| 1 |
Author(s):
ARKAPRABHO SAHA.
Page No :
|
CurePharm: AI-Powered Healthcare for Smarter Patient Care
Abstract
This study explores how AI can improve healthcare websites by making patient support faster and more efficient. It discusses features such as appointment management, symptom guidance, and personalized assistance, and concludes that AI can improve access to healthcare when used responsibly.
| 2 |
Author(s):
Sashya Vinodha P.
Page No : 1-2
|
Development of a Multi-Module AI Infotainment System for Real-Time Assistance and Control
Abstract
This paper presents the development of an intelligent multi-module infotainment system designed to enhance vehicle safety and driver experience through real-time data integration. By synthesizing inputs from diverse sources—including localized weather data, GPS coordinates, and simulated driving behavior—the system provides proactive features such as smart maintenance notifications, real-time driver coaching, and automated emergency response protocols. Unlike standard infotainment units, this framework utilizes predictive analytics and natural language processing to offer tailored feedback and ensure swift communication with emergency contacts during critical events. The proposed system addresses current market gaps in personalized driver assistance and demonstrates a scalable architecture for future AI-driven automotive technologies.
| 3 |
Author(s):
SUNIL NAIK.
Page No : 1-2
|
E-Vaccination Management System using Android
Abstract
Vaccination is one of the most important healthcare services used to prevent infectious diseases. Traditional vaccination systems mainly depend on manual records and paper-based management, which may cause data loss, scheduling problems, and inefficient monitoring. The use of Android applications in healthcare has improved the management of vaccination programs.
| 4 |
Author(s):
SANJANA H P.
Page No : 1-2
|
BRAIN TUMOR DETECTION USING MACHINE LEARNING THROUGH CNN
Abstract
Brain tumors are dangerous and need to be found early for better treatment and survival. This project uses a computer method called machine learning to help detect brain tumors from MRI scans. It uses a special type of model called a Convolutional Neural Network (CNN), which is good at looking at images and finding patterns. The model is trained with many labeled MRI images so it can learn to tell the difference between scans with tumors and those without. This tool can help doctors by giving quick and accurate results, making diagnosis easier and more affordable. Tests show that it works well, proving that machine learning can be useful in medical imaging.
| 5 |
Author(s):
Syeda Afra.
Page No : 1-2
|
Pneumoinia detection using AI and Grad-Cam
Abstract
Pneumonia is a serious lung infection that can lead to severe health complications if not detected early. This project presents an AI-based pneumonia detection system using deep learning techniques and Grad-CAM visualization. The proposed model utilizes Convolutional Neural Networks (CNN) to classify chest X-ray images as normal or pneumonia affected. Transfer learning models such as ResNet50 and AlexNet are used to improve detection accuracy and reduce training time. Grad-CAM is integrated to highlight the infected regions in X-ray images, providing visual interpretability and helping medical professionals understand the prediction results. The system is developed with a Flask-based web application for easy user interaction. Experimental results demonstrate high accuracy and reliable performance in detecting pneumonia from chest X-rays. This approach can assist healthcare professionals in faster and more accurate diagnosis.
| 6 |
Author(s):
AJIT VIKRAM SINGH.
Page No : 1-2
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COMPARATIVE STUDY OF GOVERNMENT DISCOMs VS PRIVATE DISCOMs IN INDIA
Abstract
ABSTRACT
The Indian power distribution sector plays a critical role in ensuring reliable electricity supply, financial sustainability, and consumer service delivery. This study presents a comparative analysis of Government-owned and Private Distribution Companies (DISCOMs) in India during FY2020–FY2025. The research utilizes secondary data collected from Power Finance Corporation (PFC), REC Limited, Ministry of Power reports, Integrated Rating & Ranking Reports, and the Distribution Utilities Ranking (DUR) framework. The analysis focuses on key performance indicators including AT&C losses, billing efficiency, collection efficiency, ACS-ARR gap, smart metering adoption, resource adequacy, renewable purchase obligation achievement, and consumer service ratings. The findings indicate that private DISCOMs such as TPDDL, BRPL, BYPL, and AEML consistently outperform many Government DISCOMs in operational efficiency, customer relationship management, digital integration, and financial sustainability. The study further highlights the importance of smart metering, AI-based billing analytics, demand-side management, and governance reforms for improving the performance of Government DISCOMs.
Keywords: DISCOM, PFC Ratings, DUR Framework, AT&C Losses, Billing Efficiency, Collection Efficiency, Smart Metering, CRM, Government DISCOMs, Private DISCOMs.
| 7 |
Author(s):
Srinath D, Vandana M.
Page No : 1-2
|
SUSTAINABLE BUSINESS PRACTICES: STRATEGIC INTEGRATION AND LONG-TERM VALUE CREATION
Abstract
In the contemporary global economy, the transition from purely profit-driven motives to
sustainable business practices has become a strategic necessity. This article explores the
evolution of sustainability from a Corporate Social Responsibility (CSR) obligation to a core
business strategy. It examines how environmental, social, and governance (ESG)
frameworks are reshaping operational models across various sectors. The focus remains
on the integration of circular economy principles, green supply chain management, and
sustainable financial reporting. While traditional models prioritized short-term shareholder
returns, the modern era demands a “Triple Bottom Line” approach—balancing People,
Planet, and Profit. This study concludes that businesses adopting sustainable practices
not only mitigate environmental risks but also achieve superior long-term financial
performance and brand resilience.
| 8 |
Author(s):
ANIKET AVINASH SHENDAGE.
Page No : 1-2
|
QUALITY CONTROL AND TESTING METHODOLOGY FOR INDUSTRIAL ASR AND MCC PANELS
Abstract
This paper presents a standardized, cost-effective Quality Control (QC) and testing methodology for industrial Automatic Sugar Regulator (ASR) and Motor Control Center (MCC) panels to satisfy IEC 61439 safety and thermal standards. Addressing emerging smart factory trends, the framework transitions from error-prone manual verification to automated testing via programmable logic controllers (PLCs) and automated relay test kits. This methodology ensures robust fault simulation accuracy under harsh environmental conditions—such as extreme temperatures, dust, and continuous vibrations—while validating IoT integration and renewable energy compatibility. Experimental deployment demonstrates minimized diagnostic errors, optimized testing cycle times, and reduced component waste. Ultimately, this approach enhances factory-floor reliability and extends panel lifecycles, supporting the digital evolution of heavy industrial power distribution technology.
| 9 |
Author(s):
Pratishtha Srivastava.
Page No : 1-3
|
Smart Glasses for Safety and Hazard Awareness: A Review of Context-Aware Assistive Systems in Safety-Critical Environments
Abstract
Global estimates indicate that approximately 285 million individuals live with visual impairments, confronting severe barriers to independent mobility in complex urban environments [1]. This navigation gap is most pronounced in high-traffic transit hubs; the Indian Railway system, for instance, manages over 23 million daily passengers [12] and an annual volume exceeding 8.6 billion [12], yet its navigational infrastructure remains largely inaccessible to the visually impaired. This paper provides a systematic review of the field, employing the PRISMA methodology to synthesize findings from 60 relevant articles published between 2015 and 2025 [2]. Our primary objective is to define the technical and design requirements necessary to bridge the "Information-Safety Gap" in safety-critical hubs. By shifting from basic "Assisted Seeing" to a paradigm of "Managed Reaction," we evaluate how Augmented Reality (AR) smart glasses—integrated with context-aware sensors and conversational User Experience (UX)—can transform hazardous environments into navigable spaces.
| 10 |
Author(s):
samiksha Jadhav.
Page No : 1-3
|
AI-Powered Web Application for Student Placement Prediction
Abstract
Campus placement prediction plays a crucial role in enhancing student career opportunities and institutional performance. This paper presents a Smart Placement Prediction System that leverages Machine Learning techniques to predict student employability outcomes. The system utilizes Logistic Regression to classify students based on academic performance, technical skills, and extracurricular activities. A full-stack web application is developed with role-based access for students, administrators, and recruiters. The dataset is preprocessed and used to train the model, achieving high prediction accuracy. The system provides insights into key placement factors and helps institutions take proactive measures to improve placement rates. The results demonstrate the effectiveness of machine learning in automating and improving the placement prediction process..
| 11 |
Author(s):
Bharath .
Page No : 1-3
|
UNITEX HYDROLIC MECHINE COMPANY
Abstract
ABSTRACT
To really get how a company works, you have to go
behind the scenes—see how teams work together,
watch the processes unfold, and figure out what drives
performance. I spent two months inside Unitek Hydraulic
Machine Company, a small manufacturer in Coimbatore,
Tamil Nadu, to do just that. My main aim was to break
down Unitek’s structure, trace how their machines
actually get built, check out how they handle quality, and
use SWOT to see where they stand. I talked to
employees, spent hours watching the workflow, and
studied everything from internal documents to testing
records. What’d I find? Unitek has real technical strength
and keeps customers happy. Still, they struggle with high production costs, are slow to adopt automation,
and nobody knows them outside India. There's big
potential if they target exports and start building smart
hydraulic systems. At the heart of it all, Unitek’s clear
structure and focus on quality keeps them steady and
their customers loyal. My top suggestions: get serious
about ERP software, improve digital marketing, and
invest in training.
| 12 |
Author(s):
DENISE LARA A.
Page No : 1-3
|
IMAPACT OF SOCIAL MEDIA MARKETING ON CONSUMER BUYING INTENTION
Abstract
Social media has changed the way companies communicate with customers and
promote their products. Businesses now use social networking platforms to share
information, advertisements, and promotional campaigns that influence consumer
purchasing decisions. This study examines the influence of social media marketing
on customer buying intention with special reference to Vaibav Bajaj. The research
analyzes how online advertisements, brand communication, customer reviews, and
promotional activities on social media affect customer interest and purchasing
behavior. Data for the study were collected from customers through questionnaires
and supported by information from books, journals, and online resources. The
findings reveal that social media marketing plays an important role in building
product awareness and motivating customers to consider purchasing products. The
study highlights the importance of using effective social media strategies to improve
customer engagement and strengthen market performance.
| 13 |
Author(s):
Shekhar M D.
Page No : 1-3
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AI Workspace for Intelligent Task Automation and Communication
Abstract
Artificial Intelligence is transforming the way users manage tasks and communication in daily life. This project presents an AI Workspace for Intelligent Task Automation and Communication, a smart platform designed to automate routine activities such as task scheduling, email handling, reminders, and communication management through an interactive interface. The system integrates AI-based assistance with modern web technologies to improve productivity, reduce manual effort, and provide a centralized workspace for users. The platform supports real-time task management, automated notifications, secure authentication, and chatbot-based interaction. Technologies such as React, Node.js, Express, MongoDB, and AI APIs are used for implementation. The proposed system aims to simplify workflow management and enhance user efficiency through intelligent automation and seamless communication features.
| 14 |
Author(s):
Rohan Gaikwad, Sahil Chavan, Kiran Pawar , Amit Lokhande,Om Raut.
Page No : 1-3
|
RAG MicroSim: A Hybrid Retrieval-Augmented Generation and Market Micro-Simulation Framework for High Frequency Trading Analysis
Abstract
Standard analytical models are unable to explain the non-linear market dynamics produced by
High-Frequency Trading (HFT), which operates in sub-millisecond domains. The most advanced
anomaly detectors currently in use, such as Transformers, rely on deep learning and achieve high
F1-scores, yet they function as opaque "black boxes" that are unable to reason causally. On the
other hand, when used with Large Language Models (LLMs), Retrieval-Augmented Generation
(RAG) offers explainability but, due to its inability to retrieve past logs for unusual situations,
fundamentally fails during fresh, out-of-distribution market events (such as localised flash
crashes). To include a discrete-event market micro-simulator directly into the RAG pipeline, we
present RAG-MicroSim, a deterministic hybrid architecture. This approach synthesises
mathematically constrained limit order book (LOB) states on demand, avoiding static-corpus
restrictions.
RAG-MicroSim produces counterfactual "what-if" evidence using the Hawkes Process for
stochastic order flow and Order Book Imbalance (OBI) as a rigorous mathematical trigger. The
algorithmic depletion of liquidity is successfully reconstructed by the system when tested against
the empirical baseline of the 2010 Flash Crash. With an F1-score of 0.94 in anomaly detection
and complete causal interpretability, statistical benchmarking demonstrates how RAG-MicroSim
unites semantic AI and quantitative physics.
| 15 |
Author(s):
Manoj HR.
