/ijset.vol.12.issue6.355

Deep Learning and Gen AI based System for Ingredient Recognition and Recipe Insight

Authors- Mahesh Banjade, Jagadish Shrestha, Anupam Bhattarai, Ayush Acharya, Shanta Maharjan, Amul Neupane

Abstract-This paper presents “Deep Learning and Gen AI based System for Ingredient Recognition and Recipe Insight” to develop a machine learning-based system for Ingredient Recognition and recipe insight. Using YOLOV7, the presented system recognizes raw food ingredients from images and suggests recipes and dishes based on those ingredients based on the gpt-2 transformer model. Additionally, it provides recipe recommendations for the dish on the demand of the user. It can also provide recipe recommendations based on images of dishes or ingredients uploaded by the user through the vision transformer. The project focuses on improving ingredient recognition accuracy, enhancing user experience, and offering valuable culinary insights. The project is expected to develop a user-friendly system that provides accurate ingredient recognition and personalized recipe suggestions, benefiting home cooks, professionals, and food education platforms.

DOI: /10.61463/ijset.vol.12.issue6.356

Cloud-Based Telemetry for Baja Buggy

Authors- Shivam Kumar, Kushal Mitta, Tushar Suhag

Abstract-This paper examines the possibilities of implementing cloud telemetry systems for the Baja buggies, putting an emphasis on improvement of data capture, processing and dissemination in the course of competitive off-road racing. Given that Baja racing requires high performance vehicles operating under very hostile environments, then the benefits that region using cloud telemetry suffice. Key performance parameters like time to complete along with many other parameters including speed, engine temperature, fuel amount and suspension dynamics are continuously obtained by fusing different types of sensors such as IR sensors for RPM, accelerometers for dynamic measurement of the vehicle. The data coming from these sensors can be sent to a cloud platform in real time to make better decisions for the future operations. This helps to enhance vehicle performance and tactics of the battle in real time, thus winning the contesting teams. The paper presents the operational stands of the architecture of the cloud telemetry system while indicating the ways through which the data is collected, the way it is transmitted and the route it takes and the way the data is represented. Such issues as data transfer delays, difficulties in connectivity in remote parts of the world and risks of the data have been tackled as well.

DOI: /10.61463/ijset.vol.12.issue6.357

Optimizing Production from Shale Oil and Gas Reservoir through Hydraulic Fracturing and Other Stimulation Techniques

Authors- Mamukuyomi Julie Bemigho, Dr. Ekeinde Evelyn Bose

Abstract-Hydraulic fracturing is a technique that increases the well deliverability and productive life of a well. Shale gas and light tight oil have low permeability, due to which they cannot be produced commercially. Fracture treatment is used to enhance their production. Hydraulic fracturing is the fracturing of rock by a pressurized liquid. A vertical well is drilled and multiple fractures are induced from the wellbore at various azimuths to produce artificial channels. To propagate the fractures, rods of perforate guns are lowered into uncased sections of the wellbore. These guns are then fired from the wellbore and holes are broken free and perforated during the production from the initial fracture. The injectivity test for hydraulic fracturing is conducted from the acquired wireline and/or petrophysical logs. This paper presents the case history of the optimization of hydraulic fracture treatments and performance of the fracture treatment of a shale gas well. The results show that the well deliverability and life have significantly improved after the treatment, saving millions of dollars of operational cost and also increasing the field productivity by 200%.

DOI: /10.61463/ijset.vol.12.issue6.358

Soil Stabilization Using Plastic Waste and Limestone

Authors- Avneesh Singh, Dr. Sunil Sugandhi

Abstract-We studied here suggest and proved that the use of plastic waste for stabilization of soils would reduce the problem of disposing plastic waste and also reduce environmental problems. It is seen that CBR test, Proctor test, Sieve analysis are performed to check the suitability of plastic waste as soil stabilizer. Sieve Analysis gives the physical properties of the soil sample. Modified Proctor Test gives the OMC and Dry Density of soil sample. CBR Test results the Optimum Plastic content. Optimum Plastic content is the percentage of plastic added in the soil sample above which the CBR value falls. Modified Proctor Test is recommended than Standard Proctor Test because the soil which is tested will be used for road construction which requires high compaction. soil stabilization is possible by plastic waste which is a cheap method of soil stabilization.

CBR Analysis and Soil Stability Improvement Using Bituminous Stabilization

Authors- Arvind Patel, Dr. Sunil Sugandhi

Abstract-Soil stabilization is a method of improving soil properties by blending and mixing bituminous materials. Soil is used sub base and base material, if strength of soil is poor, then stabilization is usually required. Sub grade is sometimes stabilized or changed with solider soil .Soil could be black cotton or as fly ash which could fly in interaction with air. There are many stabilizers used for stabilizing the soil such as, cement, lime, bitumen, fly ash etc., in this paper bitumen as stabilizer. Bitumen mixture is expensive material in road construction. So it’s quantity play vital part to stabilize the soil. It increases the stability of soil mechanically. It does not react with soil. It is just fill the pores of soil.

Generative AI-Driven Automated News Content Generation: Integrating Web Scraping, Media Creation, and Social Media Optimization

Authors- Sandeep Chataut, Naseeb Dangi, Nabin Pakka, Nitesh Nepal, Krishna Rauniyar

Abstract-The advent of digital technologies has dramatically transformed journalism, shifting from traditional print and broadcast media to online platforms. Automation in news production is becoming a key area of innovation, aiming to reduce manual effort and enhance efficiency. This paper investigates a fully automated system for generating multimedia news content from news websites. The system combines web scraping, audio-visual content generation, and artificial intelligence to deliver ready-to-publish news content. The system at its core uses Node.js and the popular web automation library, Puppeteer, for getting news from different sources through web scraping. For creating audio from this content, a fast, local neural text-to-speech system, Piper TTS, is used to create transformation for audio generation, where FFMPEG is used for audio processing and merging. Remotion is used for creating professional-quality videos with images, text overlays, background music, background video, etc. By using generative artificial intelligence, we are able to optimize social media reach by producing relevant hashtags and relevant content metadata. It produces its output by automating a news article processing pipeline that extracts the important parts, like titles, content, source info, association media, and images, and stores them in a JSON structure. The system intelligently mixes title and content audio files together with natural flow and timing once text has been converted to speech. Next, the audio is synchronized with visual elements in the video generation process, which results in a compelling news presentation that keeps viewer engagement. Not only does it cut down on manual intervention, but it achieves a level of consistency in content output across multiple media formats, which may help news organizations transition to approaching how they create content and get it out in the digital age. This implementation was successful, therefore providing a great deal of encouragement for the future of scaling automated journalism across various languages and regions as a blueprint.

Authors- Lithika Jadav Malothu

Abstract-Toxicity prediction is a pivotal step in drug discovery, cru- cial for minimizing late-stage failures due to adverse effects of chemical compounds. This study investigates the perfor- mance of multiple machine learning models, including Light- GBM, Random Forest, and XGBoost, in combination with different molecular featurizers such as Extended Connectiv- ity Fingerprints (ECFP), ChemBERTa, and Graph Convolu- tional Networks (GCN). Using the Tox21 dataset as a bench- mark, we demonstrate that the Random Forest model paired with ChemBERTa achieves superior predictive accuracy and interpretability for toxicity prediction tasks. Additionally, our analysis identifies key substructures, such as aromatic rings and halogenated groups, that significantly influence toxic- ity predictions, emphasizing the role of appropriate model- featurizer combinations in enhancing prediction accuracy and interpretability. This work contributes to the growing body of research in cheminformatics by providing insights into the selection of optimal model-featurizer pairs, which can poten- tially guide safer and more efficient drug development pro- cesses.

DOI: /10.61463/ijset.vol.12.issue6.359

Exploring Machine Learning Approaches for Detecting Android Malware: A Survey

Authors- Hiral Patel, Associate Professor Dr.Mukta Agrawal

Abstract-The number of malicious applications targeting the Android platform has significantly expanded with the rise in usage of mobile devices. Malware is now exceedingly difficult to detect because of how carefully it is coded. The daily increase in the volume of malware has rendered manual procedures ineffective for detecting it. Traditional signature-based detection techniques, which rely on recognized malware patterns, frequently fall short of the task of identifying newly developed malware variants. The ever-evolving landscape of Android malware can be combated using machine learning techniques, which are more dynamic and flexible. This study provides a thorough analysis of machine learning-based methods for Android malware detection.

DOI: /10.61463/ijset.vol.12.issue6.360

A Correlation between Visiting Reading Lounges for Leisure With Respect to Stress Alleviation

Authors- Ar. Dimple Khilnani, Dr. Tanisha Dutt De

Abstract-Stress is a major public health concern that has a physical and psychological impact on numerous people. Many people can cope with minor levels of stress, and some even become more productive when under distress. Stress, on the other hand, becomes problematic when it becomes overwhelming and a person begins to experience negative consequences such as increased anxiety and depression, multiple somatic complaints with no organic cause, or engaging in unhealthy behaviors such as smoking, poor dietary habits, and poor sleep habits. The failure of an individual to adequately manage stress can have a cascade of negative consequences on both a personal and professional level. Stress is supposed to begin in the school and last throughout one’s professional career. It would be beneficial to encourage the use of stress management techniques from the start of a student’s education, thereby assisting them in laying a foundation on which healthful behaviors can be built. While various methods have been investigated to help relieve stress, reading is one of the most effective stress management practices. If provided a suitable setting to concentrate peacefully, such as found in libraries, reading can become even more enjoyable and sanative.

DOI: /10.61463/ijset.vol.12.issue6.361

Sound Bomb Technology

Authors- Deepak Singh

Abstract-This paper explores the concept, design, and potential applications of a “sound bomb,” a device that utilizes high-intensity acoustic energy as a disruptive or destructive force. By generating sound waves at extreme decibel levels (typically above 160 dB), a sound bomb can produce shockwaves capable of causing physical damage, disorientation, or incapacitation. The research delves into the underlying physics of sound wave propagation, the creation of shockwaves at high pressures, and the conversion of acoustic energy into a blast-like effect. The study also examines the potential uses of sound bombs in military, law enforcement, and crowd control, alongside their ethical implications and safety concerns. Experimental methods and theoretical models are presented to assess the feasibility and limitations of sound bombs as non-lethal acoustic weapons.

DOI: /10.61463/ijset.vol.12.issue6.362

A Study on Graph Labelling, Graph Coloring,their Types and Applications

Authors- Research Scholar Rouf Gulzar, Assistant Professor Priyanka Bhalero

Abstract-Graph theory is an interdisciplinary field that lies at the interface of computer science and mathematics. It studies graphs, which are engineering Structures used to model two dimensional interactions inside given objects by the method of graphical representations and mathematical derivations. The study of graph theory is applied in many fields, including biology, computer science, social sciences, and even transportation networks. Many mathematical theorems and models are proven using graph theory for correct comprehension and future study. Graph labeling is one of the main areas of graph theory. In this review paper, We examine several distinct types of graph labeling , including Graceful, Prime, Edge, Harmonious, and Graph coloring, as well as their mathematical requirements and characteristics. Additionally, we will explore some of the applications of graph labeling in important technological fields like automatic routing, communication network addressing, X-ray crystallography, and coding theory. Applications of graph theory include the creation of security keys, the segmentation of brain MRIs, the detection of tumors using cut sets, virus graphs, and their use during the COVID-19 pandemic. One of the most crucial areas for graph theory research is the assignment of color to different graph constituents. Applications in the sciences, medical sciences, computer engineering, electronics and communications, electrical engineering, network theory, artificial intelligence and machine learning, psychology, and economics are numerous. There are still a lot of unanswered questions, and mathematicians and academics from all over the world are working on it. In this study, we examine graph coloring, coloring types, graph-coloring theorems and axioms, and its applications.

