International Journal of Science, Engineering and TechnologyProceeding of the November 2025 Conference
Comparative Performance Evaluation Of A Proposed Controller Tuning Strategy Against Conventional, Fuzzy Logic, And Optimization-Based Methods Using Key Control Indices
Authors: Asma Shibli, Dr Mohd Ilyas, Prof. Anwar Shahzad Siddiqui
Abstract: Precise control performance in dynamic and nonlinear systems remains a significant challenge for traditional PID controllers, primarily due to their fixed-gain nature and limited adaptability under varying operating conditions. This paper presents a fuzzy gain-scheduled PID tuning strategy that dynamically modifies the proportional, integral, and derivative gains based on real-time error and change in error using fuzzy logic inference. The proposed controller integrates the simplicity of a classical PID with the intelligent adaptability of fuzzy reasoning to enhance system stability, minimize overshoot, and accelerate transient response. A comparative performance evaluation is conducted against conventional tuning methods (Ziegler–Nichols and Cohen–Coon), fuzzy logic controllers, and optimization-based approaches (Genetic Algorithm and Particle Swarm Optimization) using standard control performance indices such as rise time, settling time, peak overshoot, steady-state error, and integral error metrics (IAE, ISE, ITAE). Simulation results validate that the proposed fuzzy gain scheduling method significantly improves dynamic performance and robustness while reducing steady-state error and control effort. The results demonstrate that the proposed approach offers an effective and computationally efficient tuning mechanism suitable for real-time applications in nonlinear and time-varying systems.
DOI: http://doi.org/10.5281/zenodo.17431856
Prediction Of Plant Leaf Health By Image Contour Segmentation And LSTM Model
Authors: Arpit Pethe Scholar, Arjun Rajput Assistant Prof, Dr. Sanjay Sharma, Dr. Sanjay Sharma
Abstract: Agriculture is the backbone of the Indian economy and contributes significantly to the GDP. Crop diseases, particularly those affecting leaves, lead to a decline in both the quality and quantity of agricultural produce. Traditional methods like expert diagnosis and pathogen analysis depend on skilled professionals and may be time-consuming. These approaches are also prone to human errors, affecting the accuracy of disease identification and management. This paper has proposed Plant Leaf Heath Prediction Model (PLHPM) leaf health prediction model that segment input image and extract features for learning. Image segmentation was done by active contour method, while histogram features was extracted from the image. Extracted histogram features were used for the LSTM model training. Experiment was done on real dataset images of potato. Result shows that proposed Plant Leaf Heath Prediction Model (PLHPM) has increases the detection precision and accuracy in less execution time.
DOI: http://doi.org/10.5281/zenodo.17440544
Ai-Based Patient Health Monitoring with Real Time Alert and Rescue System
Authors: Charumathi S, Kanishka Shree N, Thanuja G A
Abstract: This paper presents the design and implementation of an AI-based Patient Health Monitoring System that integrates symptom prediction, appointment and specialist mapping, OCR- based prescription parsing, medication reminders, and an auto- mated emergency alert-and-rescue mechanism with live location sharing. The system employs NLP for symptom understanding, machine learning classifiers for disease probability estimation, OCR for prescription digitization, and an event-driven notifica- tion architecture for fast emergency response. Emphasis is placed on real-time capability, data security, and practical integration with healthcare workflows. We demonstrate the system architec- ture, algorithms, implementation details, and an evaluation plan for accuracy and responsiveness.
Seasonal Variation In Heavy Metal Concentration In Telfairia Occidentalis Leaves In Ibeno Local Government Area, Akwa Ibom State
Authors: Erienu Obruche Kennedy, Onwugbuta Godpower Chukwuemeka, Njor Oru Ogar, Clark Poro David, Alani Olubukola Anuoluwapo, Essiet Akanimo Gordon, Apuyor Kingsley Efe
Abstract: The concentrations of heavy metals in the Ibeno Local Government Area of Akwa Ibom State, Nigeria were examined. This study employed an experimental design methodology. In December 2024 and June 2025, fifteen composite samples of Telfairia occidentalis leaves were collected. The leaf samples underwent washing with de-ionized water, were dried to a constant weight in an oven at 105 °C, and then pulverized to achieve a 2 mm mesh size for subsequent analysis. The ground leaves were digested using 1.0 cm3 of concentrated HClO4, 5 cm3 of concentrated HNO3, and 0.5 cm3 of concentrated H2SO4 in a 50 cm3 Kjeldahl flask. The concentration of heavy metals was determined using Atomic Absorption Spectroscopy. The data were analyzed based on the first-order kinetic model InC = InCo – kt. The concentrations of heavy metals (mg kg-1) during the dry season were: Mn (7.73 ± 3.06), Fe (5.93 ± 1.28), V (0.16 ± 0.26), Cd (0.21 ± 0.16), Ni (0.02 ± 0.01), while during the wet season, they were: Mn (7.75 ± 3.76), Fe (5.96 ± 4.07), V (0.21 ± 0.09), Cd (0.19 ± 0.06), Ni (0.03 ± 0.06). The results indicated that the concentrations of heavy metals varied between the wet and dry seasons. The mean concentrations of certain heavy metals (Ni, V, Pb, Zn, and Co) in the leaves of Telfairia occidentalis fell within the acceptable range of WHO standards for vegetables and food products, with the exception of Cd, Fe, and Mn. In conclusion, Telfairia occidentalis can serve as a resident indigenous plant bioindicator for monitoring anthropogenic influences of V, Pb, Mn, and Zn in the soil of the study area.