Page No : 1-3
|
EARLY DETECTION OF PARKINSON’S DISEASE USING MACHINE LEARNING
Abstract
The project “Early Detection of Parkinson’s Disease Using Machine Learning” was developed with the aim of identifying Parkinson’s disease at an early stage through intelligent data analysis and predictive modeling techniques. The system analyzes patient-related data such as voice measurements, tremor patterns, and biomedical features to detect symptoms associated with Parkinson’s disease and assist healthcare professionals in early diagnosis. The application was developed using Python with a user-friendly interface using Streamlit, and machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Logistic Regression were used to achieve accurate prediction results. The system provides prediction outputs, graphical analysis, and confidence scores to improve understanding of the diagnostic process and support medical decision-making. This project highlights the importance of artificial intelligence in healthcare by providing a faster, cost-effective, and reliable solution for early disease detection while encouraging awareness, research, and technological innovation in medical applications
| 16 |
Author(s):
S. Kalaivani, S. Suganya.
Page No : 1-3
|
Measuring Perceived Equity and Its Influence on Sustainable Organizational Culture among IT Professionals
Abstract
The IT sector is a rapidly changing industry that faces changes regularly, so organizations should maintain a flexible and adaptable work culture. This study focuses on IT professionals expectations regarding fairness in the reward system, inclusion in the decision-making process and in interpersonal relationships. It also aims to understand the impact of this kind of thinking on the organizational culture. The Equity Theory and sustainable human resource management are used as the theoretical foundation for analysis. A total of 30 reviews were taken for analysis out of 480 reviews. The analysis findings reveal a positive effect of workforce involvement in sustainable practice in connection with fairness in rewards, employee treatment, and interpersonal relationships. The study highlights the significance of HR practices in promoting transparency, inclusiveness, and empathetic leadership to build a healthier work environment and enhance organizational sustainability.
| 17 |
Author(s):
Sujal Rajesh Aher, Om Arvind Yelwande, Aditya Mahendra Shelke, Sushant Sandeep Salve.
Page No : 1-3
|
AI-Based Tour and Travel Management System Using Django and MySQL
Abstract
Abstract - Abstract— In the contemporary digital era, travelers consistently face challenges regarding fragmented itinerary planning and rigid, generalized travel packages. This paper proposes a comprehensive, intelligent AI-Based Tour and Travel Management System designed to revolutionize the user travel experience by replacing manual planning with automated, data-driven personalization. The application is engineered on a robust Django web framework utilizing Python to orchestrate complex server-side business logic and seamlessly integrate machine learning models. The system relies on a secure MySQL relational database management system (RDBMS), structured to enforce strict transactional integrity, optimize indexing, and efficiently handle high-concurrency read-write logs during real-time ticket and hotel resource allocation. The core intelligence leverages Machine Learning (ML) algorithms, utilizing content-based filtering to deliver customized destination, hotel, and itinerary recommendations tailored specifically to individual user preferences, budget constraints, and historical behaviors. Experimental results and system evaluations demonstrate that the integrated AI model significantly reduces itinerary planning latency while enhancing user satisfaction metrics compared to traditional, static travel portals. Ultimately, this system serves as a scalable, end-to-end enterprise solution that optimizes resource management for service providers while providing a highly responsive, personalized, and intuitive interface for global travelers.
Key Words: Artificial Intelligence, Machine Learning, Django Framework, MySQL Database, Recommendation Systems, Full-Stack Development.
| 18 |
Author(s):
SUGI SARAVANAN S, MS NAVEENA M .
Page No : 1-4
|
A Study on Supply Chain and Logistics Management
Abstract
Supply chain and logistics management is a key functional area that ensures the efficient
movement of goods, services, and information from suppliers to final consumers. This study
aims to evaluate the effectiveness of supply chain practices, identify operational challenges,
and highlight their impact on organizational performance. The research is descriptive and
analytical in nature and is based on secondary data collected from academic sources, reports,
and publications. The analysis indicates that an organized logistics system helps reduce costs,
improve delivery speed, and increase customer satisfaction. However, problems such as weak
coordination, poor forecasting, and outdated systems can reduce efficiency. The study
concludes that adopting modern technology, improving planning, and strengthening supply
chain coordination are essential for achieving long-term operational success.
| 19 |
Author(s):
Boopesh S, Vetri Prabhu T .
Page No : 1-4
|
A LEVEL OF STUDY ON MULTIMODAL TRANSPORTATION
Abstract
The logistics industry plays a crucial role in economic growth and business operations
by ensuring efficient movement of goods and services. The internship at Allcargo Gati
Limited, Chennai provided practical exposure to logistics operations, supply chain
management, and digital logistics systems.
This study focuses on understanding operational workflow, express distribution
services, departmental functions, digital transformation, and strategic positioning of
Allcargo Gati in the logistics industry. The report also evaluates strengths, weaknesses,
opportunities, and challenges faced by the company.
The logistics sector is evolving rapidly with technological advancements such as ERP
systems, cloud computing, real-time tracking, and automation. Allcargo Gati has
adopted digital logistics systems and integrated supply chain solutions to improve
efficiency and customer satisfaction.
The internship helped in gaining knowledge about shipment handling, sorting, tracking
systems, customer service, and warehouse operations. The study concludes with
findings and suggestions to enhance operational efficiency and service quality.
| 20 |
Author(s):
Prof. S. H. Choudhari, Mr. Dipak P. Patil, Mr. Durgesh S. Patil, Mr. Nadim N. Tadavi, Mr. Saurabh O. Devare, Mr. C. D. Patil.
Page No : 1-4
|
A Review on Automatic Railway Gate System
Abstract
Railway safety is a critical concern in transportation systems, especially at unmanned or partially controlled level crossings where accidents frequently occur due to human error or lack of proper signaling. To address this issue, a Solar Powered Automatic Railway Gate System is proposed to enhance safety, reduce manual intervention, and ensure energy-efficient operation. This system automatically controls the opening and closing of railway gates based on train detection using sensors, thereby preventing collisions between trains and road vehicles. The proposed system integrates solar energy as a renewable power source, making it sustainable and suitable for remote areas where electricity supply is unreliable. A combination of microcontroller-based control unit, infrared (IR) sensors or ultrasonic sensors, and motor-driven gate mechanisms is used to detect the approaching train and operate the gate accordingly. When a train is detected within a predefined range, the system triggers the gate to close automatically and activates warning signals such as buzzers and LED indicators for road users. After the train passes, the gate reopens safely.
| 21 |
Author(s):
Karthik Kumar K.
Page No : 1-4
|
Integration of Dual-Axis Solar Tracking and Cleaning system
Abstract
Solar energy is the renewable energy resource and it has numerous advantages over the other forms of energy. Solar energy is the best form of renewable energy resource and it is easily available form of energy. The solar panels with tracking and cleaning system are generally suitable for the dusty environments, in countries like India. The dust gets accumulated above the solar panel and it reduces power efficiency by 50%, if in case the solar panel is not cleaned at regular intervals of time. In this System as the panels rotate 360⁰ throughout the day, where cleaning brushes slides over the panel. In case of daily generation of solar energy, the tracking and cleaning system is proposed to give more efficiency.
Solar energy is the renewable energy resource and it has numerous advantages over the other forms of energy. Solar energy is the best form of renewable energy resource and it is easily available form of energy. The solar panels with tracking and cleaning system are generally suitable for the dusty environments, in countries like India. The dust gets accumulated above the solar panel and it reduces power efficiency by 50%, if in case the solar panel is not cleaned at regular intervals of time. In this System as the panels rotate 360⁰ throughout the day, where cleaning brushes slides over the panel. In case of daily generation of solar energy, the tracking and cleaning system is proposed to give more efficiency.
| 22 |
Author(s):
Vasanth Kumar. M.
Page No : 1-4
|
A STUDY ON OPERATIONS ANDEMPLOYEE DEVELOPMENT ATCHIRANJILAL SPINNER
Abstract
This study focuses on understanding the operational activities, production process,employee development, and organizational structure of Chiranjilal Spinners. Themain objective of this study is to analyze how the company manages its textile manufacturing operations efficiently while maintaining product quality and employeeproductivity.The study examines various aspects such as yarn production processes, quality control systems, workforce management, production planning, and organizational practices followed by the company. It also highlights how Chiranjilal Spinners contributes to the textile industry through modern spinning techniques, effective management practices, and continuous operational improvements.The research includes both primary and secondary data sources. Primary data wascollected through observation and interaction with employees and supervisors, whilesecondary data was collected from company records, websites, articles, andindustrial references.Furthermore, the study identifies the strengths of the company, including qualityproduction, experienced workforce, efficient machinery, and systematic workflow2management. At the same time, certain challenges such as machine maintenance,production pressure, and workforce management are also discussed.Overall, this study concludes that Chiranjilal Spinners plays a significant role in thetextile manufacturing sector by maintaining quality standards, operational efficiency,and employee coordination.Keywords: Chiranjilal Spinners, Textile Industry, Yarn Production, Employee management, Production Process, Quality Control, Textile Manufacturing, Industrial operations, Workforce Development, Spinning Industry
| 23 |
Author(s):
Kishore M .
Page No : 1-4
|
Impact of Automation on Productivity in Sriwin Electric Company
Abstract
ABSTRACT:
Understanding the internal operations of an organization is important for improving productivity, coordination, and performance. This organizational study was carried out at Sriwin Electric Company to analyze its organizational structure, operational process, employee coordination, and overall business performance. The study mainly focused on understanding the company’s workflow, quality management practices, production activities, and customer service system.
The study was conducted through direct observation, interaction with employees, and analysis of company records during the internship period. Data was collected from various departments including Production, Quality Control, Maintenance, Human Resources, and Sales & Marketing.
The findings revealed that Sriwin Electric Company has strong technical expertise, good employee coordination, and a quality-focused working environment. However, the company also faces challenges such as increasing production costs, limited automation, and the need for stronger digital marketing strategies.
The study concludes that implementing modern technologies, improving automation systems, and adopting better management software can help the company improve efficiency and expand its market reach.
| 24 |
Author(s):
Bhavnay Gupta.
Page No : 1-4
|
Real-Time Handwritten Character Recognition Using Convolutional Neural Networks
Abstract
This paper presents a real-time handwritten character recognition system capable of identifying handwritten English alphabets (A-Z) and numeric digits (0-9) using deep learning. The system employs two separate Convolutional Neural Network (CNN) models — one trained on the A-Z Handwritten Character Dataset comprising 372,450 samples and another trained on the MNIST dataset comprising 60,000 digit samples. A confidence-based router mechanism determines whether a drawn character is a letter or digit by comparing output probabilities of both models and selecting the prediction with higher confidence. Both models achieve 99% validation accuracy after 5 training epochs. A real-time graphical user interface built using Tkinter allows users to draw characters on a digital canvas and receive instant predictions. Key contributions include an auto-crop preprocessing technique that eliminates scale sensitivity, a dual-model confidence router for letter-digit disambiguation, and contour-based character segmentation for word and sentence level recognition.
| 25 |
Author(s):
DILIP KUMAR K.
Page No : 1-4
|
DIGITAL TRANSFORMATION IN FINANCE AND ITS IMPACT ON SKILL DEVELOPMENT
Abstract
This study focuses on understanding the training structure, learning environment, and placement support provided by JSpiders, Chromepet, Chennai. The main objective of this study is to analyze how the institute helps students, especially fresh graduates and non-technical candidates, develop the skills required to enter the IT industry.
The study examines various aspects such as the courses offered, training methodology, practical exposure, and placement assistance provided by the institute. It also highlights how JSpiders bridges the gap between academic knowledge and industry requirements through structured programs in Java, SQL, and software testing. The research includes both primary and secondary data, where primary data is collected through observation and interaction, and secondary data is gathered from online sources and institutional materials.
Furthermore, the study identifies the strengths of the institute, including experienced trainers, industry-oriented curriculum, and regular mock interviews. At the same time, it also discusses certain limitations such as large batch sizes and the need for additional self-practice by students.
Overall, this study concludes that JSpiders plays a significant role in preparing students for entry-level IT jobs by enhancing their technical and soft skills. However, individual effort and continuous practice are essential to achieve successful career outcomes.
| 26 |
Author(s):
DHARUN KUMAR.S.
Page No : 1-4
|
LABOUR WELFARE PRACTICES IN WAREHOUSING AND TRANSPORTATION SECTORS
Abstract
This study focuses on understanding the operational activities, production process, employee development, and organizational structure of Venkateswara Fabrics. The main objective of this study is to analyze how the company manages its textile manufacturing operations efficiently while maintaining product quality and employee productivity.
The study examines various aspects such as yarn production processes, quality control systems, workforce management, production planning, and organizational practices followed by the company. It also highlights how Venkateswara Fabrics contributes to the textile industry through modern spinning techniques, effective management practices, and continuous operational improvements.
The research includes both primary and secondary data sources. Primary data was collected through observation and interaction with employees and supervisors, while secondary data was collected from company records, websites, articles, and industrial references.
Furthermore, the study identifies the strengths of the company, including quality production, experienced workforce, efficient machinery, and systematic workflow management. At the same time, certain challenges such as machine maintenance, production pressure, and workforce management are also discussed.
Overall, this study concludes that Venkateswara Fabrics plays a significant role in the textile manufacturing sector by maintaining quality standards, operational efficiency, and employee coordination.
| 27 |
Author(s):
Naveen.M.
Page No : 1-4
|
A Study on Production and Quality Control at Sriwin Electric Company.