DOI: /10.61463/ijset.vol.12.issue6.363

Impact of Medication Adherence on Patient and Health OutcomesImpact of Medication Adherence on Patient and Health Outcomes

Authors- Mr. Mangesh Ashok Lahane, Dr. Shivshankar Mhaske, Professor Shubham Wankhede, Mr. Krishnakant Laate, Miss Vaishnavi Dhondge

Abstract-Utilization of medications as prescribed impacts the relative effectiveness of evidence-based medical treatments. Regaining control of the magnitude of the clinical effects of proven innovations to advance healthcare is necessary. This review examines recent studies examining the magnitude of the effect of full adherence to drug therapy on the relative risk of two outcome measures: mortality and cardiovascular events. Studies conducted to identify the impact of medication non-adherence are included throughout. Increased awareness of the significant impact of non-adherence on medical practice can contribute to the creation of effective counseling and intervention strategies to address the problem sooner rather than later.

DOI: /10.61463/ijset.vol.12.issue6.364

Drone Swarm Coordination Using IoT for Large-Scale Agricultural Monitoring

Authors- Assistant Professor Dr. Pankaj Malik, Shantanu Tiwari, Kritika tomar, Kaustubh Kalambkar, Dhanashri Yeole

Abstract-The increasing demand for sustainable and efficient agricultural practices has led to the integration of advanced technologies such as drones and IoT in precision agriculture. This paper presents a novel framework for Drone Swarm Coordination enabled by IoT for large-scale agricultural monitoring. The proposed system leverages IoT networks for real-time communication, data aggregation, and task synchronization among drones, ensuring seamless coordination and enhanced monitoring efficiency. Key innovations include adaptive path-planning algorithms to optimize field coverage, energy-efficient operation strategies to extend mission durations, and robust communication protocols for real-time data exchange. Simulated and field-tested results demonstrate the system’s potential to improve crop health assessment, pest detection, and soil condition analysis, providing actionable insights for farmers. By addressing challenges such as scalability, communication latency, and resource constraints, this study lays the groundwork for a transformative approach to large-scale agricultural monitoring using drone swarms and IoT.

DOI: /10.61463/ijset.vol.12.issue6.365

Genetic Based Environmental Image Watermarking Using Frequency Feature

Authors- M. E. Scholar Shalu Singh Pawar, Professor Aparna Kushwaha

Abstract-Environmental images are highly susceptible to manipulation, which can occur either intentionally or unintentionally. Such tampering can take place at various stages of image processing, both within and outside of communication networks. These stages include image examination, extraction, and transmission, where vulnerabilities may be exploited, leading to potential alterations in image integrity. In this paper, a comprehensive survey of various methodologies, techniques, and frameworks adopted or proposed by researchers in the field of image watermarking is presented. The study explores the foundational requirements of a robust watermarking algorithm, emphasizing that these algorithms should not only be functional but also effective in safeguarding the embedded information. A detailed account of the image features utilized in watermarking, as identified and proposed in the works of various scholars, is provided to offer insights into the key attributes leveraged for watermark embedding. To ensure the reliability of the embedded watermark, it is crucial for the embedded image to exhibit robustness against a wide range of potential attacks. The paper also includes a compilation of common image attacks, detailing their nature and the challenges they pose to watermarking systems. This serves as a guide for evaluating the performance and resilience of watermarking techniques under diverse conditions.

Wireless Charging Networks for IoT Livestock Tracking In Rural and Remote Areas

Authors- Assistant Professor Dr. Pankaj Malik, Hitika Ghanani, Priyansh jain, Piyush Gurjar, Vaidika Joshi

Abstract-Livestock tracking in rural and remote areas faces significant challenges, particularly in maintaining the energy requirements of IoT devices over extended periods and vast terrains. Traditional power solutions, such as battery replacement or solar charging, are often impractical due to logistical constraints and environmental limitations. This paper proposes a novel framework for leveraging wireless charging networks to sustain IoT-based livestock tracking systems in such regions. The proposed system incorporates advanced wireless power transfer (WPT) technologies and energy-efficient IoT devices, ensuring uninterrupted functionality. The study evaluates the feasibility and performance of the wireless charging network through simulations, focusing on key metrics such as charging efficiency, device uptime, and cost-effectiveness. Results demonstrate the system’s potential to significantly reduce maintenance efforts while enhancing the reliability of livestock tracking. This research provides a sustainable and scalable solution, paving the way for improved livestock management and agricultural productivity in underserved areas.

DOI: /10.61463/ijset.vol.12.issue6.366

Coir Geocell System for Slope Land Stabilization and Cultivation with Brinjal (Solanum Melongena) Crop

Authors- Abhishek C., Sibi Joy, Jithu P. Ajith, Aneesh R.

Abstract-Coir geotextiles is one among the widespread sustainable and environmental friendly solutions adopted for checking soil erosion and stabilization of slope. In such applications, grasses are typically used as vegetation. If cultivation of crop as vegetable could be adopted in such sloping regions, it would be highly beneficial as more area would be brought under cultivation. Application of coir geotextile alone would not suffice the need as geotextile as such cannot contain the manures and other amendments which are to be incorporated for the crop along the slope. Coir geocells can play a significant role in stabilization of slope protecting it from soil erosion along with cultivation. The study focuses on the cultivation of brinjal (Solanum melongena) in coir geocell on a sloping terrain of 40% and the assessment of soil erosion from the treatment plot in comparison with a control and coir geotextile laid plot. The soil loss from the treatment plots were estimated using multislot divisor. The crop was evaluated for various parameters for assessing the growth of the crop in coir geocell. The study revealed that the coir geocell is effective in controlling the soil erosion along with the cultivation of crop in them. It was found that the total soil loss from the coir geocell during the period of study was nearly one tenth of the total soil loss from the control plot during the same period. The crop exhibited good growth on the sloping terrain.

DOI: /10.61463/ijset.vol.12.issue6.367

Exploring Advanced Machine Learning Approaches for High-Quality Image Restoration and Reconstruction

Authors- M. Tech Scholar Shubhangi Mansore, Professor Kamlesh Patidar

Abstract-Image restoration plays a significant role in various modern applications. In the field of image processing, the process of “image restoration” is essential for correcting distortions and improving image quality. It involves estimating and mitigating the effects of noise and blur that degrade an image. Understanding the specific types of noise and blurring processes that affect an image is crucial for successful restoration. Having prior knowledge about the image, including the nature of the distortions, is vital for effective restoration. Images often require restoration due to various atmospheric and environmental factors. These factors introduce noise and blur, necessitating the use of diverse image processing techniques to restore the image’s original quality. The process of image restoration involves accurately estimating the noise and blur present in the image and applying appropriate methods to correct them. In modern applications, image restoration is widely used. For instance, radar technology utilizes image restoration to enhance and recover images, and it is also employed in the preservation of museum artifacts. This article provides an overview of current image restoration techniques. The proposed approach evaluates performance metrics such as frequency, PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), and SSIM (Structural Similarity Index) using datasets like Kodak24, CBSD68, Urban100, and LIVE. For example, the PSNR values for these datasets are 27.24, 29.38, 30.04, and 30.91, respectively. The MSE values are 367.56, 224.88, 193.1, and 158.02, while the SSIM scores are 0.8690, 0.9337, 0.9432, and 0.8008, respectively. This article highlights the effectiveness of contemporary image restoration techniques in enhancing the quality of distorted images.

Novel meta-Heuristic Optimization Based Switching Angles Tuning for Improving Power Quality Parameters in Capacitor Run Induction Motor

Authors- N.Murali, M.Annamalai, S.Gobi Mohan, R.Vidhya Prakash

Abstract-Industry incorporates CRIM based VVC to reduce the power quality deterioration under variable speed control operation. To sustain the minimal power quality deterioration in the VVC power electronics system, a bio-inspired meta-heuristic algorithm called Sperm Swarm Optimization (SSO) algorithm is proposed to tune the switching instants. To obtain the optimal gain for the switching instants, the VVC system adjusts the CRIM to operate at better power quality. The SSO algorithm inspires human anatomy based swarm techniques to improve the VVC performance by enhancing the power quality parameters. The effectiveness of the proposed algorithm for the VVC system applied to CRIM is verified with the MATLAB Simulink model. The proposed SSO algorithm achieves a better total harmonic distortion below the IEEE standard limits under different load conditions. The proposed algorithm provides the good power quality and better speed control comparable to other optimization algorithms.

DOI: /10.61463/ijset.vol.12.issue6.368

Demo Model in Smart Home Application Design

Authors- Nguyen Tai Tuyen, Nguyen Dinh Thanh

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

DOI: /10.61463/ijset.vol.12.issue6.370

Plant Phenotyping Using Convolution Neural Networks

Authors- Prasanth Yadla

Abstract-Plant phenotyping of a plant is describing the visual or observable characteristics such as height, biomass, leaf shape and so on. In this project we are using phe- notyping to find the water requirement of the plant [1–3].The number of leaf tips, width of the plant and collars of the plant can be phenotypic indicators of water stress. Our aim is to apply Image Processing and Neural networks to given images to detect leaves and col- lars as key points. This would help in identifying the phenotype features of the plant in an automated fashion. Specifically, we used the Tensorflow Object Detection API, an open source framework built on top of Tensorflow to localize and identify leaf tips and collars. We have achieved over 55% accuracy in detecting the leaf tips using Inception Network out of the total manual count and also detected collars to a reasonable accuracy.

DOI: /10.61463/ijset.vol.12.issue6.371

Intellectual Outlier Identification in Network Anamoly Using Deep Reinforcement Learning

Authors- Assistant Professor Kiruthika.S, Danushya.V, Jananisri.V, Jayashree.M, Juniper Zibiah.F.C

Abstract-The rapid expansion of Internet of Things (IoT) devices has intensified the challenge of securing network infrastructures from several attacks. Traditional security measures often struggle with the diverse and large-scale nature of IoT traffic. This paper proposes an advanced approach to detection and mitigation by integrating Deep Reinforcement Learning (DRL) with the VGG16 convolutional neural network. The system employs VGG16 to extract and analyze features from network traffic, identifying potential anomalies indicative of attacks. These features are then processed by a DRL agent, which learns and optimizes defensive strategies based on real-time feedback. This adaptive learning approach enhances detection accuracy and response effectiveness by continuously updating its strategies based on evolving attack patterns. The system’s dynamic nature allows it to scale with the growing complexity of IoT networks, offering a robust solution to improve network security. This paper details the integration of DRL and VGG16, presents experimental results demonstrating the system’s effectiveness, and discusses its potential to significantly advance IoT security management.