DOI: http://doi.org/10.5281/zenodo.17600209
Personal Voice Assistant Robot
Authors: Shwethaa.A.R, Ashwini GV, Samarth R Biradar, Shrikant Ravi Rathod, Sowmya G, Yamanur
Abstract: This project focuses on the development of a multifunctional Personal Assistant Robot designed to perform autonomous floor cleaning and home automation tasks. The system integrates various sensors, actuators, and controllers to navigate indoor environments, detect obstacles, and execute tasks such as vacuuming, mopping, and controlling lights or appliances. The robot can be operated using voice commands or a mobile application, enhancing user convenience and supporting smart home integration. By combining real-time decision-making with automation, this robot serves as a cost-effective and efficient solution for modern households, particularly aiding elderly or physically challenged individuals.
A Comprehensive Review of Data Anonymization Techniques
Authors: Dhananjay M.Kanade, Prof. Dr. Cherish S. Sane
Abstract: The exponential growth of data across healthcare, education, social networks, automotive systems, and cloud environments has intensified the need for robust and practical data anonymization strategies. This review synthesizes findings from multiple contemporary research works addressing anonymization frameworks, distributed anonymization, privacy–utility trade-offs, vulnerability analysis, clustering-based anonymization, diversity constraints, encryption-assisted anonymization, and novel methods including DNA-computing-based storage. The review identifies methodological advances, evaluates performance and scalability, and highlights challenges such as re-identification vulnerabilities, attribute sensitivity, bias propagation, and trade-offs between utility and privacy. The comparative analysis shows that while traditional techniques such as k-anonymity and l-diversity remain foundational, modern solutions integrate machine learning, distributed architectures, encryption, and clustering and mechanism design. Finally, the review outlines future research directions for developing context-aware, utility-optimized, and adversary-resistant anonymization systems suitable for heterogeneous and large-scale data ecosystems.
The Role Of Compensation Management On Employees’ Engagement: A Selected University In The South-West Region Of Nigeria
Authors: EKUNDAYO, Olajesu G, OGUNNIYI, Adeola D, DAPO-THOMAS, Moyinoluwa
Abstract: Compensation has to do with the receiving of benefits in various forms as an exchange for work done or service rendered. Organizations have a responsibility to compensate their employees who provide services that facilitate the goals and objectives of the organization. Employee engagement, a human resource concept, refers to the connection an employee feels towards their organization that drives their dedication and devotion to work and their organization. This paper generally seeks to assess the role of compensation in driving employees’ engagement in a selected university in the south west region of Nigeria. This paper adopted a simple random sampling technique and two theories were used for the study. A total of 220 questionnaires were randomly administered to staffs in the selected university, 200 copies of the questionnaire were retrieved, which amounted to a 91% response rate. This research shows that compensation has a positive and significant influence on employee engagement. The findings also revealed that there is a significant impact between compensation management and employee’s engagement. The findings illustrate that compensation management has a moderate relationship with a predictive capacity of R=0.522. This study recommended that employees need to be well compensated and rewarded in order to improve employees’ productivity and performance, as well as positive attitudes. This study established that individuals who are well remunerated tend to possess a high level of productivity and attitude to work. The study established that to facilitate employees’ engagement, compensation management strategies should be effectively implemented by managers of organizations for the purpose of achieving organizational productivity.
DOI: http://doi.org/10.5281/zenodo.17678790
An Empirical Study On The Impact Of Artificial Intelligence And Machine Learning In Financial Services
Authors: Sambhav Khatri, Tushar Antil, Manik Bansal
Abstract: This study investigates the impact of artificial intelligence (AI) and machine learning (ML) on the financial services sector, focusing on their applications, benefits, challenges, and ethical considerations. Employing a mixed-methods approach, it combines quantitative surveys of financial professionals with qualitative thematic analysis of existing literature to provide a comprehensive understanding of AI/ML adoption and performance. Major findings reveal significant improvements in risk management, fraud detection, and customer personalization, alongside persistent challenges in data quality, model interpretability, and ethical governance. This research contributes original empirical insights and theoretical implications, offering actionable recommendations for strategic AI integration in financial institutions.