Abstract
This study focuses on analyzing the production process and quality control practices followed at Sriwin Electric Company. The main objective of the study is to understand how the company maintains product quality, manages production activities, and ensures customer satisfaction in a competitive industrial environment.
The study was conducted during the internship period through direct observation, employee interaction, and analysis of company operations. Information was collected from various departments including Production, Quality Control, Maintenance, and Administration. The research mainly examined workflow management, production efficiency, defect control, safety procedures, and coordination between departments.
The findings revealed that Sriwin Electric Company maintains effective quality standards and follows systematic production procedures to ensure product reliability. The company has skilled employees, strong teamwork, and good customer support services. However, certain challenges such as high production costs, limited automation, and dependency on manual operations were identified during the study.
The study concludes that implementing modern production technologies, increasing automation, and improving management systems can enhance productivity and operational efficiency. Overall, Sriwin Electric Company demonstrates strong potential for future industrial growth through continuous improvement and technological advancement.
| 28 |
Author(s):
S Aravinth .
Page No : 1-4
|
A Study on Workplace Management and Industrial Operations in Vinayaka Electro Pvt. Ltd., and Performance at Vinayaka Electro Pvt. Ltd.
Abstract
Understanding the internal functioning of an organization is important for improving
productivity, employee coordination, and organizational growth. This organizational
study was conducted at Vinayaka Electro Pvt. Ltd., an electrical and electronics
manufacturing company located in Tamil Nadu. The study focused on understanding
the organizational structure, production process, quality management practices,
employee coordination, and overall company performance.
The study was carried out for two months through direct observation, employee
interaction, and analysis of company records. The research examined various
departments including Production, Quality Control, Maintenance, Human Resources,
Sales & Marketing, and Finance. SWOT analysis was also used to identify the
strengths, weaknesses, opportunities, and threats of the organization.
The findings revealed that Vinayaka Electro Pvt. Ltd. maintains strong product
quality standards and efficient departmental coordination. However, the company
faces challenges such as increasing production costs, dependence on manual
operations, and limited digital marketing activities. Opportunities for future growth
include automation, expansion into new markets, and adoption of smart electrical
technologies.
2
The study concludes that effective organizational structure and quality management
are the major strengths of the company. Recommendations include implementation
of ERP systems, employee training programs, automation of operations, and
stronger online business presence.
| 29 |
Author(s):
Mrs.Madevamma M S.
Page No : 1-4
|
CNN-Based Handwritten Text Recognition Using Deep Learning Techniques
Abstract
This paper presents a handwritten text
recognition system using Convolutional Neural Networks
(CNN), BiLSTM, and CTC loss. The proposed model performs
image preprocessing, feature extraction, and sequence
prediction to recognize handwritten text efficiently. The system
is trained using the IAM Handwriting Dataset and implemented
using TensorFlow. Experimental results demonstrate improved
recognition accuracy and reduced error rates for offline
handwritten text recognition applications.
| 30 |
Author(s):
LOKENDRA HINGWE.
Page No : 1-4
|
Performance Evaluation of Plastic Waste Modified Bituminous Mix Using Marshall Stability Method
Abstract
The rapid increase in plastic waste and deterioration of road infrastructure has become a major concern in developing countries like India. This study evaluates the performance of plastic waste modified bituminous concrete using the Marshall Stability method. Shredded plastic (LDPE and PP) was incorporated using the dry process in varying percentages (0%, 5%, 8.5%, and 12% by weight of bitumen). Marshall Stability tests were conducted to determine the optimum plastic content and evaluate the engineering properties of the mix. Laboratory results indicate that an optimum plastic content (OPC) of 8.5% significantly improves Marshall Stability by approximately 40%, enhances Marshall Quotient, and reduces air voids. Volumetric analysis such as VMA, VFB and bulk density were also evaluated. The study further includes an economic and environmental analysis, demonstrating that plastic modified bituminous mix is a sustainable, durable, and cost-effective solution for flexible pavements.
| 31 |
Author(s):
Dr. K. Durai1, K. Saravanan2, A. Kumaraguru3.
Page No : 1-4
|
Impact of Supply Chain Disruptions on Logistics Service Performance
Abstract
The study is on the supply chain disruptions in logistics and supply chain industry and their impact on logistics service performance. The research focuses on major disruptions such as transportation delays, supplier failures, labour shortages, inventory shortages, fuel price fluctuations, IT system failures and natural disasters, which affect delivery reliability, operational efficiency, cost, flexibility and customer satisfaction. Descriptive research design was used to collect both primary and secondary data. The primary data was collected from 140 respondents working in logistics and supply chain sectors through a structured questionnaire. The findings of the study reveal that transport delays, labour shortages and IT/system failures are the major disruptions experienced by logistics firms and transport disruptions varied significantly with employee designations. The study concludes that supply chain disruptions have a significant impact on logistics service performance. The study also highlights the importance of effective risk management, technology adoption, operational flexibility, and resilient supply chain strategies to improve logistics efficiency and organisational performance.
| 32 |
Author(s):
HARINE P.R.
Page No : 1-4
|
AI-BASED DETECTION AND FORENSIC ANALYSIS OF DEEPFAKE IMAGE
Abstract
The misuseThe misuse of deepfake images may lead to misinformation, identity theft, cybercrime, social manipulation, and digital fraud. As AI-generated images become more realistic, it becomes difficult to distinguish fake images from genuine images through normal visual observation.
| 33 |
Author(s):
SUNIL KUMAR.P.
Page No : 1-5
|
Digital Marketing Strategies for Small Businesses
Abstract
Digital marketing has transformed the way small businesses operate and grow in the
modern economy. This study aims to examine various digital marketing strategies and
analyze their impact on small business growth. The research adopts a descriptive
methodology using secondary data collected from journals, websites, and industry
reports. Key strategies analyzed include social media marketing, search engine
optimization (SEO), email marketing, and content marketing. The findings reveal that
digital marketing significantly enhances brand visibility, customer engagement, and
revenue generation for small businesses. Social media platforms are identified as the
most effective tools due to their wide reach and cost efficiency. However, lack of
technical knowledge and limited resources remain key challenges. The study concludes
that adopting well-planned digital marketing strategies can lead to sustainable growth
and competitive advantage for small businesses.
| 34 |
Author(s):
Karthika K.
Page No : 1-5
|
ROLE OF CONTENT MARKETING IN SHAPING USER BUYING BEHAVIOUR
Abstract
Content marketing has emerged as a powerful strategic tool that significantly influences user buying behaviour in the modern digital marketplace. Unlike traditional promotional methods, content marketing focuses on creating and distributing valuable, relevant, and consistent content to attract and engage a clearly defined audience. This approach helps consumers move through different stages of the buying decision process by increasing brand awareness, shaping perception, building trust, and encouraging long-term relationships. Informative and engaging content such as blogs, videos, social media posts, reviews, and storytelling enables consumers to evaluate alternatives, reduce perceived risk, and make informed purchase decision. Additionally, personalized and interactive content enhances user engagement and emotional connection with brand, further impacting purchase intentions and brand loyalty. This abstract highlights how content marketing influence consumer attitudes, preference, and decision-making patterns, emphasizing its role in shaping buying behaviour in a highly competitive and information-rich environment.
| 35 |
Author(s):
V. Nanthini.
Page No : 1-5
|
Impect of social media marketing on consumer buying behavior
Abstract
ABSTRACT
Platforms such as Facebook, Instagram, and YouTube allow companies to reach
a large number of consumers quickly and effectively. This study focuses on the
impact of social media marketing on consumer buying behaviour. Social media
advertisements, influencer promotions, product reviews, and online interactions
play a major role in influencing the purchasing decisions of consumers. Many
consumers rely on social media platforms to gather information about products,
compare brands, and read customer reviews before making a purchase. The
study also highlights how social media creates awareness, builds brand loyalty,
and increases customer engagement. Overall, social media marketing has a
significant influence on consumer behaviour and helps businesses improve their
marketing strategies and sales performan
| 36 |
Author(s):
Raman Singh.
Page No : 1-5
|
Multi-Purpose Amphibious UGV.
Abstract
This electronic document details the design methodology and fabrication process for a small-scale Unmanned Ground Vehicle (UGV) engineered for high resilience and robust remote operation in challenging, potentially amphibious environments. The platform integrates the Ai-Thinker ESP32-CAM System-on-Chip (SoC) to provide real-time, high-resolution visual feedback and Wi-Fi based remote control. Actuation is achieved using high-torque MG996R servo motors and L293D-controlled drive motors, managed using the Arduino environment. Crucially, the chassis utilizes Additive Manufacturing (AM), focusing on a water-resistant hull design and flexible wheels printed in Thermoplastic Polyurethane (TPU 95A). The material analysis shows the TPU offers exceptional elongation (>560%) and good chemical resistance, key factors for amphibious endurance and operational durability.
Key Words: Unmanned Ground Vehicle, ESP32-CAM, TPU 95A, Additive Manufacturing, MG996R, Remote Control.
| 37 |
Author(s):
D Girija.
Page No : 1-5
|
THE EFFECT OF LEADERSHIP STYLE ON EMPLOYEE SATISFACTION
Abstract
ABSTRACT :
Leadership style plays a crucial role in shaping employee attitudes, motivation, and overall job satisfaction within an organization. This study examines the effect of different leadership styles such as transformational, transactional, autocratic, and democratic leadership on employee satisfaction levels. Effective leadership influences communication, employee engagement, trust, and workplace culture, which directly impact how employees perceive their jobs and organizational environment The research analyses how leaders’ behaviours, decision-making approaches, and interpersonal relationships affect employee morale, commitment, and productivity. Data collected through surveys and questionnaires help identify which leadership styles contribute positively to employee satisfaction and which may lead to dissatisfaction, stress, or higher turnover intentions. The study also considers factors such as organizational structure, work environment, and employee demographics.
KEYWORDS: Online Advertising, Consumer Buying Behaviour, Consumer Perception, Influencer Marketing, Customer Engagement, commerce Behaviour
| 38 |
Author(s):
M.Janani.
Page No : 1-5
|
ROLE OF CONTENT MARKETING IN SHIPPING USER BUYING BEHAVIOUR
Abstract
ABSTRACT:
In the modern digital landscape, content marketing has become a critical tool for influencing consumer buying behaviour. The primary objective is to understand how different forms of content such as blogs, videos, social media posts, and influencer marketing affect consumer engagement and buying intentions. The research adopts a descriptive methodology and relies on secondary data sources including academic journals, industry reports, and online articles. The findings reveal that high-quality, informative, and engaging content significantly impacts consumer awareness and trust. Video content and influencer marketing are found to be particularly effective in influencing purchasing decisions due to their visual appeal and credibility. Furthermore, consistent and personalized content enhances customer loyalty and long-term relationships. The study concludes that businesses must strategically utilize content marketing to stay competitive in the digital age. Overall, content marketing not only drives immediate sales but also builds strong brand equity over time.
KEYWORDS: Content Marketing, Consumer Buying Behaviour, Digital Marketing, Customer Engagement, Brand Awareness, Influencer Marketing
| 39 |
Author(s):
R.Rithika mary.
Page No : 1-5
|
A STUDY ON CUSTOMER SATISFACTION TOWARDS GAIN GARMENTS
Abstract
ABSTRACT
Customer satisfaction is a key factor that determines the success and growth of any business
organization, especially in the garment industry where competition is intense and customer
preferences change frequently. The present study titled “A Study on Customer Satisfaction
towards Gain Garments” focuses on analysing the level of satisfaction among customers
with respect to the products and services offered by the company.
The main objective of the study is to evaluate customer satisfaction and identify the factors
influencing their perception towards Gain Garments. The study examines various aspects such
as product quality, pricing, design, timely delivery, and customer service. Understanding
these factors helps the company to improve its performance and meet customer expectations
effectively.
The research is based on a descriptive research design. Primary data has been collected
through a structured questionnaire from a sample of 100 customers using random sampling
method. Secondary data has been gathered from books, journals, and online sources. The
collected data has been analysed using simple statistical tools such as percentage analysis and
graphical representation.The findings of the study reveal that most customers are satisfied with the quality of garments
and timely delivery provided by Gain Garments. The study also indicates that customer
satisfaction plays an important role in building loyalty and encouraging repeat purchases.
In conclusion, the study highlights that maintaining high product quality, improving service
efficiency, and understanding customer needs are essential for enhancing customer
satisfaction. By focusing on these aspects, Gain Garments can strengthen its market position
and achieve long-term success in the competitive garment industry.
| 40 |
Author(s):
Sinchana M C.
Page No : 1-5
|
The Sustainable Aquqrium: Powered IoT Solution for Effortless Fish care.