DOI: /10.61463/ijset.vol.12.issue6.372

A Study on Graph Labelling, Graph Coloring, their Types and Applications

Authors- Research Scholar Rouf Gulzar, Assistant Professor Priyanka Bhalero

Abstract-Graph theory is an interdisciplinary field that lies at the interface of computer science and mathematics. It studies graphs, which are engineering Structures used to model two dimensional interactions inside given objects by the method of graphical representations and mathematical derivations. The study of graph theory is applied in many fields, including biology, computer science, social sciences, and even transportation networks. Many mathematical theorems and models are proven using graph theory for correct comprehension and future study. Graph labeling is one of the main areas of graph theory. In this review paper, We examine several distinct types of graph labeling, including Graceful, Prime, Edge, Harmonious, and Graph coloring, as well as their mathematical requirements and characteristics. Additionally, we will explore some of the applications of graph labeling in important technological fields like automatic routing, communication network addressing, X-ray crystallography, and coding theory. Applications of graph theory include the creation of security keys, the segmentation of brain MRIs, the detection of tumors using cut sets, virus graphs, and their use during the COVID-19 pandemic. One of the most crucial areas for graph theory research is the assignment of color to different graph constituents. Applications in the sciences, medical sciences, computer engineering, electronics and communications, electrical engineering, network theory, artificial intelligence and machine learning, psychology, and economics are numerous. There are still a lot of unanswered questions, and mathematicians and academics from all over the world are working on it. In this study, we examine graph coloring, coloring types, graph-coloring theorems and axioms, and its applications.

DOI: /10.61463/ijset.vol.12.issue6.373

Classifying DDoS Attacks Using Machine Learning

Authors- Syed Nabiel Basha K, Associate professor Dr S R Raja

Abstract-Distributed Denial-of-Service (DDoS) attacks pose a significant threat to the stability and security of network infrastructures, resulting in severe disruptions and economic damage. This work uses machine learning approaches to classify and effectively mitigate DDoS attacks. Utilizing a labeled dataset of network traffic, important attributes such as packet flow rates, source entropy, and protocol distribution are investigated for training and testing several machine learning models. Paper on Classifier Performance Comparison: Decision Trees, Random Forests, and Neural Network Detecting Normal vs. Malicious Traffic. Results The results indicate the best model was [Random Forest] at a very high accuracy of 100%. Real-time attack detection highly depends on feature engineering with hyperparameter tuning. This scalable and efficient research contributes toward network resilience by ensuring early detection of DDoS. Future work may involve the dynamic retraining of the model to capture changing attack vectors and will also aim to integrate this solution with intrusion prevention systems.

DOI: /10.61463/ijset.vol.12.issue6.374

Virtual Try-on: Enhancing Fashion

Authors- Sanika Sartape, Gayatri Sonune, Vijay Palsaniya, Kovil Tidke, Navnath Kale

Abstract-This paper explores the design and implementation of a virtual clothes try-on system enhanced by AI-driven recommendation algorithms. Using pose estimation tools like OpenPose and MediaPipe, the system accurately detects user body keypoints to position and scale virtual garments realistically. OpenCV is employed for garment overlay and image processing, ensuring a seamless and visually appealing experience. The paper delves into the technical methodologies, practical applications, and potential challenges of deploying such systems. Through this work, we aim to contribute to the growing field of fashion technology, offering insights into how virtual try-on platforms can transform fashion industry.

Cr (VI) Remediation with Economical Adsorbents: A Path to Environmental Sustainability

Authors- Shilpa S.Patil, Dr.S.V.Anekar

Abstract-Hexavalent chromium Cr(VI) is a highly toxic heavy metal frequently found in Electroplating,Tanneries,Textile & paint industrial wastewater. This study explores the use of waste foundry sand (WFS), Bael fruit shell (BFS), and copper benzene-1,3,5-tricarboxylate (Cu-BTC) metal-organic frameworks (MOFs) for Cr(VI) adsorption. Batch adsorption experiments were conducted under varying pH, Cr(VI) concentrations, contact time, and adsorbent dosages. Characterization of the adsorbents was performed using SEM analysis. The results showed that the Cu-BTC MOF had the highest adsorption capacity, with significant synergies observed when combined with WFS and BFS. These findings indicate that integrating waste materials with MOFs can provide a cost-effective and efficient method for environmental remediation.

DOI: /10.61463/ijset.vol.12.issue6.376

Analysis of Trends in Edible Oils

Authors- Pallavi B Pawadigoudra, Shweta J Sabannavar Assistant Professor

Abstract-Edible oils contain a high level of fatty acids such as saturated fatty acids as well as mono and polyunsaturated fatty acids. Due to there high oxidation stability, they have been extensively used in processing and cooking. FAAs(Fatty acid amines) are endogenous lipid molecules that exhibit various physiological activities. They are usually present at nanomolar levels in biological samples. A method for the Qualitative and Quantitative determination of six FAAs (linoleamide, linoleoyl ethanolamide, oleoyl ethanolamide, palmitic amide, oleamide, and octadecanamide) in edible vegetable oils was established.

DOI: /10.61463/ijset.vol.12.issue6.377

Sonicpix.ai: Bridging the Gap between Audio and Visual Realms Using AI

Authors- Akshat Balmukund Mehta

Abstract-Sonicpix.ai introduces a groundbreaking approach to integrating auditory and visual modalities through artificial intelligence. By combining OpenAI Whisper’s transcription capabilities with DALL•E’s generative technology, the platform translates auditory inputs into visually meaningful outputs in real time. This innovation serves various domains, including creative industries, accessibility solutions, and law enforcement, offering transformative potential. With its user-centric design and advanced AI features, Sonicpix.ai sets a new standard for sensory integration, paving the way for unprecedented advancements in this field.

DOI: /10.61463/ijset.vol.12.issue6.378

Simulation of Sensor Data Processing via Cellular Neural Networks on an IoT Platform

Authors- Nguyen Tai Tuyen

Abstract-This page focuses on the development of a multifunctional wireless information sensor module designed for AIoT systems, aiming to improve the efficiency of data collection, processing and analysis. The unique feature of the module lies in the integration of a Cellular Neural Network (CeNN) algorithm, which enables advanced data processing right at the sensor. The module was tested in two typical applications: fire warning and surface water monitoring. The results showed outstanding performance in terms of accuracy, data transmission speed and energy efficiency, confirming the potential for practical deployment in many fields.

DOI: /10.61463/ijset.vol.12.issue6.379

Optimization of Oil Volume through Computational Fluid Dynamics Analysis for Gearbox Systems

Authors- Girish Jadhav, Nitin Tawhare

Abstract-This oil flow research discusses the type of lubrication system used for the gears and bearings inside a gearbox. It also emphasizes how crucial gearbox lubrication is. The effects of both too much and too little lubrication on a gearbox system are thoroughly examined. Additionally, it will detail the many software programs that can be used to do Computational Fluid Dynamics (CFD) as well as the program that is used to analyse the selected gearbox’s CFD. Finally, studies also demonstrate how altering the amount of oil affects its flow.

Apaak.ai: Revolutionizing Audio Processing and Speaker Diarization with Enhanced Sentiment Analysis and Conversational Skills

Authors- Akshat Balmukund Mehta, Arzoo Gupta, Aashna Shah, Preet Marathe

Abstract-This paper is an overview of APAAK.ai, an application that uses audio processing to diarise conversations between individuals using audio recordings or telephonic conversations. It covers the framework on which the application is primarily built and the various objectives of the app, including but not limited to, which part of the conversation is talked through by which individual respectively, what is being said in the conversation, the sentiment synopsis of the conversation, the overall sentiment analysis and the various steps that can be taken to improve the conversation. Furthermore, the scope of the application, areas of improvement, metric analysis and field-specific applications are also mentioned.

DOI: /10.61463/ijset.vol.12.issue6.380

A Novel Approach for Machine Learning Algorithms

Authors- Research Scholar G.Divya, Associate Professor Dr.V.Maniraj

Abstract-Machine learning (ML) is the field of study focused on algorithms and statistical models that enable computer systems to perform specific tasks without needing explicit programming. These learning algorithms are employed in numerous applications that we interact with daily. For instance, when using a search engine like Google to browse the internet, one of the reasons it works effectively is because of a learning algorithm that has been trained to rank web pages. These algorithms find applications in areas such as data mining, image processing, predictive analytics, and more. The primary benefit of machine learning is that once an algorithm learns how to handle data, it can perform tasks automatically. This paper presents a concise overview and future potential of the wide-ranging applications of machine learning algorithms.

Characterisation and Elucidation of Wheat

Authors- Kavya N. Madder. Dr.Shweta J Sabannannar, Assistant Professor

Abstract-Gluten free bread is highly acceptable to the typical consumer to be diagnosed with celiac disease and gluten intolerance. The main problem with the gluten free bread is compromised properties in terms of texture ,shape, size with other quality. Attributes, which are due to the lack of gluten during bread doughing. In that case mixed wheat flour is a better option. Wheat is unique in that when its flour is mixed with a limited amount of water it forms visco elastic dough. These visco elastic properties enable the dough to hold the CO2 gas produced by yeast or baking powder and to expand to leavened baked products like bread with a light aery texture. These properties of the wheat dough are attributed to its gluten proteins. Rice has no such proteins. Hence, the production of baked goods from rice that have similar texture and eating quality to those from wheat is a real challenge. The current study has been made to blend the blend of rice and wheat flour and to study the quality analysis of prepared mixed wheat flour.

DOI: /10.61463/ijset.vol.12.issue6.381

Innovation Pédagogique et Expérimentation : Lorsque la créativité Rencontre La conception Dans L’enseignement De L’architecture

Authors- Kaouthar Zair

Abstract-L’enseignement de l’architecture permet de développer des approches pédagogiques interactives et complémentaires de création, de conception et de mise en œuvre avec des méthodes variées. En effet, l’atelier de projet est un laboratoire de recherche et d’expérimentation sur l’architecture dans son écriture extérieure et intérieure en tant que discipline intégrant la forme et l’espace. Dans le cadre d’un ensemble de stratégies et de méthodes conçues afin de développer une pensée créative dans la conception du projet architectural, nous présentons dans cet article, les modèles pédagogiques que nous avons réfléchis, mis en place puis expérimentés dans l’enseignement du premier cycle de formation en architecture.

DOI: /10.61463/ijset.vol.12.issue6.382

Bridging Financial Reporting and Business Innovation: Strategies for Media Operations in the Age of AI

Authors- Lihui Zhang

Abstract-Over the last ten years, digital transformation has had an unparalleled impact on all corporate sectors. We are about to enter a time when enterprises, society, and consumers are all undergoing significant digital change. As a result, in recent years, organisations from a variety of industries have made digital transformation a top priority. Scholarly opinions on the idea and components of digital transformation vary, but it is generally agreed that it has a big influence on customer choices and calls for organisational change. The need for digital transformation and its impacts on customers have been further hastened by recent difficulties like the COVID-19 epidemic. This calls for an editorial viewpoint on this crucial subject in order to create a research agenda for the future that takes into account all of the facets of digital revolution. This editorial perspective’s goal is to examine research on digital transformation from a multidisciplinary perspective and offer insights into a number of important areas that are expected to accelerate this change, including the Internet of Things, social media, mobile apps, artificial intelligence, augmented and virtual reality, the metaverse, and corporate digital responsibility. Introduction, role, significance, multifarious influence, and conclusions are the lenses through which each area is examined. There are recommendations for future research directions.