DOI: http://doi.org/10.5281/zenodo.17707771
To Study the Effectiveness Of Waste Management Practices
Authors: Sneha Verma, Divisha Batra, Arshia Gulati
Abstract: Urban India is generating more waste because of a growing population, changing lifestyles, and increased consumption. Waste management is one of the country's biggest environmental challenges. This is mainly due to lack of public involvement, poor segregation habits, and limited infrastructure. National campaigns like Swachh Bharat Abhiyan have raised awareness, but effective practices at the household level still fall short. This research paper analyzes public attitudes, awareness, and participation in responsible waste management. We gathered primary data from 100 residents in Sonipat, Haryana, using structured questionnaires and also interviewed waste collectors and municipal workers. The findings show that while 88% of respondents know about waste segregation, only 43% practice it consistently. Younger and more educated individuals were more willing to adopt eco-friendly waste solutions than older age groups. The study concludes that effective waste management needs motivation, infrastructure improvements, incentives, and community collaboration. Policy makers, NGOs, and local authorities should promote technology use, decentralized composting, and structured awareness programs to turn knowledge into long-term action.
Weather Patterns And Air Quality During The Winter Season 2025–2026: A Case Study Of North India
Authors: Dilpreet, Kanika, Yash yadav, Abhay rana, Dr. Rashi Malik
Abstract: The winter season of 2025–2026 has shown a continuing pattern of deteriorating air quality across various regions, especially in North India, where cold weather conditions and high emission levels have intensified pollution episodes. This study explores the interrelationship between winter weather parameters and air pollution during this period. It focuses on how factors such as temperature inversion, low wind speed, humidity, and fog influence the dispersion and concentration of pollutants like PM₂.₅, PM₁₀, and NO₂. The data were collected from meteorological reports, air quality monitoring stations, and environmental research publications. The analysis indicates that prolonged calm conditions, low temperatures, and shallow boundary layers during the months of December 2025 to February 2026 restricted the vertical mixing of air, causing pollutants to accumulate near the surface. Emission sources such as vehicle exhaust, industrial activities, biomass burning, and stubble burning further worsened air quality. The study concludes that unfavorable winter meteorological conditions, combined with human-induced emissions, significantly contribute to pollution peaks. It emphasizes the urgent need for coordinated pollution control policies, early weather-based warning systems, and public participation to mitigate winter air pollution in upcoming years
DOI: http://doi.org/10.5281/zenodo.17711195
The Rise of the Digital Economy: Contribution of Fintech and E-commerce to India’s GDP
Authors: Dr Rashi malik, kunal jangra, Nishit, naman
Abstract: India’s digital economy, especially the FinTech and e-commerce sectors, has witnessed an amazing evolution in the last decade. This analysis assesses the impact of these two high- growth digital sectors on India’s GDP by way of an increase in financial inclusion, better market access, innovation-led growth, and employment generation. With support from government policies such as Digital India, Jan Dhan–Aadhaar–Mobile (JAM) Trinity, UPI, and the National Logistics Policy, India is now one of the world’s fastest growing digital economies. The pace of the digital economy is accelerating national growth, despite obstacles like questions of data privacy, cybersecurity threats, and regulatory intricacies. The study also finds that FinTech and e-commerce are not just sectors but enablers of India’s emergence as a globally competitive, technology-driven economy.
The Impact of AI-Driven Algorithmic Trading on Market Efficiency and Volatility: Evidence from Global Financial Markets
Authors: Dr.Rashi Malik, Shreya Sehgal, Kashwin, Aakash
Abstract: Recent advancements in Artificial Intelligence (AI) have significantly transformed the landscape of global financial markets through the increasing integration of AI-driven algorithmic trading. This study explores how such technological developments affect market efficiency and volatility across major exchanges worldwide. AI algorithms, capable of executing trades within milliseconds, have revolutionized the speed and accuracy of market operations. The research evaluates to what degree these innovations enhance market efficiency—through improved price discovery, narrower bid-ask spreads, and increased liquidity—while also assessing their potential to heighten volatility during times of economic uncertainty. Using a quantitative research approach, this study applies time-series analysis and regression modeling based on data from major global exchanges, including the NYSE, NASDAQ, and LSE. Key variables include multiple indicators of market efficiency and several measures of volatility. Findings indicate that AI-based algorithmic trading supports market efficiency by accelerating information assimilation and improving price accuracy. However, the study also reveals that these systems may induce short-term volatility spikes under specific conditions due to noise and rapid transaction frequencies.