Abstract
Abstract—The paper presents a smart and compact aquatic system that combines IoT sensors, machine learning, and image processing to automate and optimize aquarium maintenance. The system continuously monitors key water parameters such as turbidity, temperature, conductivity, pH and water level, using sensors interfaced with an STM32 microcontroller. A machine learning model analyzes this data to predict water quality in real time, while a CNN-based image processing module detects fish breed such as Bala Shark, Bristenose Pieco ,Clown Loach, Freshwater Angelfish, Neon Tetra, Silver Dollar and Swordtail and fish diseases from captured images. Additionally, the system features automated fish feeding and water level monitoring for improved care and efficiency. Designed for ease of use, the portable setup is ideal for homes, educational institutions and research environments, offering a realiable and intelligent solution for fish keeping with manual effort.
Index Terms—Sustainable Aquarium, IoT, Water Quality Prediction, Solar Panel Fish Breed Detection, Fish Disease Detection, Machine Learning, CNN, STM32,Automation.
| 41 |
Author(s):
Neetish.
Page No : 1-5
|
A CAR WITH A SPECIAL STEERING SYSTEM FOR MUSCULAR DYSTROPHY DISORDERED PEOPLE
Abstract
This project showcases the design and development of a unique steering system tailored for individuals living with Muscular Dystrophy (MD), a neuromuscular condition that leads to progressive muscle weakness and a decline in motor control. Conventional steering systems demand constant rotational force and a strong grip, which can be challenging for drivers with MD. The innovative solution we propose features a low-effort, joystick-based steering interface paired with an electronic drive-by-wire control system, allowing for safe and independent vehicle operation. Our design incorporates high-sensitivity sensors, adaptive force calibration, and multiple safety redundancies to cater to different levels of muscular ability. We developed both hardware and software components to guarantee real-time responsiveness, stability, and reliability of the system. Experimental tests show that this adaptive steering system significantly lessens the muscular effort needed while ensuring precise vehicle control, highlighting its potential to improve mobility and accessibility for those with muscular dystrophy.
| 42 |
Author(s):
H Pavan Kumar.
Page No : 1-5
|
Biometrically Secured ATM Vigilance System
Abstract
The Biometrically Secured ATM Vigilance System represents a comprehensive, multi-layered approach to combating the escalating threats against Automated Teller Machines, focusing on both user transaction security and physical machine integrity. Its novelty lies in integrating advanced biometric authentication methods, specifically fingerprint and facial recognition, into a very robust identity verification process. This step is intended to completely replace or supplement traditional Personal Identification Number and card-based verification and hence greatly reduce risks
from card cloning, skimming, and "shoulder-surfing." By furnishing a clear link between the account and unique physiological data of the authorized user, it ensures that only legitimate account holders can initiate transactions, significantly reducing unauthorized withdrawals and identity fraud.
The project proposes to address the very critical issue of physical tampering and theft at ATMs by designing and implementing an active, localized physical security module. This vigilance subsystem incorporates a highly sensitive vibration sensor that is strategically placed inside the casing of the ATM. When this sensor detects unauthorized physical shock or an attempt to breach the structural integrity of the machine, it immediately triggers a series of counter- measures through a microcontroller. These include the activation of a DC motor that mechanically locks the main access door of the room housing the ATM and traps the intruder. It also turns on a controlled sprinkler system that sprays a non-lethal but
incapacitating substance-for example, a highly potent irritant or even a knockout agent such as a simulated chloroform release in a closed-loop test environment pending safety regulations-to neutralize the threat. An integrated buzzer immediately produces an audible alert while sending a real-time, critical alert SMS with the location to the nearest security personnel or police station via a GSM Modem module for a quick, coordinated security response. By combining advanced digital authentication with proactive physical deterrence, such a dual strategy sets a new benchmark for securing.
| 43 |
Author(s):
Nazeem Abdul Rahim Shaikh .
Page No : 1-5
|
A Study on Social Media Influencer Marketing and Its Effect on Brand Trust
Abstract
Social media influencer marketing is a good way for businesses to get customers and make their brand look good. This study looks at how social media influencer marketing affects how people trust brands. We got our information from 40 people who answered a questionnaire. We looked at what people think of media influencers how credible they are and how they affect what people buy. What we found out is that social media influencer marketing helps a lot in making people trust brands and it also makes people more likely to engage with brands and buy from them. So we can say that social media influencers help people trust brands and that is good for marketing.
| 44 |
Author(s):
Shanmugapriyan V.
Page No : 1-5
|
Study on Challenges Faced by Micro, Small, and Medium Enterprises in Creditworthiness with Reference to Coimbatore
Abstract
The MSME sector in Coimbatore, a global manufacturing hub, faces a systemic credit accessibility crisis despite high liquidity and ambitious lending targets. This study investigates the transition from traditional asset-backed lending to modern cash-flow-based underwriting. It identifies four critical dimensions of creditworthiness: information asymmetry, the collateral conundrum, revenue volatility due to order migration, and the "survival credit" trap. Utilizing a Triangulated Risk Assessment Model (T-RAM), the research analyzes how digital footprints, cluster-specific risks, and policy moderators influence credit access in the 2025-2026 industrial landscape. Findings suggest that digital formalization is no longer optional but a prerequisite for competitive interest rates.
| 45 |
Author(s):
D.DILLI KUMARI .
Page No : 1-5
|
A STUDY ON WORKING CAPITAL MANAGEMENT AND ITS EFFECT ON LIQUIDITY
Abstract
This study focuses on evaluating the impact of working capital management on the liquidity position of firms and its overall influence on short-term financial stability. Working capital management is a crucial area of financial management that involves the efficient control and utilization of current assets and current liabilities. It mainly includes components such as cash, inventory, accounts receivable, and accounts payable, which directly affect a firm’s operational efficiency and liquidity position. The main objective of this research is to analyze the relationship between working capital management and liquidity by using secondary data collected from the financial statements of selected companies over a specific time period. A quantitative research approach has been adopted to measure liquidity performance and assess how effectively firms manage their working capital. Various financial ratios such as current ratio, quick ratio, and other liquidity indicators are used to evaluate the efficiency of short-term fund management. The analysis reveals that effective working capital management plays a significant role in improving a firm’s liquidity position and ensuring smooth business operations. Companies that maintain an optimal level of working capital are better equipped to meet their short-term obligations without financial pressure, thereby maintaining operational continuity and financial stability. Proper management of receivables ensures timely cash inflows, efficient inventory control reduces unnecessary holding costs, and effective management of payables supports better cash flow planning.
Key Word: Working Capital Management, Financial Management, Current assets and Current liabilities, Accounts receivable, and Accounts payable, Cash Flow
| 46 |
Author(s):
SANJAI S.
Page No : 1-5
|
Impact of Promotional Activities on Bike Sales at Vaibav Bajaj
Abstract
An organizational study helps in understanding the structure, functions, operational activities, and overall performance of a company. This report presents a detailed organizational study conducted at Vaibav Bajaj, an automobile dealership and service organization engaged in the sales and servicing of Bajaj two-wheelers. The study was carried out to understand the internal working environment, departmental coordination, customer service practices, operational efficiency, and business performance of the company.
The study focused on major departments such as Sales, Service, Spare Parts, Finance, Human Resources, and Customer Relationship Management. Information was collected through direct observation, employee interaction, discussions with management, and analysis of company records. The report evaluates the organizational structure, workflow process, customer handling methods, inventory management, and service quality standards maintained by the company.
The findings reveal that Vaibav Bajaj maintains strong customer relationships, effective service quality, and efficient teamwork among departments. The company has developed a positive reputation in the local automobile market due to timely service delivery and customer satisfaction. However, the organization also faces certain challenges such as increasing market competition, dependency on manual processes in some operational areas, and the need for stronger digital marketing strategies.
The study concludes that Vaibav Bajaj has strong growth potential in the automobile industry through technological adoption, improved customer engagement, enhanced employee training, and modernization of operational systems. Recommendations such as implementation of advanced management software, expansion of online services, and strengthening customer feedback systems can further improve organizational efficiency and business performance.
| 47 |
Author(s):
Author 1: Dr. S. K. Gurumoorthi professor Department of Management Studies GRT Institute of Engineering & Technology-Tiruttani Author 2 : P T Jaya sudha ( 10324631014) Department of Management Studies GRT Institute of Engineering & Technology-Tiruttani .
Page No : 1-5
|
A STUDY ON IMPACT OF AI IN TALENT ACQUISITION PROCESS IN KAPITUS STRATEGY SERVICES PVT LTD AT CHENNAI
Abstract
This study focuses on the role of Artificial Intelligence (AI) in the recruitment process and its impact on organizational performance. The main objective of the study is to analyze how AI is used in sourcing, screening, and selecting candidates, and to understand its effectiveness in improving recruitment efficiency. The study is based on primary data collected from respondents through a structured questionnaire, along with secondary data from relevant sources. Various factors such as recruitment delay, quality of hiring, employee perception, and organizational impact have been considered for analysis.
The findings of the study reveal that AI plays a significant role in reducing recruitment time and improving operational efficiency. However, the results also indicate that employees have mixed perceptions regarding the effectiveness of AI, especially in areas like decision-making accuracy and job-role matching. Concerns related to transparency, ethical issues, and bias in AI systems have also been identified. Statistical tools such as chi-square and correlation were used to analyze the relationship between variables, which showed that AI improves efficiency but does not always guarantee accurate placement of candidates.
Overall, the study concludes that AI is a valuable tool in modern recruitment practices, but its success depends on proper implementation, ethical considerations, and a balanced approach combining both technology and human judgment.
| 48 |
Author(s):
Maseera A. Sayyed.
Page No : 1-5
|
Real-Time Abnormal Activity Detection System Using Python and MediaPipe with Automated Alert Mechanism
Abstract
Advancements in computer vision and machine learning have significantly transformed the domain of intelligent surveillance systems. This paper presents a real-time Abnormal Activity Detection System developed using Python and the MediaPipe framework. The proposed system continuously monitors live video streams to identify unusual or suspicious human behaviors by extracting skeletal pose landmarks from video frames. These landmarks are mathematically evaluated against a pre-trained repository of normal gesture patterns using the Euclidean distance metric. Upon detection of an anomalous activity, the system autonomously triggers an alert mechanism that delivers an email notification to designated security personnel, embedding the captured incident image along with the corresponding geographical location. The architecture supports multi-location monitoring through a role-based login interface, an administrative dashboard, and a comprehensive incident logging module. Experimental evaluations demonstrate that the system achieves reliable detection accuracy at a processing rate of 15 to 20 frames per second, with alert delivery latency under five seconds. The framework proves to be computationally lightweight, platform-independent, and readily deployable in environments such as offices, educational institutions, public spaces, and residential areas.
| 49 |
Author(s):
Tejaswini Sachin Taware.
Page No : 1-5
|
Reinforcement Learning-Driven AI Control for PMSM with Field-Oriented Control Tejaswini Taware
Abstract
This seminar work presents a reinforcement learn-ing based field-oriented control strategy for Permanent Magnet Synchronous Motor (PMSM) drives. A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is used to replace the conventional PI current controller in the dq-axis current loop. The controller is validated using a 10 s staircase per-unit speed profile with repeated acceleration and braking transitions. The obtained results show fast tracking, low overshoot, stable dq current regulation, and improved robustness for practical intelligent drive applications.
| 50 |
Author(s):
Vani S Badiger.
Page No : 1-5
|
Intelligent Cattle Monitoring System Using IOT, Computer Vision and Deep Learning for Precision Livestock Farming
Abstract
This project presents an advanced cattle-monitoring system that leverages computer vision and deep learning to help farmers—especially in remote regions—manage livestock more efficiently and protect herd health. The platform integrates individual cow identification, automated feeding control, shed-environment monitoring, and smart vaccination reminders while providing real-time detection of Lumpy Skin Disease (LSD) through an AI-powered image-analysis module that identifies early symptoms to enable prompt intervention and limit spread. Additional capabilities such as cattle tracking, environmental alerts, and individualized vaccination schedules reduce manual labor, improve hygiene, and enhance productivity. With a simple user interface and automated operations, the system promotes technology-driven, self-reliant farming in India, strengthens rural livelihoods, and safeguards cattle health and farm economics.
| 51 |
Author(s):
Mr. Sagar Mahadev Wandare.
Page No : 1-5
|
DOMESTIC WASTEWATER TREATMENT BY USING POST ANOXIC HYBRID BIOREACTOR.
Abstract
The study was conducted on Post Anoxic Hybrid Bioreactor (PAHB) for domestic wastewater treatment to provide optimized media filling ratio for carbonaceous and nitrogenous organic matter removal at varying Hydraulic Retention Time (HRT) and Surface Area Loading Rate (SALR). This study focused on the use of a hybrid reactor system in which MBBR carrier media was used but instead of keeping it in suspension; its mobility was restricted and carrier media wasn’t allowed to move or settle at the bottom by providing the threads. The maximum organic load applied to the reactor during the study was 23.23 g BOD3/m2/d and 26.11 g COD/m2/d on average. SALR was kept in the range of 5.8 to 23.3 g BOD3/m2/d and 7.5 to 35.6 g COD/m2/d. The media filling ratio was 40% in both aerobic and anoxic bioreactors. The sludge from primary settling tank was used as an external carbon source in anoxic bioreactor for de-nitrification.
| 52 |
Author(s):
Sanjay M.