The Business of Biotech: Strategic Operations for Advancing Medical Diagnostics

Authors- Janet Zhang

Abstract-Advancements in artificial intelligence are revolutionizing the landscape of neurosurgery by providing promising solutions to risk mitigation and enhanced patient outcomes. This article presents an innovative AI-based tool developed to aid neurosurgeons in identifying and diagnosing brain cancers accurately. The system is trained using deep learning algorithms on an extensive dataset of brain MRI images for the segmentation and classification tasks. Using the performance of the tool to estimate the diagnostic accuracy, separate MRI images were used; the median Dice similarity coefficient was 0.87, which reflects strong diagnostic accuracy. Aside from its technical effectiveness, the user experience evaluation conducted in the Neurosurgery Department of the University Hospital Ulm noted considerable improvements both in precision and operational efficiency. Besides that, it reduced users’ cognitive load and the stress level caused by use, further supporting the case for streamlining clinical work. Notable here is the fact that the system has personalized suggestions, which also shows an ability to adjust to numerous surgical scenarios. These findings, therefore, represent AI in the service of the neurosurgeon in changing neurosurgical practice; in that technology will not only help sharpen the acuity of diagnoses but also upgrade the quality of surgical procedure and outcomes. Researches are, however, moving towards using this AI tool to connect it with the robotic surgical systems for conducting minimally invasive surgeries, thus ensuring maximum benefits to both the patient and healthcare providers. This integration represents a critical step toward optimizing operational strategies in medical diagnostics and surgery and has broader impacts on delivery in healthcare.

Cloud Computing in the Pharmaceutical Industry

Authors- Professor Matthew N. O. Sadiku, Assistant Professor Chandra M. M. Kotteti, Paul A. Adekunte, Janet O. Sadiku

Abstract-Cloud computing is a model that provides easy and convenient access to networks everywhere. In recent years, cloud computing has emerged as a valuable technology, significantly impacting various sectors including the pharmaceutical industry. Cloud computing represents a revolutionary opportunity for pharmaceutical companies. It is transforming the pharmaceutical industry significantly. Cloud computing in the pharmaceutical industry refers to the use of remote servers on the Internet to store, manage, and process pharmaceutical data and applications. It provides Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-a-Service (PaaS). It offers several benefits, such as scalability, cost-effectiveness, increased accessibility, and collaboration, which have made it an attractive solution for pharmaceutical companies. In this paper, we will explore the main use cases for cloud computing in pharmaceutical industry.

Optimized Design of Adams Pulse Motor Integrated with Arduino Microcontroller for Ease of Operations

Authors- Nachiketh Nadig, Aditya Gautam, Navya Moolrajani, Ajin Branesh Asokan, Dr. Gurmail Singh Malhi

Abstract-The search for more sustainable and efficient propulsion & power systems has become a focal point in aerospace engineering, driven by the necessity to reduce carbon emissions and mitigate environmental impact. In response, this paper explores the optimized fabrication of an Adams pulse motor integrated with an Arduino microcontroller, aiming to enhance performance while addressing the specific demands of aerospace applications. The Adams pulse motor, renowned for its simplicity and efficiency, serves as the basis of this study. Operating on the principles of electromagnetism, the motor comprises a stator and rotor configuration. When given electricity in pulses, the magnetic fields generated by stators interact with the rotor, inducing rotational motion. This simplicity makes the Adams pulse motor an attractive candidate for aerospace propulsion systems, where reliability and fuel efficiency are very important. Integration with an Arduino microcontroller introduces sophisticated control capabilities, enabling real-time adjustment of motor parameters such as speed, torque, and direction. Furthermore, the Arduino facilitates sensor feedback integration, allowing for autonomous operation and fault detection. This level of control not only enhances performance but also contributes to overall system reliability. One of the key highlights of this study is the application of the optimized Adams pulse motor integrated with an Arduino microcontroller in the aerospace industry. Aircraft propulsion systems are undergoing a shift towards more sustainable alternatives. The integration of the Adams pulse motor with Arduino aligns with this motive, offering a greener and more efficient propulsion/ power solution for aerospace applications. In conclusion, the optimized fabrication of Adams pulse motors integrated with Arduino microcontrollers holds tremendous promise for the aerospace industry. As the demand for sustainable propulsion solutions continues to grow, this integrated system presents a compelling option for future aircraft propulsion systems.

DOI: /10.61463/ijset.vol.12.issue6.383

A Novel Forensic Analysis Approach to Certify Provenance of Data in Cloud Leveraging Two Factor Integrity Checks

Authors- Assistant Professor Raj Kumar T, Pofessor Sobhana N

Abstract-The Cloud has emerged as the new innovative computing paradigm in both data computation and storage as it is based on flexible pay and provisioning models. Storage of Cloud played a vital role in back up desktop user data, to host all shared scientific and mathematical data, to hoard web applications information and also to serve web based pages. Cloud based systems are also useful for faster processing and retrieval of data. Private Cloud usage has now become an essential ingredient for smaller organization network due to its distinguishable characteristics from other storage methods. Vulnerabilities in Cloud based systems impose threats to cloud storage and may affect the organization data maliciously. Forensic Acquisition and analysis of preserved data and files are essential for identifying and detecting the threats. Ensuring the integrity of Data is also vital. Data Analysis and threat identification in Cloud imposes an essential ingredient namely data provenance. Provenance of data, a meta-data defining the source history of data, is crucial for the endorsement of reliability, accountability, integrity, transparency and confidentiality of digital entities in a cloud. Hash values generation and authentication by multiple ways guarantees integrity of stored data. Cloud based storage contains large amount of artefacts useful for forensic investigators for forensic examination and analysis in the event of any unauthorised access and attack on the system. But it lacks authenticity and applicability of forensic principles when artefacts are placed before court of law. This accentuates the need for a digital forensic contrivance that can be accepted in the court of law which can satisfy and strictly follow the chain of custody forensic principles. This work presents the forensic acquisition of data provenence from a private cloud system following the forensic principles of preservation, acquisition, examination & analysis. A method is proposed namely, ProveCloud which will follow the hash value authentication, application of forensic principles and generate the provenance of data from the Private Cloud which would be helpful for identifying threats from a Cloud based system.

DOI: /10.61463/ijset.vol.12.issue6.384

Maximum Power Transmission in a Power Distribution Network Using Facts Devices

Authors- Chinedu James Ujam

Abstract-The utilization of Flexible Alternating Current Transmission System (FACTS) devices has garnered significant attention in modern power distribution networks for enhancing system efficiency and stability. This paper investigates the feasibility of achieving maximum power transfer in power distribution networks through the strategic deployment of FACTS devices. Through a comprehensive review of relevant literature and theoretical analysis, the paper evaluates the potential benefits and challenges associated with FACTS devices in maximizing power transfer capability. The study highlights the role of FACTS devices in voltage control, reactive power compensation, and impedance regulation to optimize power flow and improve network performance. Furthermore, the paper explores various FACTS technologies, including Static VAR Compensators (SVCs), Static Synchronous Compensators (STATCOMs), and Thyristor-Controlled Series Capacitors (TCSCs), and discusses their application scenarios and operational characteristics.

Context-Aware Detection Networks for Enhanced Women Safety and Threat Prevention

Authors- Associate Professor Sarvagya Jain, Vinit Dubey, Nishesh Patil

Abstract-In order to increase the frequency of security threats to women in urban areas, advanced analysis is needed for real-time monitoring and intervening. In the current observation system, specific threats can often be detected, and context information can be analyzed in real time. In this paper, we propose a robust detection algorithm specifically designed for women’s safety that significantly improves detection accuracy and response time by using a combination of scene-based context and deep learning techniques. Our approach includes: (1) highly accurate gender-classified person detection for real-time analysis of gender distribution, (2) recognition of anomalous scenarios such as a woman alone at night or surrounded by a group of women, and (3) gesture-based SOS detection. The integration of contextual data with convolutional neural networks (CNNs) facilitates early alerts and trend analysis to support proactive interventions. By identifying hotspots and providing ongoing updates, our model enhances both public safety and resource allocation for law enforcement. A prototype of the complete system is available to the community.

DOI: /10.61463/ijset.vol.12.issue6.919

Smart Nutrient Management in Anaerobic Digestion: A CNN-Powered Strategy for Optimizing Trace Elements in Biogas Production

Authors- Srinivas Kasulla, S J Malik, Anjani Yadav, Gaurav Kathpal, Fredrick Kayusi

Abstract-AD is one of the most significant biological processes that convert organic waste into biogas. It provides renewable energy by cutting down on waste management and environmental problems. Trace elements are part of the component, that is used to maximize microbial activities in the AD system because some of the activities were uniquely essential to perform a function within the machine of enzymes involved in methanogenesis; consequently, any deficiency or imbalance lowers yields of biogas (Speece and Parkin, 1987; Menon et al., 2017). The present study integrates implementing an optimization trace element supplement model in the framework of anaerobic digestion. They proposed trace element concentration forecasting and handling as an optimization means of enhancing efficiency in biogas generation through real-time AD operations. Their results with the CNN model are tremendous in predicting the optimal trace element concentrations that went along with increases in the rates of biogas production among different feedstocks. The main outcome of the research further showed that the yield of methane and system stability increased significantly upon optimizing trace elements supplementation through the CNN-based approach (Wang et al., 2020; Zieliński et al., 2019). Optimizing trace elements supplementation using CNN has indicated a promising platform for upscaling into an industrial biogas system with an effective data-driven decision that is sure to improve upon nutrient management. Further expansion of the model’s applicability may lead to further extension of the different feedstocks and variable environmental conditions and further introduce sustainability and efficiency in biogas production practices (Azbar et al., 2019; Kasulla and Malik, 2021). The proposed CNN model, therefore, is better as it leads to higher accuracy, precision, and lower time complexity compared with the conventional methods due to their far more accurate, dynamic, and real-time optimization of trace element supplementation and hence of utmost importance for the maximization of the production of biogas using a wide variety of feedstocks and therefore leads to an even more efficient and adaptable method for industrial-scale digestions.

DOI: /10.61463/ijset.vol.12.issue6.385

Towards Sustainable Biogas from Sugarcane Bagasse: Synergistic Effects of Pre-treatment Methods on Methane Production

Authors- Srinivas Kasulla, S J Malik, Fredrick Kayusi, Anjani Yadav, Gaurav Kathpal

Abstract-New depleting fossil fuel needs coupled with changes in the climate scenario open critical demands for renewable sources of energy. Among these, one finds biogas, a product of renewable energy harvested from organic wastes through anaerobic digestion. The aim of the current research work therefore deals with producing a detailed review concerning the synergistic interactions between several pre-treatment techniques that include both thermo-chemical and enzymatic types regarding the possible production of biogas from sugarcane bagasse. Generally, it searches for optimized combinations of these pre-treatments directed at the highest possible yields of methane from all the potentially available lignocellulosic biomass. It looks for optimized strategies that may indicate further prospects for better biogas within a wider perspective: the better alternative to sustainable energy production and waste management (Hernández-Beltrán et al., 2019; Prasad et al., 2024). This can be seen clearly in the experimental data; any blend of different pretreatment categories, such as Steam Explosion followed by Acid Hydrolysis or Alkali Treatment supplemented with Enzyme Addition, significantly increases biogas and methane production in comparison to all scenarios of single treatments. Methane contents have been found to be better uniform for all these combinations but Acid Hydrolysis is especially associated with enhancements of larger size, at least considering longer treatment times. There is, therefore scope for further improvement in multi-stage pre-treatment processes regarding the production of biogas from sugarcane bagasse (Galindo-Hernández et al., 2018; Capecchi et al., 2015). One implication that might be drawn from the results of this work is that multi-stage pre-treatments may one day be used to diffuse methane production from sugarcane bagasse feedstock much more accessible to the biogas process. This work examines progress on a number of blends of thermal, chemical, and enzymatic pre-treatments in optimizing the sustainable production of biogas from lignocellulosic feedstocks. These advances will one day lead to an environment-friendly and scalable production of biogas from agricultural wastes (Prasad et al., 2024; Olatunji et al., 2021).