Smart Tenant and Cashless Rent Payment System
Authors: Deepthi Jha, Prajwal S, H K Maruthi, Gireeshgowda K V, Havyas M N
Abstract: Tenants and property owners frequently experience delays, disagreements, and a lack of transparency when rent is collected manually. A smart tenant and cashless rent payment system that facilitates safe online payments and automates property administration is presented in this Paper. The solution, which was created with the MERN stack and Stripe connectivity, offers automated reminders, payment history monitoring, and dashboards for both owners and tenants. The framework provides a scalable, user-friendly solution for contemporary rent management while increasing efficiency and lowering dispute.
A Study On the Feasibility of Mobile Applications Among Secondary and Senior Secondary Students in Sonepat, Haryana
Authors: Aman Khurana, Akshit Sapra, Vaibhav Rana, Garv Arora, Dr. Rashi Malik
Abstract: The widespread use of smart phones and internet connectivity has radically changed the way education systems are structured all over the world. This paper explores the viability and usefulness of mobile applications as a learning tool among students in secondary and senior secondary schools in the town of Sonepat, Haryana. By using mixed methods as a method of gathering primary data, the use of questionnaires and interviews to gather data about the engagement with educational apps among students was performed. The research determines the popular channels, interest among the subject and additional issues that arise like data privacy and language barrier. A set of strategic suggestions are provided to achieve application design, usability, and outreach improvements and enable a digitally inclusive learning environment.
DOI: http://doi.org/10.5281/zenodo.17733713
Study and Analysis of Soil Stabilization Using Admixture
Authors: Ritu Mewade, S.S. Kushwaha
Abstract: Rapid urbanization and industrialization have led to environmental challenges, including a shortage of buildable land for infrastructure projects. Construction on clay soils is expensive due to the need for soil stabilization. Initially, engineers relied on trial-and-error methods and mechanical stabilizers; however, understanding the behavior of expansive soils has become crucial. Expansive soils, which are prevalent in southern India, can be stabilized using chemical and mechanical treatments. This study explores the use of industrial waste admixtures like fly ash (FA), rice husk ash, quarry dust (QD), and marble powder (MP) for stabilizing clay soils. The goal is to reduce construction costs and manage industrial waste. Experimental results showed that the dry strength of soils improved with the addition of QD, with the optimal ratio of 70:30 for Soil 1 and Soil 2, and 80:20 for Soil 3. The optimum moisture content (OMC) and maximum dry density (MDD) were determined for each mixture. Further experiments incorporating single- and double-layer geogrids showed significant reductions in swelling behavior, swell pressure, and improvements in the California Bearing Ratio (CBR), indicating enhanced soil stabilization. The study concluded that QD is the most effective admixture, and the inclusion of geogrids further improved the soil’s stability and strength, as confirmed by Artificial Neural Network (ANN) modeling with a correlation of 0.95.
DOI: https://doi.org/10.5281/zenodo.17750322
Real-Time Integrated Leaf Disease Detection Using CNN And Deep Learning
Authors: Kishan kannaujiya, Sandeep Bind, Kundan Kumar, Naimisha Awasthi
Abstract: Accurate and early detection of Leaf diseases is a critical component of precision agriculture, as it helps in reducing crop losses and ensuring sustainable food production. Traditional deep learning–based disease detection systems rely exclusively on leaf image analysis and neglect environmental and soil factors that influence disease occurrence and treatment outcomes. This study presents an advanced, real-time, integrated decision- support system that combines Convolutional Neural Networks (CNN) for leaf disease identification, soil health analysis using pH and NPK parameters, and real-time weather data integration through the OpenWeather API to enhance treatment precision and crop management. The proposed system operates in three major phases: (i) Leaf image preprocessing and classification using a fine- tuned CNN model trained on the PlantVillage dataset, achieving 98.6A rule-based treatment recommendation engine integrates the CNN classification output with soil and weather context to generate actionable and environment-specific suggestions, including suitable fertilizers, pesticides, and preventive measures. The system also incorporates a disease severity estimation module that quantifies infection extent and a risk prediction model that estimates short-term outbreak probabilities using recent climatic data. Experimental evaluation demonstrates that integrating soil and weather data improves treatment recommendation accuracy by 18Overall, the integration of deep learning with contextual environmental intelligence provides a comprehensive, data-driven, and farmer-friendly solution that supports decision-making, reduces input waste, and promotes sustainable agricultural practices. This framework can be further extended for large-scale deployment in smart farming environments through IoT and drone-based automation.
DOI: http://doi.org/10.5281/zenodo.17750325