Page No : 1-5
|
CareConnect Ai medical chatbot
Abstract
AI medical chatbots leverage artificial intelligence to provide automated yet intelligent interactions in healthcare, revolutionizing the way patients access medical information and assistance. These chatbots use natural language processing (NLP) to comprehend patient queries and machine learning (ML) to analyse symptoms, offering preliminary assessments based on vast datasets of medical knowledge. Their ability to integrate with electronic health records (EHRs), telemedicine platforms, and wearable health devices enhances their functionality, allowing personalized recommendations based on individual patient history and real-time health metrics. One major advantage of AI medical chatbots is their accessibility—they provide 24/7 assistance, helping patients receive immediate guidance without waiting for appointments. They assist in chronic disease management by offering medication reminders, lifestyle recommendations, and periodic health check-ins. Additionally, they contribute to mental health support by recognizing distress signals through sentiment analysis and providing interventions such as relaxation techniques or guidance to seek professional help. However, challenges remain in ensuring the accuracy of medical recommendations. AI chatbots must be trained with reliable medical data to prevent misinformation, as incorrect advice could have serious consequences. Privacy and security are also critical concerns, as healthcare data is sensitive and must be protected from cyber threats. Future advancements in AI medical chatbots will focus on refining diagnostics, improving conversational accuracy, and enhancing human-AI collaboration. With continued innovation, these chatbots will become even more sophisticated, providing accurate, ethical, and personalized healthcare guidance while complementing medical professionals rather than replacing them. Their role in modern healthcare will continue to evolve, bridging gaps in accessibility, efficiency, and patient engagement.
| 53 |
Author(s):
Sathya K.
Page No : 1-5
|
DISCRIMINATION OF HAND WRITING BETWEEN BILINGUAL WRITER IN REGIONAL LANGUAGE AND ENGLISH
Abstract
Handwriting plays an important role in personal identification and forensic document
examination. The study of discrimination of handwriting between bilingual writers in
regional language and English focuses on identifying the differences and similarities in
writing patterns produced by the same individual in two different languages. Bilingual
writers often use distinct writing styles, letter formations, spacing patterns, slant, pressure,
and stroke movements depending on the language being written. These variations may occur
due to differences in script structure, writing habits, educational background, and language
familiarity.This research aims to analyze the characteristic features of handwriting in both
English and a regional language to determine whether the writer maintains consistent
individual traits across languages. The study also examines how natural variations influence
handwriting identification and the challenges faced in forensic comparison when multiple
scripts are involved. Samples collected from bilingual writers are carefully observed using
parameters such as alignment, pen pressure, connecting strokes, speed, size of letters, and
rhythm of writing.
The findings of the study help in understanding the uniqueness of bilingual handwriting and
its application in forensic science, criminal investigations, signature verification, and
document authentication. The research concludes that although handwriting may vary
between languages, certain individual characteristics remain consistent and can assist experts
in identifying the writer accurately.
| 54 |
Author(s):
L . Sherbin leo .
Page No : 1-5
|
A Study on Service Effectiveness at Maxpro Asia
Abstract
Service effectiveness plays a major role in determining customer satisfaction and organizational success in the financial and commodity trading industry. The present study titled “A Study on Service Effectiveness at Maxpro Asia” focuses on evaluating the quality, reliability, responsiveness, and customer support services offered by Maxpro Asia. The study aims to understand customer perceptions regarding trading facilities, technical support, market updates, and overall service performance provided by the company. Primary data for the study were collected through questionnaires distributed among customers and employees of Maxpro Asia. Secondary data were gathered from company websites, journals, articles, and financial service reports. The research uses descriptive analysis to interpret customer opinions and satisfaction levels regarding service effectiveness. The findings reveal that customers are generally satisfied with the company’s trading support, response time, and market assistance. However, certain areas such as advanced training programs and faster grievance handling require further improvement.
| 55 |
Author(s):
VIJAY KUMAR.
Page No : 1-5
|
Economic Inequality as a Constitutional Blind Spot
Abstract
Economic inequality has emerged as a significant global issue, deeply impacting social cohesion, democratic processes, and the safeguarding of fundamental human rights. Despite its critical significance, economic inequality is often neglected in constitutional frameworks, which typically emphasize political and civil rights while overlooking economic justice. This study critically examines economic inequality as an overlooked aspect of constitutional law, investigating the conceptual underpinnings, historical and ideological origins, and institutional barriers that contribute to its marginalization. Through a comparative analysis of different constitutional systems, this study highlights the limitations of formal equality doctrines and the reluctance of courts to tackle these economic disparities. It also explores the challenges and opportunities related to judicial activism and constitutional reforms aimed at integrating substantive economic rights into the Constitution. By identifying the structural reasons for the persistent neglect of economic inequality, such as liberal individualism, separation of powers, and postcolonial legacies, this paper advocates for a shift in constitutional law towards embracing substantive equality and economic justice. The proposed reform pathways emphasize the explicit constitutional recognition of economic rights, enhanced judicial capacity, and participatory democratic mechanisms to ensure inclusive policymaking. Ultimately, this study contends that addressing economic inequality within constitutional discourse is essential for constitutions to function as comprehensive instruments of social justice and democratic legitimacy in modern societies.
| 56 |
Author(s):
Sharan kumar Y.
Page No : 1-5
|
IMPACT OF SOCIAL MEDIA MARKETING ON CUSTOMER PURCHASE INTENTION
Abstract
The rapid growth of social media platforms has transformed the way
businesses communicate with customers and influence purchasing behavior.
This study examines the impact of social media marketing on customer
purchase intention, focusing on key factors such as content quality,
interactivity, credibility, and electronic word-of-mouth. Using a quantitative
research approach, data were collected from social media users through a
structured questionnaire and analyzed using statistical techniques. The findings
reveal that social media marketing has a significant and positive effect on
customer purchase intention, with engaging and informative content playing a
crucial role in shaping consumer attitudes.
| 57 |
Author(s):
SURESH P.
Page No : 1-5
|
A STUDY ON BUDGETING AND COST CONTROL IN INTERIOR DESIGNANDCIVILWORKSWITHETHICALCONSIDERATIONS
Abstract
Budgeting and cost control are essential aspects of interior design and civil works projects, ensuring efficient utilization of resources and successful project completion. Thisstudyaimstoanalyzebudgetingpractices,costcontroltechniques,andtheroleof ethicsinmanagingconstructionandinteriorprojects.Theresearchadoptsadescriptive approach using secondary data collected from journals, industry reports, and online resources. The study examines cost estimation, material management, labor cost control, and financial planning processes. It also highlights ethical issues such as transparency, fair pricing, quality assurance, and avoidance of corruption in project execution. The findings indicate that effective budgeting and strict cost control measures lead to timely project completion, reduced wastage, and improved client satisfaction. Ethical practices further enhance trust, accountability, and long-term business sustainability. The study concludes that integrating ethical principles with properbudgetingandcostcontrolstrategiesiscrucialforthesuccessofinteriordesign and civil works projects.
| 58 |
Author(s):
NARESH KUMAR C , Mr.P. RAJAPANDIAN .
Page No : 1-5
|
MACHINE LEARNING BASED FAKE NEWS DETECTION USING PYTHON
Abstract
Fake news detection has become a major challenge in the digital era due to the rapid spread of misinformation through social media and online platforms. The increasing availability of unverified content makes it difficult to identify trustworthy information, leading to serious social, political, and economic consequences. This paper presents a machine learning-based approach for detecting fake news using the Random Forest algorithm. The proposed system applies Natural Language Processing (NLP) techniques such as text preprocessing, tokenization, stop-word removal, and stemming to clean and prepare textual data. Feature extraction is performed using Term Frequency–Inverse Document Frequency (TF-IDF) to convert text into numerical form suitable for model training. The Random Forest classifier is then used to classify news articles as real or fake based on learned patterns. The model is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest algorithm provides high accuracy and robustness compared to other traditional models. The system is efficient in handling large datasets and reduces the risk of overfitting. This approach can be applied in real-world applications such as social media monitoring and news verification platforms. The study highlights the importance of machine learning techniques in combating misinformation. Future improvements may include the use of deep learning models and real-time detection systems for enhanced performance
| 59 |
Author(s):
Ms.Lowrence Khanna,Ms. Rimmy Chhabra, Mr. Ayushman Choudhary, Ms. Kanika.
Page No : 1-5
|
Power Optimization in Counters Using Gray Coding
Abstract
Digital counters are the basic building blocks in electronic systems. They are used in processors, communication circuits, timers, and embedded devices. But the normal binary counters consist of a problem: in the counting process, multiple bits suffer from high switching activity, and their bits change simultaneously. And that's why the power consumption increases. Due to this, there is a sudden increase in the switching activity. And because of this, power consumption also increases. In today's world there is a need for using low-power consumption designs, especially for battery-operated devices.
In this research paper, we will perform the comparison between a binary counter and a gray counter. Let's see which counter performs how much switching and how the gray coding improves the power efficiency.
| 60 |
Author(s):
Santolina Ashwini .
Page No : 1-5
|
INFLUENCE OF CRIME EXPOSURE ON BEHAVIOURAL RESPONSE: A GENDER BASED DIFFERENCE BETWEEN POLICE OFFICER
Abstract
Police officers are frequently exposed to traumatic and stressful situations such as violent crimes, accidents, abuse cases, and emergencies during their professional duties.
| 61 |
Author(s):
Avadhanam Sai Eswara Subhash.
Page No : 1-6
|
CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING AP0PROACH
Abstract
The agricultural industry is the key source of
livelihoods for many people that live in rural areas of
India; however, it has many issues which affect the
efficiency of the industry through non-optimized crop
yield production and non-use of data in decision-making.
The reliance on experience in farming means that there
are difficulties experienced by farmers who can be
negatively affected financially due to rapid movements in
climate. This article discusses how the intelligent Crop
Recommender System was developed through the use of
machine learning (ML) to provide accurate forecasts of
yields and crops to grow. Through the implementation of
two separate ML predictive modules, namely the Crop
Yield Predictor and Crop Recommender that utilized the
Random Forest algorithm in a Python and Flask
environment, the Crop Yield Predictor achieved an
accuracy rate of 96.84% for yield predictions and the
Crop Recommender achieved an accuracy rate of 87.56%
for recommendation based on analysis of the 94 375
records in the data set. The Crop Recommender System
allows users to enter their regional parameters e.g. soil
type, season, area etc., which will provide the user with
actionable information on growing crops. Additionally,
the Crop Recommender System contains a fertilizer
timing module which enables farmers to optimally apply
resources. The migration from manual experience to
automated predictive analytics will therefore allow
farmers to enhance productivity, reduce crop losses and
thus increase food security in the ever-changing
agricultural sector.
| 62 |
Author(s):
Saarthak Shivam.
Page No : 1-6
|
Facial Emotion Recognition using Ensemble Deep Learning and SVM
Abstract
Facial Emotion Recognition (FER) is an important
application of Artificial Intelligence, Computer Vision, and Deep
Learning that enables machines to identify human emotions
through facial expressions. Human emotions such as happiness,
sadness, anger, fear, surprise, disgust, and neutrality play a
major role in communication and behavioral understanding.
However, accurately classifying emotions remains challenging
due to variations in facial appearance, image quality, lighting
conditions, and similarities between emotional expressions.
This project presents a hybrid Facial Emotion Recognition
system using ensemble Deep Learning and Machine Learning
techniques for accurate emotion classification. The system
utilizes the FER-2013 Dataset consisting of grayscale facial
images categorized into seven emotion classes. Multiple
Convolutional Neural Network architectures including LeNet-5,
ResNet-50, and VGG-16 are used for deep feature extraction.
The extracted features are combined using ensemble learning
and classified using a Support Vector Machine classifier.
The proposed CNN-SVM hybrid architecture improves feature
representation, reduces overfitting, and enhances classification
robustness compared to traditional single-model systems. The
developed FER system has potential applications in healthcare,
surveillance systems, driver monitoring, gaming, robotics, and
human-computer interaction.
| 63 |
Author(s):
Prince Kumar.
Page No : 1-6
|
Skill-Connect: A Collaborative Platform for Students
Abstract
Currently, students from different institutions experience difficulties in building professional networks, discovering
collaborators for their projects, and accessing project-related
opportunities. Existing social media platforms and professional
networking platforms such as LinkedIn are primarily geared
toward professionals and are not a complete fit for the needs
of students. Skill-Connect is a platform reserved for student
users in multiple colleges. Students will be able to create
detailed profiles that describe their skills, interests and academic
accomplishments. It will allow peer-to-peer connections, groups
that could collaborate on projects, and sharing of information
regarding internships, events, as well as academic resources. This
is a simple, accessible, and scalable system designed to give
students with different backgrounds a resource that will allow
them to connect, communicate, and collaborate. Skill-Connect
will enhance academic development, increase innovation, and
ultimately prepare students for a career.
| 64 |
Author(s):
Asfand Ahmad Jamalee.