DOI: /10.61463/ijset.vol.12.issue6.386

Building Scalable Enterprise Applications: Challenges and Lessons Using.Net and Mvc Frameworks

Authors- Rama Krishna Prasad Bodapati

Abstract-Designing scalable enterprise applications is a complex job with proper planning, efficient design, and robust technologies. The paper explores challenges and lessons learned while implementing scalable systems using the.NET framework and MVC (Model-View-Controller) architecture. The core issues include performance optimization, database scaling, maintaining application state, high availability, and security concerns in the context of enterprise-level applications. The paper, therefore, looks at modules such as microservices, modular design, and integration with the cloud to be able to raise workloads without compromising either performance or reliability. Also, this paper gives readers insights on best practices when it comes to continuous integration/continuous deployment and testing of large applications. Through the use of practical examples and case studies, the paper gives developers valuable guidance for building enterprise applications that scale, maintain, and last using.NET and MVC.

Software Transmogrification for Expatiated Cerebration of Produced Water Conductivity

Authors- Egu, D.I., Oluwatope, A.

Abstract-Perspicuous appraisal of total dissolved solids (TDS) for aggrandized elucidation of produced water in the petroleum industry is the bane for the failure of most gentrified oil recovery (EOR) designs, cutting-edge formation evaluation/geochemical analysis, and holistic wastewater management programs. Hesitatingly, the correlations of aqueous systems for determination of electrical conductivity (EC) in the experimental gravimetric method usually give rise to higher mean standard errors of 15%and above. The aim of this research is to elucidate sensitivity analysis of both experimental and numerical (or improved OLI studio correlations) models of electrical conductivity and electrical conductivity with wider concentration domain for four extreme brine salinity vignette reservoir system located offshore in the Niger Delta, Nigeria. To achieve this, the multiplier-total dissolve solid (Schlumberger Gen-4) chart was digitized for the following ions; Mg2+, Ca2+, Na+, Cl-, K+, HCO3-, SO42- and CO32- beyond its range from 10ppm to a maximum of 500,000ppm and computer program was developed using Python with burgeoned multiple regression order of 15 within the OLI studio simulator. These correlations were compared with the concentrated desulphated sea water (DSW) and aqueous sodium chloride samples at extreme concentrations and stubby EC. The result shows that the ionic pairing number increases with ionic strength of greater concentrations with increased 15th-order degree polynomial and appreciable R2. Inevitably, the application of aggrandized archetypal equations to epitomize vacillation of concentration of brines is not only less cumbersome; it is quicker, and cost-effective, while its comparison with the experimental and published data greatly aided the reduction of errors (0.53%) even at higher salinity concentrations.

Coadjuvants of Deterministic and Probabilistic Tacks for Penumbra Quantification in a Labyrinthic Nugatoric Field Burgeoning

Authors- Egu, D.I., Oluwatope, A.

Abstract-The labyrinthic nugatoric Field is a recently acquired complex marginal oil/gas Field located North of Yenegoa in the Niger Delta land area of Southern Nigeria is heavily ridden with numerous rugosity in production performance leading to adverse down times and serious workover challenges technically hinged on the magnitude of identifiable subsurface uncertainties which includes reservoir rocks and fluid properties, reservoir energy, quantity and quality of geological, engineering and geophysical data, type of simulator and experience and knowledge of evaluator. Materials used for this study are suites of seismic sections, 5 well logs, a base map, time to depth conversion chart, reservoir data and the Schlumberger-petrel software which was used for the static, dynamic, deterministic and probabilistic re-modeling. Results gave a range of a minimum of 5,198,470.887STB to a maximum of 6,151,196.678STB for the probabilistic estimates and an average of 5,885,392.06STB for the deterministic estimate. It is however suggested to use the average estimates with a range of a minimum of 5,388,304.57STB to a maximum of 6,302,868.08STB. A comprehensive detailed analysis of results showed that the estimated STOIIP is approximately 5.8MMSTB with variations in the values of the low and high cases with estimated population standard deviations of 2.536 from the estimated deterministic value and a predicted variance of 6.431296. This means that the sampling mean most likely followed a normal distribution. In this case, the standard error of the mean σ_x ̅ ; (SEM) gave 1.793. In other to validate the degree of confidence of the modeled results, the 95% confidence interval results gave 1.960σ_x ̅ with marginal error of 8.428 ± 3.515 (±41.70%) and a geometric mean of 8.037406MMSTB. The population standard variance gave 6.431296 and the sample standard deviation gave 3.58645 while the sample standard variance gave 12.862592. A comparison of the deterministic and probabilistic methods has provided adequate quality assurance for estimating hydrocarbon reserves and the two values agree at least for the base cases with appreciable degrees of confidence. Comparative differences and similarities were deduced from this study.

Prognostication of Liquefied Petroleum Gas Espousal for Domestic Cookery in Enugu State, Nigeria

Authors- Egu, D.I., Oluwatope, A., Eze, U.J.

Abstract-The drawbacks of effective demographic survey attestation of other parts of the country is inconceivably the regression why more Nigerians continue to rely on cheaper sources of cooking fuels such as woods, kerosene, charcoal and dung because our government seldom lacks accurate field information, filtering and prognostic feedback mechanism capacity. This obtrusive conundrum exacerbates the trepidation of intending flexible LPG consumers. The aim of this abstraction is to carry out a vivid mathematical prognosticative demographic questionnaire survey report and results of liquefied petroleum gas (LPG) espousal for cookery in parts of Enugu State, South Eastern Nigeria. The objectives are to identify, plough, illuminate and enumerate latent barriers affecting LPG utilization with emphasis on escalating high prices and availability by mapping out both users and non-users and to carry out sensitivity analysis of survey results and perhaps attempt to identify potential market key indicators for possible increased LPG penetration in the short term.A demographic survey questionnaire was prepared and issued to 50,000 Respondents to excogitate the effects of accessibility, plausibility and adoption of LPG in the study area. Detailed comparative result circumspection showed that Respondent’s debt owed increased quadratically with coefficients of -0.034, 1.794 and 14.11 and regression of 0.953, while their monthly fuel consumption gave coefficient of 0.308, -7.375 and 49.81 with gentrified regression of 1. The major finding of this research is the verisimilitude of the sensitivity survey results revealing three precocious palpitation potential key market indicators for possible increased LPG penetration in the short term which includes Respondent’s debt owed, fuel consumption types/rates and monthly fuel consumption rates. It is approbated that more ensconced demographic survey reports be illuminated in other parts of Nigeria to boost the current available data and perhaps squelch consumer trepidations amidst current soaring and unabated price increases.

Introduction to SAP S/4hana (Sap Press)

Authors- George Christopher, Adebisi Heritage

Abstract-SAP S/4HANA represents a major evolution in the landscape of enterprise resource planning (ERP) solutions, offering organizations the tools to drive digital transformation, enhance business processes, and capitalize on real-time analytics. This article explores the key features, architecture, deployment strategies, and benefits of SAP S/4HANA. It delves into the transition from legacy SAP systems like ECC to S/4HANA, highlighting how its in-memory computing, integration with advanced technologies, and flexible deployment options reshape enterprise operations. Additionally, we explore the integration capabilities, industry-specific use cases, challenges faced during implementation, and the future of SAP S/4HANA as a critical component of digital enterprises.

A Study on Effects of Water Proofing Admixture on Concrete

Authors- M.Tech Scholar Viplove Lahori, Professor Afzal Khan

Abstract-This study investigates the effects of waterproofing admixtures on various mechanical properties of concrete, such as split tensile strength, compressive strength, and flexural strength. The control concrete (M25 grade) exhibited a split tensile strength of 5.97 MPa. The incorporation of a waterproofing admixture significantly improved the split tensile strength to 6.269 MPa, indicating enhanced resistance to cracking and tensile stresses. Similarly, the compressive strength of the concrete increased from 27.87 N/mm² with the use of the waterproofing admixture, demonstrating improved resistance to compressive forces. Flexural strength also showed a progressive increase with the use of waterproofing admixtures, further emphasizing their effectiveness in enhancing the concrete’s performance under bending stresses. The results highlight that waterproofing admixtures contribute to a refined microstructure, reducing porosity and improving particle dispersion, which ultimately leads to stronger and more durable concrete.

A Model to Assess the Feasibility of Applying Biomaterials in the Building Industry

Authors- Iman Naji, Associate Professor Dr. Polat Hancer

Abstract-IPCC Report 2022 shows that the Paris Agreement 2015 objectives are not met and the world will experience a 1.5 C increase in temperature limit sooner than predicted schedule, by 2040. So, neither low carbon nor even zero carbon building strategies are enough to prevent such a dramatic environmental impact caused by carbon emissions as the most major GHG produced by human beings, and we need an effective way to reverse the process of producing carbon to capture it from the atmosphere so that creating a model of carbon-negative building which is feasible to be constructed and used has been taken into consideration nowadays. The ideal theory is that innovative carbon-negative biomaterials should gradually substitute the generic carbon-producing materials but the reality shows that they haven’t found any significant place in the public building construction industry and that’s because of the lack of a comprehensive multilateral assessment system that can calculate, evaluate and interpret every aspect of feasibility in using a carbon-negative biomaterial and suggest the optimum case. Carbon-negative material studies mostly concentrate on the carbon capture capability of the materials and so do the life cycle assessment (LCA) tools. The application feasibility of the claimed carbon-negative biomaterials is a missing part, mostly neglected in scientific research. This research aims to investigate the best format of integration among the existing LCA tools in order to find a way to approach an acceptable assessment model for measuring the feasibility of applying carbon-negative biomaterials (CNB) in the building industry along with showing the modeling gaps according to the presented ideal model with the approach to generalize CNB at the construction market.

DOI: /10.61463/ijset.vol.12.issue6.387

An Investigative Study into the Causes of Rust on Surgical Instruments

Authors- Khaled Sakhail Alshammari

Abstract-This study aims to investigate the underlying causes of rust and identify effective strategies for prevention. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. Semi-structured interviews were conducted with healthcare professionals to gather in-depth insights into their experiences with rust formation, cleaning and disinfection practices, and storage procedures. Additionally, a quantitative survey was administered to a larger sample of healthcare professionals to collect data on their perceptions and practices. The findings of this study indicate that inadequate cleaning and disinfection, improper storage , and the use of low-quality materials are among the primary factors contributing to rust formation. To recommend, healthcare facilities should implement rigorous cleaning protocols, optimize storage conditions, select high-quality instruments, adhere to proper sterilization procedures, implement a preventive maintenance program to address issues promptly and provide regular training to healthcare personnel on proper cleaning, disinfection, and sterilization techniques.. By addressing these factors, healthcare providers can significantly reduce the incidence of rust-related complications and enhance patient safety.