Page No : 1-6
|
Predicting Diabetes Progression Through Regression and Ensemble Learning: A Comparative Machine Learning Study
Abstract
Diabetes Mellitus remains a global health crisis, requiring high-precision tools for early intervention. This research introduces GlycoSense, an advanced ensemble framework designed to predict diabetes risk using the Pima Indians Diabetes Database (PIDD). The methodology utilizes a stacking strategy integrating three heterogeneous base learners—Random Forest, Gradient Boosting, and XGBoost—optimized via 5-fold stratified cross-validation and a Logistic Regression meta-learner.
To ensure clinical validity, the system employs a robust preprocessing pipeline featuring class-stratified median imputation and SMOTE-based oversampling applied strictly within training folds to prevent data leakage. Experimental results demonstrate that the stacking architecture achieves an Accuracy of 89.4% and an AUC-ROC of 0.94, significantly outperforming standalone models and traditional regression. To address the "black-box" challenge of ensemble methods, the study integrates SHAP (SHapley Additive exPlanations), providing mathematically rigorous feature attribution that identifies Glucose, BMI, and Age as the primary drivers of progression. The findings confirm that combining ensemble learning with Explainable AI (XAI) creates a transparent, high-performance decision-support tool ready for clinical integration.
Keywords: Diabetes Prediction, Stacking Ensemble, XGBoost, Explainable AI, SHAP, Clinical Decision Support.
| 65 |
Author(s):
ELANCHITHIRAN.B .
Page No : 1-6
|
A Comprehensive Study on DLAX Roofing Manufacturing Company”
Abstract
This study provides an in-depth understanding of DLAX Roofing Manufacturing Company, a growing producer of metal roofing sheets known for its focus on quality and customer satisfaction. The report explains how the company operates, how roofing sheets are manufactured, and how each department contributes to smooth production. It also highlights the importance of raw material quality, modern machinery, and strict quality control in ensuring durable roofing products.
By analysing the company’s workflow, strengths, challenges, and production data, the study gives a complete picture of DLAX Roofing’s performance and future potential. Overall, the findings show that the company is well-structured, efficient, and capable of expanding further in the roofing industry.
| 66 |
Author(s):
Abstract In the present business environment, the manufacturing industry is no longer driven only by production efficiency and cost control. Companies are increasingly expected to understand customer needs, respond to market changes, and build strong market positions through effective marketing strategies. Marketing has become an essential function that connects production with customer demand and long-term business growth. This paper presents an analytical study on marketing strategy in the manufacturing industry by examining its major elements, influencing factors, challenges, and strategic importance. The study is based on secondary data collected from books, journals, articles, and industry sources. The findings indicate that marketing strategy plays a crucial role in helping manufacturing firms improve customer satisfaction, enhance competitiveness, strengthen brand value, and achieve sustainable growth. The paper concludes that a customer-oriented and adaptable marketing strategy is necessary for the success of manufacturing organizations in today’s competitive market..
Page No : 1-6
|
An Analytical Study on Marketing Strategy in Manufacturing Industry
Abstract
Abstract
In the present business environment, the manufacturing industry is no longer driven only by production efficiency and cost control. Companies are increasingly expected to understand customer needs, respond to market changes, and build strong market positions through effective marketing strategies. Marketing has become an essential function that connects production with customer demand and long-term business growth. This paper presents an analytical study on marketing strategy in the manufacturing industry by examining its major elements, influencing factors, challenges, and strategic importance. The study is based on secondary data collected from books, journals, articles, and industry sources. The findings indicate that marketing strategy plays a crucial role in helping manufacturing firms improve customer satisfaction, enhance competitiveness, strengthen brand value, and achieve sustainable growth. The paper concludes that a customer-oriented and adaptable marketing strategy is necessary for the success of manufacturing organizations in today’s competitive market.
| 67 |
Author(s):
vineet kumar.
Page No : 1-6
|
Improving power factor by using Capacitor Bank
Abstract
The present research paper examines the design and application of Capacitor Banks as a major technique to enhance Power Factor (PF) of electrical systems. The introduction of inductive loads in the contemporary industrial environment (induction motors and transformers) causes lagging power factor, which causes higher transmission losses, voltage instability, and high utility penalties. The paper is about the use of fixed compensation to Automatic Power Factor Correction (APFC) systems, which microcontroller-based logic to actively switch capacitor steps with regards to real-time changes in load. The proposed system is able to reduce source demand, minimise the heating losses of 1^2R, as well as improve the overall distribution network efficiency by offering localised reactive power compensation. The process involves mathematical modelling of reactive power requirements, simulation and hardware testing Experiments show that the power factor of optimized capacitor bank integration can be substantially increased compared to a typical lagging value (e.g... 0.75) to nearly unity (0.98-0.99). Moreover, the paper touches upon the critical operational aspects, such as harmonic resonance and switching transient, and gives. a holistic framework of the attainment of a more stable. and cost-effective electrical infrastructure
| 68 |
Author(s):
SILAMBARASAN K , Mr. KANDAVEL.
Page No : 1-6
|
A STUDY ON THE EFFICIENCY OF LOGISTIC SERVICES AT ALLCARGO GATTI
Abstract
Logistics services play a vital role in the growth and success of business organizations by
ensuring the smooth movement of goods from manufacturers to customers. Efficient logistics
operations help companies reduce transportation costs, improve delivery performance,
maintain inventory control, and enhance customer satisfaction. The present study titled “A
Study on the Efficiency of Logistic Services at Allcargo Gati” focuses on evaluating the
effectiveness and efficiency of logistics services provided by the organization. The study aims
to analyse the quality of transportation, warehousing, delivery performance, customer
handling, and operational efficiency of the company.
The research is based on both primary and secondary data. Primary data were collected from
employees and customers through structured questionnaires, while secondary data were
gathered from company records, journals, websites, and previous research studies. Various
statistical tools such as percentage analysis, chi-square analysis, and graphical representations
were used to interpret the collected data. The study examines factors influencing logistics
efficiency, including timely delivery, safety of goods, communication systems, transportation
management, and customer support services.
The findings of the study reveal that efficient logistics services significantly contribute to
customer satisfaction and organizational performance. The study also identifies certain
operational challenges such as delays in delivery during peak periods, tracking issues, and
transportation coordination problems. Suggestions are provided to improve technological
support, strengthen supply chain coordination, and enhance customer relationship
management practices. The study concludes that Allcargo Gati has established effective
logistics service practices, but continuous improvement and innovation are necessary to
maintain competitiveness in the rapidly evolving logistics industry.
| 69 |
Author(s):
Sharmila R.
Page No : 1-6
|
CAMEL-Based Comparative Analysis of Selected Public and Private Sector Banks
Abstract
The banking sector plays a significant role in the economic development of a country by mobilizing savings, providing credit, and maintaining financial stability. In India, both public and private sector banks contribute substantially to economic growth and financial inclusion. This study evaluates the financial performance of selected public and private sector banks using the CAMEL analysis framework. The CAMEL model examines five important dimensions of banking performance: Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity. The study focuses on five public sector banks namely State Bank of India (SBI), Bank of Baroda (BOB), Punjab National Bank (PNB), Central Bank of India (CB), and Canara Bank, along with five private sector banks namely HDFC Bank, ICICI Bank, Axis Bank, Federal Bank, and IDBI Bank. Secondary data collected from annual reports and RBI publications for the period 2021–2025 were analysed using ratio analysis, mean, standard deviation, and ranking methods. The findings reveal that private sector banks generally performed better in profitability, asset quality, and management efficiency, while public sector banks maintained strong market presence and financial stability.
| 70 |
Author(s):
Jasleen kaur.
Page No : 1-6
|
Buy Now or Miss Out: The Impact of FOMO Marketing on Impulse Buying Behaviour
Abstract
In today’s digital world, social media and online shopping platforms have significantly changed consumer buying behaviour. Businesses are increasingly using emotional and psychological marketing strategies to attract consumers and improve sales. One of the most popular strategies used in modern marketing is FOMO marketing. FOMO, which stands for “Fear of Missing Out,” refers to the feeling of anxiety or pressure consumers experience when they believe they may miss exciting opportunities, discounts, trends, or experiences that others are enjoying. Companies use marketing techniques such as flash sales, limited-time offers, scarcity messages, influencer promotions, and countdown timers to create urgency among consumers.
The present study focuses on understanding the concept of FOMO marketing and analysing its impact on impulse buying behaviour. The study is completely based on secondary data collected from research journals, books, articles, websites, and previously published studies related to consumer behaviour and digital marketing. The research highlights the important role of social media platforms, influencer marketing, and online shopping trends in influencing consumer emotions and purchasing decisions. The findings of the study reveal that FOMO marketing strongly affects consumer behaviour, especially among young consumers and active social media users. Consumers often make impulsive purchasing decisions because they fear missing discounts, limited offers, trending products, or social experiences. The study also found that scarcity-based marketing strategies and influencer promotions increase emotional buying behaviour and encourage unplanned purchases. However, excessive exposure to FOMO marketing may also create stress, dissatisfaction, regret, and unnecessary spending among consumers. The study concludes that although FOMO marketing is highly effective in increasing sales and customer engagement, businesses should use such strategies ethically and responsibly. Transparent communication, honest advertising practices, and consumer awareness are essential for maintaining long-term customer trust and healthy purchasing behaviour.
Key Words: FOMO Marketing, Impulse Buying Behaviour, Consumer Behaviour, Social Media Marketing, Digital Marketing, Online Shopping, Influencer Marketing, Emotional Buying Behaviour
| 71 |
Author(s):
Deepanshu Singh.
Page No : 1-7
|
Energy Consumption Using LSTM
Abstract
The global transition toward carbon neutrality
and the integration of intermittent renewable energy sources
have necessitated unprecedented sub-hourly forecasting precision
within Smart Grid environments. Given that building energy
consumption accounts for nearly 40per of global total energy
use and 33per of greenhouse gas emissions, predicting volatile
load patterns is fundamental for demand-side management and
grid stability. This paper presents a rigorous, multi-layered
investigation into hybrid Long Short-Term Memory (LSTM)
networks for multivariate time-series modeling. Unlike traditional
stochastic models such as ARIMA, which assume stationarity and
linearity, our proposed framework captures the non-linear, multi-
seasonal nature of electricity demand by integrating Discrete
Wavelet Decomposition for multiresolution feature extraction. To
address the ”vanishing gradient” problem and improve long-
term sequence stabilization, we implement a ”Teacher Forcing”
training strategy, utilizing ground-truth outputs to prevent error
accumulation. Furthermore, we propose a hybrid architecture
that leverages Support Vector Regression (SVR) for residual re-
finement and Deep Extreme Machine Learning (DELM) for rapid
sequence learning. All model hyperparameters were tuned using
the Developed Henry Gas Solubility Optimization (DHGSO) algo-
rithm to ensure structural robustness. Experimental validation
was conducted using the London Smart Meter dataset and a
two-year multi-campus university dataset. Results highlight a
(15–20)per improvement in Root Mean Square Error (RMSE)
and significant reductions in Mean Absolute Error (MAE)
compared to standalone LSTM and MLP models. Finally, we
employ Layer-wise Relevance Propagation (LRP) to enhance
model interpretability, identifying critical 24-hour and 168-hour
temporal lags. This study provides a robust solution for utility
providers seeking to minimize operational risks through precise,
high-fidelity demand prediction.
| 72 |
Author(s):
Vasu Pandey.
Page No : 1-7
|
TeeVision: Customize and Buy your Fits
Abstract
This project presents an interactive web-based platform that allows users to customize clothing in real time before making a purchase. The application is developed using modern frontend technologies such as JavaScript and React.js, along with tools like Three.js and Framer Motion to enhance visual interaction and user experience. Users can personalize outfits by adding text, uploading images, and modifying colors, with instant preview of changes. The system focuses on improving user engagement and providing a more flexible online shopping experience. Overall, the project demonstrates how modern web technologies can be effectively used to build dynamic, responsive, and user-friendly e-commerce applications.
| 73 |
Author(s):
Ben William, Bhumika M, Boya Gowthami, Lavanya SM.
Page No : 1-7
|
Nutri Coach AI
Abstract
Nutri Coach AI is an innovative, AI-powered nutrition assistant that democratizes access to personalized, genetically informed health coaching. The system automates the interpretation of genetic testing reports (Nutri DNA PDFs) through a multi-stage pipeline: PDF extraction using `pdf plumber`, data cleaning & normalization, rule-based genetic interpretation via a YAML knowledge base, and integration of Large Language Models (LLM) for intelligent reasoning and contextual explanations. This hybrid approach combines the transparency of rule engines with LLMs’ nuanced capabilities, generating four personalized outputs—Nutrition Plans, Fitness Recommendations, Supplement Protocols, and Lifestyle Modification. Nutri Coach AI empowers individuals, fitness coaches, and healthcare providers to translate complex genetic insights into actionable, evidence-based nutrition and lifestyle interventions—addressing limitations of generic apps and costly traditional services. The project delivers a fully functional web application, robust API, comprehensive documentation, and validation methodologies to guarantee clinical accuracy and user trust.
| 74 |
Author(s):
Vansh Rastogi.