DOI: /10.61463/ijset.vol.12.issue6.389

Influence of Social Media on Antimicrobial Resistance (AMR) Awareness

Authors- Vanishree Mittal, Dr. Adarsh Keshari, Dr. Padam Singh, Dr. Pooja Sharma

Abstract-Background: Antimicrobial resistance (AMR), often referred to as the “silent pandemic,” is one of the most significant threats to global health, impacting societies and economies worldwide. The World Health Organization (WHO) has listed AMR among the top 10 threats to global health. Addressing AMR requires a comprehensive approach, including antimicrobial stewardship programs, public awareness campaigns, and scientific research. AMR occurs when microorganisms (bacteria, fungi, viruses, and parasites) develop resistance to drugs intended to eliminate them, leading to more difficult-to-treat infections, prolonged illnesses, increased medical costs, and higher mortality rates. Objective: The objective of this research was to evaluate the role of social media in raising awareness about antimicrobial resistance and the importance of appropriate antibiotic use among the general public. Methods: An online cross-sectional survey was conducted from July 16, 2024, to July 23, 2024. The survey consisted of 15 questions, which participants self-completed. It was distributed via email, and participants voluntarily consented to the study online, with both their identities and data kept anonymous. All participants were from non-medical backgrounds to minimize the influence of demand characteristics. Data analysis was conducted using Microsoft Excel 2016. Results: Of the participants, 62.7% (47 out of 75) believed that social media could be an effective tool for raising awareness about AMR if content is properly monitored. Another 25.3% (19 out of 75) thought it could serve as a valuable resource for learning about AMR. Only 12% (9 out of 75) felt that social media was not a suitable platform for increasing awareness on this topic. Conclusion: The findings highlight the potential of social media as a valuable tool to improve knowledge about antimicrobial stewardship globally. Future studies are recommended to explore the impact of social media-based education on practical behaviour regarding AMR.

DOI: /10.61463/ijset.vol.12.issue6.390

Machine Learning for Cyber security Operations: A User-Oriented Framework

Authors- Assistant Professor Mrs. R.Bhuvaneswari, Ms.T.Misha

Abstract-To safeguard an enterprise’s cyber security, Security Information and Event Management (SIEM) systems are commonly utilized to standardize security events from various protective technologies and generate alerts. These alerts are reviewed by Security Operation Center (SOC) analysts to determine their legitimacy. However, the sheer volume of alerts most of which are false positives often overwhelms the SOC, exceeding its capacity to address them all. Consequently, genuine threats and compromised systems may go undetected. Machine learning offers a practical solution to minimize false positives and enhance the efficiency of SOC analysts. In this paper, we present a user-oriented machine learning framework designed for cyber security operations in real-world enterprise environments. We examine common SOC data sources, their workflows, and strategies for leveraging and processing this data to create an effective machine learning system. The paper is targeted towards two groups of readers. The first group is data scientists or machine learning researchers who do not have cyber security domain knowledge but want to build machine learning systems for security operations center. The second group of audiences are those cyber security practitioners who have deep knowledge and expertise in cyber security, but do not have machine learning experiences and wish to build one by themselves. Throughout the paper, we use the system we built in the Symantec SOC production environment as an example to demonstrate the complete steps from data collection, label creation, feature engineering, machine learning algorithm selection, model performance evaluations, to risk score generation.

A Study on Locally Dually Flat (α, β)-Metrics

Authors- Vasantha D M

Abstract-The purpose of this study is to articulate a detailed understanding of the significance of locally dually flat (α, β)-metrics. Here, the (α, β)-metric is represented by α=√(a_ij (x)y^i y^j ), which constitutes a Riemannian Metric, and β=b_i (x)y^i, which is identified as a differential one-form.

Optimizing CBR of Expansive Soil Subgrades Using Woven Geotextiles for Road Construction

Authors- Rammohan Sharma, Hariram Sahu

Abstract-This study investigates the effect of geotextile reinforcement on the stabilization of expansive soils used in pavement subgrade applications. Expansive soils, prone to swelling and shrinkage due to moisture fluctuations, present significant challenges for road construction. Laboratory experiments, including California Bearing Ratio (CBR) tests under soaked and unsoaked conditions, were conducted to evaluate the performance of two woven geotextiles (PEC-50 and HP-370) placed at varying depths (4 cm, 7 cm, and 8 cm) within the soil samples. The results demonstrate that geotextile reinforcement significantly improves the load-bearing capacity of expansive soils, with the highest CBR value of 4.32% achieved using PEC-50 at a 4 cm depth. Geotextile placement closer to the upper layers of the soil provided superior performance due to better load distribution and resistance to deformation. PEC-50 consistently outperformed HP-370 across all placement depths, highlighting the importance of geotextile material selection. The study concludes that geotextile reinforcement is an effective solution for stabilizing expansive soils, particularly in moisture-sensitive regions. Practical recommendations include optimal placement depths, geotextile selection criteria, and considerations for multi-layer systems in high-load scenarios. Future work should focus on field validation, dynamic loading effects, and the environmental impact of geotextile applications. This research provides a foundation for cost-effective and sustainable pavement design in expansive soil regions.

DOI: /10.61463/ijset.vol.12.issue6.391

Experimental Investigation on Bitumen Emulsion Used in Gravel Road Stabilization

Authors- Veerendra K Shama, Hariram Sahu

Abstract-This experimental study investigates the use of cationic medium-setting (CMS) bitumen emulsion in stabilizing gravel soils to improve their strength for road subgrade applications. Gravel soil, often used in road construction, typically requires stabilization to enhance its load-bearing capacity. The study examines the effects of CMS bitumen emulsion combined with small amounts of cement on the California Bearing Ratio (CBR) and other key engineering properties of gravel soil. Laboratory tests were conducted, varying mixing conditions, compaction efforts, and curing times. The results showed that the addition of CMS bitumen emulsion, particularly with 3% bitumen emulsion and 2% cement, significantly improved the soil’s CBR values and dry density, indicating enhanced subgrade strength. The best results were observed under Case D conditions, where the soil was mixed with 3% bitumen emulsion and 2% cement and allowed to rest for 5 hours before testing. The stabilization process proved to be cost-effective, environmentally friendly, and suitable for locally available gravel soils, providing a more economical and efficient alternative to traditional stabilization methods. This approach offers reduced pavement structural thickness and can contribute to sustainable road infrastructure development, particularly in regions with limited resources or high material costs. The study highlights the potential of bitumen emulsion as a sustainable and effective solution for improving the mechanical properties of gravel soils, especially for rural roads and low-traffic areas.

DOI: /10.61463/ijset.vol.12.issue6.392

Review on Optimizing CBR of Expansive Soil Subgrades Using Woven Geotextiles for Road Construction

Authors- Rammohan Sharma, Hariram Sahu

Abstract-strength, high moisture sensitivity, and volume instability. Expansive soils, such as silty clay, exhibit significant swelling and shrinkage behavior, often resulting in pavement failure. This review evaluates the effectiveness of woven geotextiles in improving the California Bearing Ratio (CBR) of expansive soils, focusing on their reinforcement capabilities, placement depth optimization, and comparative performance. The study discusses previous research findings, highlighting the advantages of geotextiles as cost-effective, durable, and sustainable solutions for subgrade stabilization. It concludes by emphasizing the potential of woven geotextiles in addressing the limitations of traditional stabilization methods and providing recommendations for future studies.

DOI: /10.61463/ijset.vol.12.issue6.393

SAP and BW Data Warehousing: How to Plan and Implement

Authors- Odunade Ojo, Folake Grace

Abstract-In today’s data-driven world, businesses rely heavily on efficient data management systems for decision-making and strategic growth. SAP BW (Business Warehouse) is a powerful tool used to integrate and analyze data from various sources, enabling businesses to make informed decisions. This article delves into the planning and implementation of SAP BW data warehousing, providing a step-by-step approach to successfully deploying and managing data within SAP environments. It covers key considerations, best practices, and common challenges, offering valuable insights for IT professionals and organizations seeking to optimize their data warehousing processes with SAP BW.

DOI: /10.61463/ijset.vol.12.issue6.395

Future Trends in Peer-to-Peer Payments

Authors- Ardhendu Sekhar Nanda

Abstract-The emergence of peer-to-peer (P2P) payment systems has significantly transformed the way individuals and businesses transfer money, buy goods, and access financial services. These systems, driven by technological advancements, are reshaping the financial landscape by offering speed, convenience, and low-cost solutions. With growing adoption, P2P platforms like Venmo, PayPal, Cash App, and Zelle are expanding into new markets and integrating innovative technologies like blockchain, artificial intelligence (AI), and machine learning (ML). This paper explores the future trends in P2P payments, discussing the role of emerging technologies, use cases, global adoption, regulatory concerns, and the societal impact of these systems.

DOI: /10.61463/ijset.vol.12.issue6.396

Transforming the Beauty Industry: AI-Driven Trends and Consumer Preferences in the Clean Beauty Movement

Authors- Professor Dr. Renuka Devi M, Shreya Shetty C, Vignesh Lokesh P G, Abdul Rashid Parviz

Abstract-Human skin faces environmental problems with busy lifestyles. This problem often leads to self-care first. To address this, we propose an AI-driven model that recognizes skin features such as wrinkles and dark spots. While the model uses detailed images to analyze skin health, giving users a better impression. In this research, the model provides particular skin care recommendations, including recommended medications and lifestyle changes. This allows users to make informed decisions and achieve optimal skin health. Ultimately, this research aims to democratize everyone’s skin, enabling individuals to maintain healthy skin despite their busy schedules.

DOI: /10.61463/ijset.vol.12.issue6.394

Review on Bitumen Emulsion Used in Gravel Road Stabilization

Authors- Veerendra K Shama, Hariram Sahu

Abstract-This study explores the enhancement of gravel soil strength through stabilization using cationic bitumen emulsion (CMS), focusing on its application as subgrade material in road pavements. Traditional stabilizers like cement, lime, and fly ash can be costly or inaccessible, making CMS a potential alternative. Laboratory tests assess the effects of CMS and small cement additions on California Bearing Ratio (CBR) values, dry density, compaction efforts, and curing times. Findings reveal that the combination of CMS and cement significantly improves soil strength, enabling reduced pavement thickness. This method offers a cost-effective and sustainable approach to soil stabilization, particularly in regions with limited access to conventional stabilizers.

DOI: /10.61463/ijset.vol.12.issue6.397

Development of a Simplified Mechanical Bale Making Machine Using Raw Jute

Authors- Ashok Kumar Prasad, Subrata Kumar Mandal, Atanu Maity, Arun Baiju V.G, Avi Chakraborty, Anindya Majumdar

Abstract-The raw jute plays an important role for the socio-economic and ecological condition of India from long time since when the people uses these products for their different purpose. It is bio-degradable and recyclable in nature having unique characteristic of absorbing atmospheric CO2 and releasing O2 gas to atmosphere, this will helps a lot for the purification polluted air. The bales obtained from raw jute were usually transported to the mills and warehouse for stacked used for production purposes. Considering all the drawback of the existing machine, compact size, manually operated bale press machine has been designed and developed at CSIR-CMERI. The basic requirement of stress analysis and effort has been calculated with reference to the capacity of the male adult worker. The required effort to rotate the handle is 190N, and as per design calculation the value obtained is 215.82N as the force applied workers. The equivalent stress and maximum shear stress was calculated based on the applied efforts it comes to around 114MP and 65 MP respectively also the value of analysis of von-Mises stress comes to 428MPa. Hence it is found that from the calculated values obtained the lead screw and the body structure is found to be under safe condition.