Page No : 1-7
|
Social Comparison and Self-Esteem among Students on Facebook
Abstract
The rapid ascent of Facebook as a dominant social networking platform has had a profound impact on students' social interactions and self-perceptions. This research, anchored in Leon Festinger's Social Comparison Theory (1954), examines the relationship between Facebook usage, social comparison, and self-esteem among students. Using a quantitative, cross-sectional design, data were collected from 590 students through a structured questionnaire incorporating validated scales such as the Iowa-Netherlands Comparison Orientation Measure and the Rosenberg Self-Esteem Scale. The findings indicate a positive correlation between Facebook usage and social comparison tendencies, consistent with previous studies (Vogel et al., 2014; Bonfanti et al., 2025). Moreover, social comparison is significantly negatively correlated with self-esteem, suggesting that students who frequently engage in comparisons with others tend to report lower self-worth (Irmer et al., 2023). The study also distinguishes between upward and downward comparisons, showing that upward comparisons significantly decrease self-esteem, while downward comparisons have a slight positive effect (Taylor, 2024). Regression and mediation analyses reveal that social comparison partially mediates the relationship between Facebook usage and self-esteem, aligning with findings by Bergagna and Tartaglia (2018). Additionally, the intensity of Facebook usage moderates this relationship, amplifying the negative impact of comparison on self-esteem (Wang et al., 2024). In summary, the results highlight that it is not merely the use of Facebook but the comparison processes it triggers that play a crucial role in shaping students' psychological well-being. The study underscores the need for awareness and interventions to promote healthier social media engagement among students.
Keywords: Social comparison, self-esteem, Facebook usage, students, upward comparison, downward comparison, social media, psychological well-being
| 75 |
Author(s):
Ark Sinha.
Page No : 1-7
|
Contingency Analysis of Power System Using Matlab
Abstract
Keeping a power network secure against sudden equipment failures is one of the less glamorous but genuinely hard problems in grid operations. Every day, system operators must verify that losing any single line or transformer will not push surviving branches beyond their thermal ratings — a check known as the N-1 criterion. We tackled this problem for the IEEE 14-bus test system by writing a DC power flow solver and contingency loop entirely in MATLAB, bypassing toolbox dependencies. For each of the twenty possible single-branch outages, we computed post-contingency loading on every monitored branch, then ranked the scenarios using a Performance Index (PI). The worst outage — loss of the Bus 1-2 tie — produces a PI of 3.35 and overloads two branches simultaneously, while 75% of contingencies leave the network intact. Eight simulation figures, including a single-line diagram, a 20×20 loading heat map, a severity curve, and a pre-versus-post comparison dashboard, document the findings in detail.
| 76 |
Author(s):
Jayesh Rameshsing Girase.
Page No : 1-7
|
Smart Soil Nutrient Detection and Crop Recommendation System Using Sensors and IoT
Abstract
Agriculture is one of the most important sectors for food production and economic growth. However, many farmers still face
problems in identifying soil nutrients and selecting suitable crops for cultivation. Traditional soil testing methods are expensive,
time-consuming, and not easily accessible for small-scale farmers. Due to lack of proper soil analysis, farmers often use excessive
fertilizers and grow crops that are not suitable for their land, resulting in lower productivity and reduced profit.
This research presents a Smart Soil Nutrient Detection and Crop Recommendation System using sensors and IoT technology. The
proposed system uses chemical soil testing methods along with sensors to detect soil nutrients such as Nitrogen (N), Phosphorus
(P), Potassium (K), pH level, soil moisture, and temperature. The collected data is processed using a microcontroller and transferred
to a cloud-based dashboard through IoT communication.
| 77 |
Author(s):
Ankur Kumar Dubey.
Page No : 1-7
|
Role of Capacitor Banks in Voltage Regulation of Power Systems
Abstract
One of the most important problems in the contemporary electrical power distribution systems is voltage regulation. The variations in nominal levels of voltage lead to degraded power quality, higher system losses, faster equipment wear, and possible instability cascades. The present paper is a detailed study of the use of capacitor banks as one of the primary tools of reactive power compensation and improvement of voltage profile in radial distribution networks. The paper systematically looks into the theoretical foundations, mathematical modelling, operational properties, and application implementation solutions of fixed, switched, and automatic capacitor banks. The IEEE 33-bus radial distribution system is used as a reference benchmark and three operating scenarios are analyzed using the load-flow simulation: a base configuration with no compensation, a uniformly compensated configuration with capacitor banks installed at uniform spacing, and an optimally placed configuration derived using sensitivity analysis. As shown in the results of the simulation, the optimal positioning of the capacitor banks will lead to a reduction in the total real power loss by approximately 29.8 per cent, increase in the minimum bus voltage per unit (p.u.) to 0.9680 p.u., and the power factor of the system increasing to 0.96. The results affirm that capacitor banks located in strategic locations are still one of the most affordable and technically feasible ways of regulating the voltage in modern distribution networks.
| 78 |
Author(s):
Asif Iqbal Hajamydeen, Muhamad Kamarul Lukman Kamarudin, Muhammad Irsyad Abdullah, Md Gapar Md Johar.
Page No : 1-7
|
Web-Based SOC Ticketing System for Improving Incident Management in Security Operations Centre
Abstract
Security Operations Centres (SOCs) are responsible for managing and responding to cybersecurity incidents in increasingly complex threat environments. However, many SOCs still rely on manual or fragmented methods to track incidents, which can result in inefficiencies and delayed response times. To address this issue, this paper presents the design and implementation of a web-based SOC ticketing system aimed at improving incident management processes. The proposed system provides centralized incident tracking, ticket prioritization, status monitoring, and role-based access through a web interface. The system was developed using a system-based research approach and evaluated through functional and scenario-based testing. The results indicate that the proposed system improves incident organization, enhances visibility of incident status, and supports more efficient SOC workflows. This study demonstrates the practicality of lightweight web-based ticketing solutions in strengthening SOC incident management.
| 79 |
Author(s):
VELMURUGAN V, Mr.P. RAJAPANDIAN.
Page No : 1-7
|
Smart automation Data Visualization
Abstract
Smart Automation Data Visualization is an AI-powered analytics and dashboard generation system developed to automate the process of data analysis, visualization, predictive analytics, and business reporting. The system enables users to upload datasets in CSV or Excel format and automatically generates intelligent dashboards with suitable charts, KPI metrics, and analytical insights without requiring manual configuration or technical expertise. The proposed system uses Machine Learning techniques for dataset pattern detection and smart chart recommendation. The Random Forest algorithm is used to identify dataset structures and recommend appropriate visualization types such as Bar Charts, Column Charts, Line Charts, Histograms, Pie Charts, Donut Charts, Stacked Bar Charts, KPI Cards, and Sparklines. Data preprocessing and analysis are performed using Pandas, while machine learning operations are implemented using Scikit-learn. Interactive and responsive visualizations are dynamically rendered using Chart.js. The platform also includes predictive analytics functionality using Linear Regression models to forecast future sales, revenue, and profit trends based on historical data. The system automatically detects trends, correlations, and performance patterns, enabling users to make data-driven business decisions. In addition, AI-generated analytical reports and KPI summaries provide detailed business intelligence insights in a user friendly format. The Smart Automation Data Visualization system reduces manual analytical effort, improves reporting enhances business efficiency, and decision-making through intelligent automation and real time interactive dashboards. The proposed solution provides capabilities similar to modern business intelligence platforms such as Microsoft Power BI and Tableau while focusing on automated visualization, predictive analysis, and smart analytics generation using Artificial Intelligence and Machine Learning technologies.
| 80 |
Author(s):
Kunal, Rimmy Chhabra, Prince, Raghav Aggarwal.
Page No : 1-7
|
A Study of Common Network Attacks and Their Prevention Techniques
Abstract
As internet-connected systems grow in their number, network security has become an urgent issue for both organizations and institutions as well as for individuals, thus promoting the need for available and feasible defense options. Although there are several studies which focus on different types of attack (DoS, phishing, SQL injection), there is a lack of a simple, easy-to-learn framework that would cover several attack types from a technical and human-centric perspective. The study of this paper involves literature review of the five common network attacks which are Denial of Service (DoS/DDoS), Man-in-the-Middle (MITM), Phishing, Packet Sniffing and SQL Injection, and also a hands-on experiment of SQL Injection attack using Oracle APEX and its prevention using parameterized queries has been carried out. The result indicates that a three-layered Layered Prevention Framework (LPF) including Awareness Layer, Technical Layer and Monitoring Layer is proposed which will provide a scalable and cost-effective solution to protect the network against the common cyber threats.
Keywords: Network Security, Cyber Attacks, DoS, MITM, Phishing, SQL Injection, Intrusion Detection, Layered Prevention Framework, Oracle APEX
| 81 |
Author(s):
Dr. Chitranka k .
Page No : 1-8
|
E-Triggering Effect: How E Commerce advertisements trigger the problem recognition in consumer buying behaviour.
Abstract
This study investigates the E‑Triggering Effect, defined as the process by which e‑commerce advertisements activate problem recognition in consumer buying behaviour. Problem recognition is the first and most critical stage of the Engel‑Kollat‑Blackwell model, yet it has received limited empirical attention compared to later stages such as purchase intention or brand attitude. The research addresses four specific gaps: the direct effect of e‑commerce ads on problem recognition, the role of personalisation, the influence of scarcity cues, and the moderating effect of product involvement. A quantitative, causal‑explanatory design was adopted using two between‑subjects experiments. Experiment 1 employed a 2 (personalisation: personalised vs. generic) × 2 (scarcity: present vs. absent) design with 120 participants. Experiment 2 employed a 2 (product involvement: low vs. high) × 2 (ad type: e‑commerce ad vs. no ad control) design with another 120 participants. Problem recognition was measured using a validated 7‑point Likert scale. Independent samples t‑tests and two‑way ANOVA were used for analysis. The results supported all four hypotheses. E‑commerce advertisements significantly increased problem recognition compared to no ad exposure (t(118) = 9.97, p < 0.001). Personalised ads triggered stronger problem recognition than generic ads (t(118) = 7.08, p < 0.001). Ads containing scarcity cues produced higher problem recognition than those without scarcity cues (t(118) = 4.51, p < 0.001). A significant interaction effect (F(1,116) = 12.7, p < 0.001) revealed that the triggering effect of ads was stronger for low‑involvement products than for high‑involvement products. These findings confirm that e‑commerce advertisements effectively trigger problem recognition, and that personalisation, scarcity cues, and product involvement significantly moderate that effect. The study provides actionable insights for digital marketers seeking to design ads that initiate consumer decision‑making at the earliest possible stage.
| 82 |
Author(s):
Monica S.
Page No : 1-8
|
AI Powered Vision intothe Brain’s Microstructure
Abstract
Accurate diagnosis of neurological disorders relies on a thorough examination of brain microstructure, especially white matter. Manually analyzing MRI and DTI scans is frequently both time-consuming and prone to inconsistency. The proposed AI-powered platform automates the analysis of MRI and DTI scans by leveraging Fractional Anisotropy (FA) values alongside image processing and segmentation techniques. The system employs deep learning models, including Convolutional Neural Networks and Random Forest classifiers, to identify abnormalities such as brain tumors, Alzheimer's disease, and Parkinson's disease. Furthermore, 3D tractography visualization reveals defective fiber pathways, whereas the chatbot assists users with fundamental clinical information. The results demonstrate that the proposed system provides clear visual outputs, enhances diagnostic accuracy, and reduces radiologists' workload. This provides scalability and clinical relevance while facilitating early diagnosis and detection.
| 83 |
Author(s):
Meet Pagar.
Page No : 1-8
|
Load Flow Analysis of a 9 – Bus Power System Using MATLAB Simulink
Abstract
The load flow analysis is one of the basic tools that are applied to power system studies for determining the steady-state operating conditions of an electrical network. It gives a detailed analysis of any typical 9 bus power system with regards to calculation of bus voltages, bus phase angles, active power flows, and reactive power flows under normal operating conditions. For solving the complex systems, conventional numerical methods such as Newton-Raphson method are used, because it is very fast and accurate to converge to a solution. The system is modeled assuming that generator buses (PV buses), load buses (PQ buses) as well as a slack bus to keep the system balanced. The iterative computation is based on the bus admittance matrix which is built using the input data like line impedances, power generation and load demand. The results achieved help to understand voltage profiles, line losses and power dispersion through the system.
The study shows the need for voltage stability and transmission loss minimisation in power systems. In addition, the study illustrates the capability of the load flow results in the planning, optimization, and fault analysis of the system. The results provide a basis for future studies using more sophisticated methods like optimal power flow and integration of renewable energy sources. In sum, it reiterates the utility of load flow analysis for the reliable and efficient operation of today's
power systems
| 84 |
Author(s):
Pratik Pramod Bhosale.