DOI: /10.61463/ijset.vol.12.issue6.398

Digital Solution for Artisan Market Expansion: EPICRAFT

Authors- Sharat, Fareed Ahmed, V S Krishna Chaitanya Avvari, Assistant Professor Shweta Singh

Abstract-The project “Digital Solution for Artisan Market Expansion – Epicraft” focuses on empowering rural artisans by bridging the gap between their traditional skills and the digital marketplace. Indian artisans contribute immensely to the country’s cultural heritage, producing exquisite handmade products such as textiles, pottery, jewelry, and more. Despite their talent, artisans often struggle to achieve financial stability due to limited access to global markets, dependence on middlemen, and lack of technological literacy. The Epicraft platform addresses these challenges by providing a user-friendly, mobilefirst digital solution tailored specifically for artisans. The platform offers features such as easy product uploads, real-time sales analytics dashboards, and event creation tools to host virtual exhibitions or collaborative workshops. These tools not only help artisans showcase their work but also allow them to understand market trends and connect directly with buyers worldwide. By eliminating intermediaries, the platform enhances the artisans’ earning potential and promotes sustainable economic growth.

Comparative Evaluation of Academic Quality in Life Sciences: Hirsch’s Rule (H-Index) and i10-Index

Authors- Dr. V. K. Singh

Abstract-This manuscript explores the application of the h-index and i10-index for evaluating the academic quality and productivity of faculty in life sciences. By emphasizing Hirsch’s rule (2005), the paper refines these bibliometric indices’ application to life sciences, where high collaboration and interdisciplinary research influence citation dynamics. Benchmarks specific to these fields provide a comprehensive evaluation framework for early- and mid-career scholars. Finally, the study highlights how Hirsch’s rule aligns with researcher evaluations in fields like bioinformatics and biotechnology, where team-based research and citation behavior differ from traditional disciplines.

DOI: /10.61463/ijset.vol.12.issue6.399

Fine-Tuning YOLOv8 for Insulator Defect Detection in High-Speed Railway Systems

Authors- Zhang Zheng, Md Kiron Ali

Abstract-Insulators are essential components in overhead catenary railway systems, providing electrical insulation and mechanical support. But lightning, physical damage, negative weather, and some other external factors that affect their performance, which in turn could interrupt the electricity supply. However, conventional inspection methods are both time consuming and labor-intensive and sensitive to environmental conditions. In order to address these challenges and improve detection performance on small defects, this work presents a deep learning-based approach to automatically detect insulator defects using the fine-tuned YOLOv8n model. The original YOLOv8n model is integrated with the custom loss function, the SGD optimizer, and some other parameters. The proposed model is trained on an unbalanced catenary defect detection dataset that contains seven categories of insulator images: missing, shelter, breakage, contamination, dirt, and the good class. Due to the class imbalance, a variety of data augmentation techniques are applied. We trained our same dataset with other existing methods, comparative results demonstrate that our model performs better than conventional methods, with achieving overall precision 95.3% and recall 93.4%. Experimental results also show excellent performance on contamination, shelter and good categories insulators, and produce promising results on more challenging defects like dirt, breakage, or cracks. In addition, the study also highlights that YOLOv8n can be used to automatically detect and classify insulator defects, which is more efficient and reliable in terms of maintenance and safety of the overhead contact lines.

DOI: /10.61463/ijset.vol.12.issue6.400

Inclusivity in Indian Culture: Language, Festivals and Community Bonds with Reference to Karnataka

Authors- Vikshith k k, Prathibha P Hegde, Nandini D Bharadwaj

Abstract-Indian culture is inclusive by nature, embracing multilingualism, diverse festivals, and strong community ties. This diversity is evident in Karnataka, a state where various cultural elements come together to enrich society. Many languages boom here, including Kannada, Tulu, Konkani, and Kodava. This language diversity is promoted through schools, media, and community efforts, encouragement mutual understanding and respect. Through its festivals, Karnataka represents the spirit of inclusiveness central to Indian culture. Major celebrations like Mysuru Dasara, Ugadi (New Year), and local folk festivals like Karaga unite people from all walks of life, facilitating shared celebrations. These festivals are not only religious or seasonal gatherings; they also serve as community events that strengthen bonds and bring together people with shared traditions. Karnataka shows how diverse groups can come together, preserving their customs while embracing those of others. Community ties are another important feature of Karnataka’s inclusiveness. Residents in both rural and urban areas participate in social groups and religious gatherings that build strong relationships and foster mutual acquaintance. This community spirit enables people to support one another, especially in times of need. Local collectives and cooperatives exemplify how people work together for social and economic progress. Karnataka serves as a model of inclusiveness in India. Through its language, festivals, and community bonds, the state preserves its rich heritage while uniting people from diverse backgrounds. Karnataka’s example demonstrates how diversity can be a strength, fostering harmony and unity across cultural lines.

DOI: /10.61463/ijset.vol.12.issue6.401

Seismic Analysis of C-Shaped Building with Varying Bay Length

Authors- Vikas Patanker, Deepesh Malviya, Ankita Choubey

Abstract-This study looks at four instances of G+10 story C-shaped buildings. By considering the distinctive irregularities, engineers can design structures that satisfy performance requirements and make efficient use of materials. In order to distinguish the other three structures from the base model, we looked at the same building with varying bay lengths. The base model’s bay length is 27 meters, while the second model’s bay length is roughly 33 meters, structure III’s bay length is 39 meters and 4th models bay length is 45 meters. In this study, an irregularly shaped building model will be analyzed and designed using STAAD.Pro. Shear force, bending moment, and storey drift etc. are just a few of the parameters that will be used to compare the results with simplified analysis methods in order to illustrate the advantages of using STAAD.Pro for irregular building design.

Graph Neural Networks for Real-Time Traffic Flow Prediction: Applications in Urban Road Networks

Authors- Assistant Professor Dr. Pankaj Malik, Atharva Sharma, Aditya Thakur, Kavish Jha, Pranjal Patidar

Abstract-Accurate and real-time traffic flow prediction is essential for effective urban road network management, minimizing congestion, and optimizing transportation systems. Traditional traffic prediction methods struggle to capture the complex spatiotemporal dependencies inherent in urban traffic data. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address these challenges. GNNs are well-suited for modeling traffic networks due to their ability to handle graph-structured data, where road intersections are represented as nodes and road segments as edges. The proposed framework integrates dynamic graph construction, temporal attention mechanisms, and adaptive learning to model the evolving nature of urban traffic patterns. Real-world traffic datasets are used to validate the framework, demonstrating its superiority in prediction accuracy, scalability, and robustness compared to baseline models. The results indicate that GNN-based models can effectively capture both short-term and long-term dependencies, providing actionable insights for traffic control systems, urban planners, and smart city applications. This research highlights the transformative potential of advanced machine learning techniques in tackling real-world traffic management challenges.

DOI: /10.61463/ijset.vol.12.issue6.402

Wind Turbine Failure Prediction Using Time-Series Analysis and Deep Learning: A Predictive Maintenance Approach

Authors- Assistant Professor Dr. Pankaj Malik, Naina Manghani, Nandini Chawda, Krati Patidar, Tasneem Khan

Abstract-Wind turbines are essential for renewable energy production, but their operation is often interrupted by unexpected failures of critical components, leading to costly downtimes and maintenance. Predictive maintenance (PdM) offers a promising solution by forecasting potential failures before they occur, thereby minimizing unplanned outages and improving operational efficiency. This paper presents a deep learning-based approach for predicting wind turbine failures using time-series sensor data. Specifically, we employ Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), which excels in modeling sequential dependencies in time-series data. Our method predicts imminent failures and estimates the Remaining Useful Life (RUL) of critical turbine components, such as gearboxes and blades, based on sensor readings from operational turbines. The results demonstrate that the LSTM-based model outperforms traditional machine learning techniques, achieving higher accuracy in failure prediction and lower error rates in RUL estimation. This predictive maintenance approach can significantly enhance turbine reliability, optimize maintenance schedules, and reduce operational costs.

DOI: /10.61463/ijset.vol.12.issue6.403

The Prevalence and Impact of Abusive Language in Indian OTT Platforms: A Critical Examination

Authors- Research Scholar Vishal Sahai, Assistant Professor Ms. Garima Jain, Associate Professor Dr. Ashwani Kumar

Abstract-Over-the-top (OTT) platforms have redefined content delivery, offering creators unparalleled freedom to explore bold themes and narratives. However, this creative liberty has led to the proliferation of abusive language, including profanity, hate speech, and discriminatory expressions, often raising ethical and regulatory concerns. This study explores the prevalence of abusive language in Indian web series, analyzing its manifestations and implications. Employing a qualitative content analysis of 40 top-rated Indian web series, the research identifies recurring patterns of verbal abuse and evaluates their impact on audiences and societal norms. The findings underline the need for balanced content creation that respects artistic freedom while promoting responsible media practices.

DOI: /10.61463/ijset.vol.12.issue6.404

Automatic Production Planning and Scheduling of Size

Authors- Professor Dr. Ramesh Sengodan, Naveen K S, Karthik K, Akshata

Abstract-Efficient production planning and scheduling are critical components of any manufacturing process, especially in industries dealing with high demand and varying product sizes. This paper proposes an Automatic Production Planning and Scheduling System (APPS) aimed at optimizing the production workflow by leveraging automation and advanced scheduling techniques. The system is designed to streamline the planning process by taking into account the quantity of products booked by distributors, marketing agents, and other clients. These bookings, made in advance either a week or a month prior serve as the foundation for the production schedule. The proposed system categorizes orders based on product sizes to allocate resources such as production machines and human labour effectively. By automating these processes, the system minimizes human error and improves efficiency. Production managers can create a detailed plan that aligns with these requirements, ensuring smoother execution and reduced downtime. A key feature of the system is its ability to provide a graphical representation of completed and pending orders, offering real-time visibility into production progress. This graphical view is generated using company data and serves as an intuitive dashboard for stakeholders to monitor the production lifecycle. Schedulers integrated with the system utilize cron jobs to automatically pick up orders, segregate them by size, and execute the production plan. Additionally, the system incorporates demand forecasting capabilities to predict future requirements, enabling proactive decision-making and resource allocation. By automating the planning and scheduling process, this system enhances production accuracy, reduces manual intervention, and accelerates order fulfilment, leading to improved customer satisfaction.

DOI: /10.61463/ijset.vol.12.issue6.405

Evaluating the Energy Budget of the Nivlisen Ice Shelf in the Antarctic Region

Authors- Keerthana K, Professor Geetha Priya

Abstract-This study investigates the surface energy budget of the Nivlisen Ice Shelf, Antarctica, during the Austral summer of November 2023 to February 2024. Spanning approximately 6,800 km² with surface elevations reaching 50 meters, the analysis focuses on specific regions, including the ice shelf center, grounding line, calving front, Potsdam Glacier vicinity, and rumples. Using data from Automatic Weather Stations (AWS) and Sentinel-1 satellite observations, the study examines energy flux components—net radiation, sensible and latent heat fluxes, and subsurface conductive heat fluxes—alongside surface melt dynamics. Persistent freezing characterizes the central region, calving front, and Potsdam Glacier vicinity, where negative energy budgets indicate minimal melt activity. In contrast, melting occurs near the grounding line and rumples from November to January, transitioning to freezing in February as energy input declines. Seasonal melting peaks in early summer, followed by freezing, driven by solar and temperature fluctuations. These findings underscore significant spatial and temporal variability in the energy dynamics of the Nivlisen Ice Shelf, contributing to a better understanding of its stability and resilience to environmental changes.

DOI: /10.61463/ijset.vol.12.issue6.406

Modeling the Impact of Glacial Lake Outburst Floods in Thame Village Using 2D Hydraulic Techniques

Authors- Adithya Sunil, Professor Geetha Priya M

Abstract-This study evaluates the threats posed by Glacial Lake Outburst Floods (GLOFs) to Thame Village in the Nepal Himalayas, using HEC-RAS 2D hydraulic modeling. Three distinct GLOF scenarios were simulated, focusing on flood depth, velocity, and spatial extent to assess the impacts on the village’s livelihood and infrastructure. Maximum flood depths and velocities reached 15 meters and 15 m/s, respectively, with significant spatial variability. The findings reveal that Thame Village, heavily reliant on potato cultivation and trekking-based tourism, faces severe risks of inundation, threatening its limited income sources and local infrastructure. Hydraulic analyses of parameters such as cell volume, wetted perimeter, and conveyance underscore the influence of terrain on flood dynamics, highlighting critical areas of concern. This study emphasizes the importance of integrating advanced hydrological modeling with socio-economic considerations to inform risk mitigation strategies. Recommendations include the construction of protective barriers, early warning systems, and community-based disaster preparedness to enhance resilience against future GLOF events.

DOI: /10.61463/ijset.vol.12.issue6.407

Improved Biogas Production from Kitchen Waste by Co-Digest with Floral Waste

Authors- Assistant Professor S R Rajkumar, C Somu, S Thanga Boopathi, G Binoth, J Alto Sujan, C Ajin

Abstract-Biogas production from organic waste has become a sustainable alternative for energy generation and waste management, especially for biodegradable wastes like agricultural and floral residues. Flower waste, an often-overlooked source, holds significant potential as feedstock for biogas digesters due to its high organic content. However, optimizing the efficiency of biogas digesters with flower waste requires understanding its unique composition and degradation patterns. This study investigates strategies to enhance biogas production efficiency from flower waste, focusing on preprocessing methods, microbial additives, co-digestion with other organic materials, and optimizing operational parameters like temperature, pH, and retention time. Results show that pre-treatment methods, such as hydrothermal processing or microbial inoculation, increase the biodegradability of flower waste, thus enhancing methane yield. Additionally, co-digestion with complementary feedstocks, like food or animal waste, improves nutrient balance and stabilizes the digestion process. By implementing these techniques, biogas digester performance with flower waste can be significantly improved, leading to higher energy yields and promoting sustainable waste management solutions for floral industries.

DOI: /10.61463/ijset.vol.12.issue6.408

Development of a Simplified Mechanical Bale Making Machine Using Raw Jute

Authors- Ashok Kumar Prasad, Subrata Kumar Mandal, Atanu Maity, Arun Baiju V.G, Avi Chakraborty, Anindya Majumdar

Abstract-Objective: The raw jute plays an important role for the socio-economic and ecological condition of India from long time since when the people uses these products for their different purpose. It is bio-degradable and recyclable in nature having unique characteristic of absorbing atmospheric CO2 and releasing O2 gas to atmosphere, this will helps a lot for the purification polluted air. The bales obtained from raw jute were usually transported to the mills and warehouse for stacked used for production purposes. Considering all the drawback of the existing machine, compact size, manually operated bale press machine has been designed and developed at CSIR-CMERI. The basic requirement of stress analysis and effort has been calculated with reference to the capacity of the male adult worker. The required effort to rotate the handle is 190N, and as per design calculation the value obtained is 215.82N as the force applied workers. The equivalent stress and maximum shear stress was calculated based on the applied efforts it comes to around 114MP and 65 MP respectively also the value of analysis of von-Mises stress comes to 428MPa. Hence it is found that from the calculated values obtained the lead screw and the body structure is found to be under safe condition.

DOI: /10.61463/ijset.vol.12.issue6.409

The Role of Social Media in Academics

Authors- Harini. G, Aruna. M, Dhivya Shri. R, D. Suganthi, J Mythili, Dr. N. Prabhu

Abstract-Social research takes several factors into account that affect respondents’ opinions about a topic. A study looks at several elements of the sample population. These are investigated to find their social and financial situation. Their decisions on many aspects of the social surroundings are strongly influenced by their socioeconomic background. The socio-economic background helps one understand the reasoning behind the responses of the research study participants.

DOI: /10.61463/ijset.vol.12.issue6.954

Asrld: Adaptive Secure Reinforcement Learning-Based Route Discovery

Authors- B. Swetha, U. Madhu Sandhiya, U. Nowmitha Priya, Dr. J. Viji Gripsy

Abstract-Efficient route discovery in mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs) is a critical issue due to considerations such as augmented latency, energy consumption, and control message overhead. Traditional routing systems, though optimal in certain environments, tend to perform suboptimally in resource-constrained and dynamic environments. The work introduces the Adaptive Secure Reinforcement Learning-Based Route finding (ASRLD) model, combining adaptive threshold-based broadcasting, reinforcement learning, and blockchain security mechanisms to improve route finding efficiency. The ASRLD model reduces unnecessary route request (RREQ) messages, optimizes path choice, and improves overall routing effectiveness. Key components include an adaptive broadcasting mechanism that adjusts rebroadcasting probability based on network conditions, a Q-learning agent that self-detects best paths, an energy-aware routing scheme that considers residual energy levels, and blockchain-based security features for safe route establishment. Simulation results using NS-3 show significant improvement over traditional AODV and DSR protocols in terms of reduced routing overhead, enhanced packet delivery ratio, lower end-to-end latency, and optimized energy consumption. The proposed model delivers a robust, energy-aware, and secure methodology for route detection under challenging network scenarios.

DOI: /10.61463/ijset.vol.12.issue6.955

Green Synthesis of Manganese Oxide and Copper Oxide Nanoparticles Using Piper Dravidii Leaves Extract and Evaluation of their Antimicrobial Activity

Authors- Yogita Shinde

Abstract-Manganese oxide nanoparticles (MnO₂-NPs) and copper oxide nanoparticles (CuO-NPs) have a wide range of advantageous features that make them ideal for a variety of biological applications when their surface chemistry is properly matched. These include targeted drug delivery, tissue regeneration, cell separation, hyperthermia therapy, and enhancing contrast in magnetic resonance imaging (MRI). The study successfully demonstrated an eco-friendly approach to synthesizing CuO-NPs and MnO₂-NPs using a previously unidentified extract derived from the Piper dravidii leaves. To characterize the synthesized nanoparticles, a comprehensive suite of analytical techniques was employed, including UV-visible spectrophotometry, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). Structural analysis revealed that the obtained MnO₂-NPs and CuO-NPs possessed a cubic morphology, were inherently stabilized without additional chemical modifications, and had particle sizes ranging between 26.98 nm and 65.20 nm. The phytochemicals present in the leaves extract functioned as natural reducing agents, playing a crucial role in the green synthesis of these nanoparticles. This environmentally sustainable process also contributed to their enhanced antibacterial properties. FT-IR analysis confirmed that the nanoparticles predominantly contained hydroxyl (-OH) and carboxyl (-COOH) functional groups, rendering them hydrophilic. Due to their innate surface chemistry, additional functionalization was unnecessary for their intended applications. To assess their antibacterial efficacy, the synthesized MnO₂-NPs and CuO-NPs were tested against both Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacterial strains. The results demonstrated notable antibacterial activity, with a zone of inhibition measuring 17 mm against E. coli and 15 mm against S. aureus. Given their natural stabilization, herbal attributes, and potent antimicrobial properties, these nanoparticles hold significant potential for diverse biomedical and biotechnological applications.

DOI: /10.61463/ijset.vol.12.issue6.956

A Smart IoT Data Processing Model: Enhancing Efficiency with Machine Learning

Authors- Professor Dr S Murali krishna

Abstract-The rapid proliferation of Internet of Things (IoT) devices has generated an unprecedented volume of data across diverse sectors, necessitating efficient data processing models to harness this information effectively. This paper presents a comprehensive smart IoT data processing model that integrates machine learning techniques to enhance operational efficiency and decision-making capabilities. The proposed model addresses critical challenges such as data volume, variety, and velocity, leveraging advanced analytics to preprocess, filter, and analyze sensor data in real-time. By utilizing machine learning algorithms, the framework enables predictive maintenance, anomaly detection, and optimization of resource allocation, leading to substantial reductions in operational costs and improved service delivery. Additionally, the study emphasizes the importance of data quality and cleansing techniques in ensuring reliable insights. Through case studies and practical applications in smart cities, transportation, and healthcare, this research illustrates the transformative impact of the smart IoT data processing model on efficiency and operational resilience. The findings underscore the potential of combining IoT and machine learning to create intelligent systems that are responsive to dynamic environmental conditions and user needs, paving the way for innovation in various industries.

Dynamic Modelling and Performance Analysis of a Solar PV and Hydrogen Fuel Cell-Based Energy System

Authors- Research Scholar Amol Barve, Professor Anurag S D Rai

Abstract-This paper presents a comprehensive study on the dynamic modeling and performance analysis of a stand-alone Hybrid energy system comprising a Solar Photovoltaic (PV) array and a Proton Exchange Membrane Fuel Cell (PEMFC). The IES is developed using the MATLAB/Simulink platform to assess its dynamic response under varying load profiles typical of rural areas across different seasons. The proposed system aims to supply electrical energy to an off-grid rural village located near LNCT Bhopal. The system’s power conversion stages include a DC/DC boost chopper to elevate the voltage level, followed by a three-phase inverter with an LC filter to ensure smooth sinusoidal AC output. The results demonstrate that the LC filter effectively mitigates voltage and current spikes, ensuring stable power delivery to the load. The analysis confirms that the proposed IES efficiently meets the village’s electrical demands throughout the year while adapting to dynamic load variations and transient conditions.

Exploring Cutting-Edge Approaches in Materials Science: A Comprehensive Review of Structural, Thermal, and Electrical Properties

Authors- Enass Milud Shaban Algammudi

Abstract-Materials science is an interdisciplinary field that plays a pivotal role in the development of advanced technologies by focusing on understanding and manipulating the structural, thermal, and electrical properties of materials. This review provides a comprehensive examination of emerging materials and techniques that are reshaping industries such as electronics, energy, aerospace, and healthcare. Key topics include advancements in nanomaterials, composite materials, conductive polymers, and graphene, as well as the latest approaches in material characterization and modeling. The review also highlights the challenges faced by materials science, including issues related to scalability, cost-effectiveness, sustainability, and long-term durability. Despite these challenges, significant progress is being made, particularly with the integration of computational materials science, machine learning, and smart materials. The future of materials science is poised to revolutionize various sectors through the development of materials with tailored properties, leading to innovations in quantum technologies, energy storage, and sustainable manufacturing. This paper concludes by discussing the potential future directions in the field, emphasizing the need for continued research and interdisciplinary collaboration to address the complex challenges and realize the full potential of advanced materials.

DOI: /10.61463/ijset.vol.12.issue6.959