Page No : 1-8
|
Impact of Artificial Intelligence on Employment Pattern in India
Abstract
This research article explores the impact of
Artificial intelligence (AI) on employment in India, and
argues for the need for new policy interventions to address
this change. With the increasing use of technologies such as
robotic process automation (RPA), machine learning and
cognitive analytics, AI is emerging as a dominant technology
which is likely to have a profound impact on the world of
work. Its rapid adoption across industries in IT as well as non
IT sectors such as manufacturing, banking and finance,
healthcare, logistics, retail and public administration in India
is changing the employment dynamics. On the other hand, the
technology promotes more productivity, better efficiency and
greater innovation, while on the other hand, it also generates
negative effects, including replacement of workers, skills
mismatch and rising income inequality. This paper is a
literature review that tries to identify and synthesise the
impact of AI on employment in India. It draws from a vast
number of secondary data resources including policy reports,
industry studies and academic research studies.
| 85 |
Author(s):
A.P Amit Nigam, Amrita Roy, Srotoswini Sen.
Page No : 1-8
|
Implementation of a Wi-Fi Enabled CNC Plotter Using ESP32 and Smartphone Interface
Abstract
The advancement of embedded systems and wireless communication technologies has significantly transformed automation and manufacturing processes. Computer Numerical Control (CNC) machines have become essential in industries for achieving high precision, repeatability, and automation in fabrication and drawing applications. However, conventional CNC systems often require bulky desktop computers, wired communication, and expensive controllers, limiting their portability and affordability for educational and small-scale applications. To overcome these limitations, this research presents the design and implementation of a Wi-Fi enabled CNC plotter using the ESP32 microcontroller integrated with a smartphone-based control interface.
The proposed system utilizes the ESP32 microcontroller as the primary control unit due to its integrated Wi-Fi capability, low power consumption, and cost-effectiveness. Stepper motors controlled through motor driver modules provide accurate movement along the X and Y axes, while a servo motor controls the pen lifting mechanism. The system employs GRBL firmware to interpret and execute G-code instructions generated from vector graphics and CAD software. A smartphone application serves as the wireless user interface, enabling users to upload designs, control machine movement, and monitor operations remotely over a Wi-Fi network.
The research focuses on the hardware architecture, software integration, wireless communication mechanism, and motion control techniques involved in the CNC plotting system. Experimental testing was conducted to evaluate plotting accuracy, communication reliability, response time, and operational stability. The developed prototype successfully demonstrated smooth and accurate plotting operations with reliable wireless connectivity. The implementation reduced wiring complexity and improved system portability while maintaining satisfactory performance for educational, hobbyist, and lightweight industrial applications.
The study concludes that ESP32-based wireless CNC systems offer an efficient and economical alternative to conventional CNC controllers. The proposed system contributes toward the development of IoT-enabled smart manufacturing and automation technologies by integrating wireless communication and mobile-based control into CNC applications.
| 86 |
Author(s):
Manoj Gangwar.
Page No : 1-8
|
Role of Online Reviews in Shaping Student Buying Behavior
Abstract
The growing prevalence of e-commerce platforms and social media has greatly amplified the significance of online reviews in shaping consumer choices, especially among students who depend heavily on digital information before making purchases. This research explores how online reviews impact student purchasing behavior by evaluating the influence of review attributes such as valence, credibility, usefulness, and the trustworthiness of the source on purchase intentions (Nigam & Gupta, 2018). Additionally, the study delves into how consumer trust affects buying decisions. A quantitative research approach was employed, gathering primary data from 475 students through a structured questionnaire utilizing a five-point Likert scale. Data analysis involved statistical methods like the chi-square test, independent sample t-test, ANOVA, correlation analysis, and multiple regression analysis. The chi-square and ANOVA findings highlighted significant links between demographic factors and purchase intentions, while the t-test revealed variations in buying behavior between genders. Correlation analysis demonstrated a strong positive correlation between review usefulness, credibility, consumer trust, and purchase intentions. Regression analysis further identified review usefulness and credibility as the most significant predictors of purchase intentions, reinforcing previous research that emphasizes the critical role of information quality in online consumer behavior (Filieri, 2015; Zhang et al., 2024). The results also affirmed that consumer trust considerably boosts the impact of online reviews on purchasing behavior, aligning with Trust Theory and the Information Adoption Model (Sussman & Siegal, 2003; Babu et al., 2024). The study concludes that students heavily depend on reliable and informative online reviews when making purchasing decisions in digital settings.
Keywords: Online Reviews, Student Buying Behavior, Purchase Intention, Consumer Trust, eWOM, Digital Consumer Behavior
| 87 |
Author(s):
Sarthak Shivaningappa Kamble.
Page No : 1-8
|
AI Based Contact Extraction for CRM
Abstract
Traditional Customer Relationship Management (CRM) systems depend heavily on manual lead processing, fragmented customer records, and static workflows, which reduce operational efficiency and affect opportunity management. This paper presents an AI-based intelligent CRM framework that integrates automated contact extraction, lead management, and machine-learning-driven opportunity prediction within a unified system architecture. The proposed framework utilizes Natural Language Processing (NLP), intelligent lead analysis, and MERN-stack technologies to automate CRM workflows and enhance business decision-making. A Lead Conversion Prediction Model is incorporated to estimate the probability of converting leads into business opportunities based on customer interaction history, lead source, communication behavior, and engagement metrics. The backend is implemented using Node.js, Express.js, MongoDB, and JWT-based authentication, while React.js provides a responsive frontend interface. The system supports contact management, lead creation, dashboard analytics, ticket handling, and AI-assisted automation. Experimental evaluation demonstrates improved operational efficiency, reduced manual workload, scalable performance, and enhanced prioritization of high-potential leads. The proposed framework provides a scalable foundation for future AI-powered CRM systems integrating predictive analytics and real-time customer intelligence.
Key Words— Artificial Intelligence, Customer Relationship Management, Lead Conversion Prediction, MERN Stack, Natural Language Processing, Machine Learning.
| 88 |
Author(s):
Harsh Vardhan Tripathi.
Page No : 1-8
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Agri Vision
Abstract
The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and satellite-based remote observation is reshaping contemporary agriculture, shifting it from an experience-driven manual discipline into a richly data-oriented field. Although standalone machine learning models targeting crop selection, disease identification, and harvest volume estimation have individually attained impressive accuracy, existing research exposes a critical fragmentation in their real-world deployment: these modules rarely communicate within a consolidated decision-support environment. This review systematically examines the architectural requirements for a unified Sense-Analyze-Act agricultural framework. It critically assesses the effectiveness of ensemble learning approaches, notably Random Forest classifiers, for matching soil nutrient profiles to suitable crops. Convolutional Neural Networks (CNNs), particularly MobileNetV2, for lightweight field-based plant disease surveillance [11], [14]; and Long Short-Term Memory (LSTM) networks for predicting temporal patterns in commodity prices and soil moisture levels . The transformative potential of Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) in making agronomic guidance accessible to resource-limited smallholder farmers is also thoroughly examined. This review highlights key obstacles to deployment, namely cross-domain generalization failures, algorithmic biases stemming from unrepresentative training data, and the absence of robust multimodal sensor fusion architectures. Finally, a forward-looking research agenda is proposed, emphasizing Federated Learning approaches and autonomous unmanned aerial vehicle (UAV) surveillance programs as pathways toward bridging the gap between demonstrated algorithmic promise and smallholder operational reality.
Keywords: Precision agriculture, ensemble learning, convolutional neural networks, long short-term memory networks, multimodal artificial intelligence, smart farming, remote sensing, federated learning, retrieval-augmented generation, plant disease detection.
| 89 |
Author(s):
Malaika Matheen Khan, Dr. Sadiya Nair.
Page No : 1-9
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Passive Vs Active Social Media Use and it’s Impact on Self-Esteem
Abstract
This study investigated the relationship between self-esteem and active/passive usage patterns of social media in 56 participants (18-30). Average levels of usage for both active and passive usage were identified to be moderate overall, and slightly higher for passive usage. No statistically significant relationships between levels of usage and levels of self-esteem were found. While males utilized passive usage more, and females actively utilised more, older adults aged 26-30 expressed the highest self-esteem levels and the lowest usage, indicating the potential for life stage/age to be a greater factor than usage levels alone.
| 90 |
Author(s):
Dhanush M.
Page No : 1-9
|
Digital and modern marketing focus on greenviron sustainable design consultants
Abstract
Digital and modern marketing refers to the use of online platforms, digital technologies, and innovative strategies to promote products and services. In today's competitive business environment, companies are increasingly relying on digital marketing techniques to reach a wider audience and build stronger customer relationships.For companies like Greenviron Sustainable Design Consultants, digital marketing plays a crucial role in promoting eco-friendly architecture, sustainable design solutions, and green building consultancy services. Through digital platforms, businesses can communicate their environmental values and showcase their projects to potential clients globally.
| 91 |
Author(s):
Pathan Farhana.
Page No : 1-9
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A Lightweight Hybrid System for Crowd Stampede Prediction Using UAV-Based SSIM Analysis and RFID Sensor Fusion
Abstract
Crowd stampedes continue to be a serious concern
during large gatherings, mainly because they occur suddenly and
are difficult to foresee. In this project, I worked on developing
a simple system that can give an early indication of when such
a situation is beginning to form. The approach brings together
three kinds of inputs: live aerial footage from a UAV, a few
reference images taken from known stampede events, and basic
movement information collected through RFID tags worn by
people in the crowd. Each incoming video frame is checked
against the reference images, and whenever the similarity appears
unusually high, the RFID readings are examined to understand
how the crowd is behaving at that moment. Using these readings,
the system calculates a probability score and then converts
it into an entropy value to estimate how stable or unstable
the situation is. If the entropy becomes too low or too high,
the system triggers a warning by activating a hooter so that
the authorities can respond before the situation worsens. By
combining very lightweight visual comparison with simple sensor
data, the method aims to offer a practical and fast way to detect
the early signs of a potential stampede.
Index Terms—Crowd Monitoring, Stampede Prediction, UAV,
Drone Surveillance, SSIM, Real-Time Analysis
| 92 |
Author(s):
Monisha Devarajan.
Page No : 1-9
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NAVIGATING MARKET WITH FINANCIAL METRICS
Abstract
In today’s dynamic business environment, the intersection of marketing and finance has become increasingly crucial for companies aiming to achieve sustainable growth and maximize profitability. Financial metrics used to evaluate and assess the financial performance, health, and stability of a company or an investment. Financial metrics play a pivotal role in shaping marketing strategies and assessing their effectiveness in achieving business objectives. From revenue and profitability to the cost incurred to attract new customers, various key indicators will help to identify the effectiveness of marketing activities. This paper provides an overview of the various financial metrics used in marketing activities and how the financial measures are utilised to evaluate marketing performance and inform decision-making. The paper begins by elucidating the significance of financial metrics in marketing. It then proceeds to discuss the key financial metrics commonly used in marketing and each metric is examined in terms of its definition, calculation methodology, and practical application in assessing marketing effectiveness. It also addresses the challenges associated with financial metric implementation, including data availability, accuracy, and interpretation as well as consideration for overcoming these obstacles. This study explores the integration of financial metrics into the marketing analytics framework.
| 93 |
Author(s):
Pranjal Sharma.
Page No : 1-9
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MATLAB/Simulink-Based Optimization of Power System Stabilizer Parameters Using a Genetic Algorithm
Abstract
Oscillations caused by frequency in lower values has potential to cause great Damage to Stability of transient type in the electrical
power system grids. We need to use power system stabilizers i.e. PSS to make sure these oscillations doesn’t last long by injecting damped
signals inside generator of excited loop. In the past, researchers tried to use linear based models for tuning of PSS based but when we actually
apply this into the real world application it fails to deliver what we require. Therefore in this Paper work, we used matlab software to create
simulation based framework. We used Global Optimal Toolbox inside MATLAB for genetic algorithm to find the best possible PSS gain &
lead lag based time constant represented by T for single machine based infinite bus system. we have designed an new method of terminal
absolute error which is better than olde metrices. This method measures leftover speed and deviation of angle every 5 seconds which relates
to grid code based recovery constraint. We build 3 models on Simulink to test our genetic algorithm approach including without PSS, With
classic PSS and With Genetic Algorithmically optimized WithPSS model. This GA found the optimal value to be around 0.0616563 after
completing 18 generations. The precise parameters wee found to be K= 9.313778 & T = 0.030270s. Under the settling time of 4.5s,this GA
Based PSS overcomes the problem of oscillation with much less overshoot when comparing it to the benchmark results.
Index Terms—Genetic Algorithm, Power System Stabilizer, Transient Stability, MATLAB/Simulink, Terminal Absolute Error, SMIB,
Heuristic Optimization, Lead-Lag Compensator
| 94 |
Author(s):
Tejaswi Kale, Swarupa Khaire, Sanika Sonawane, Shraddha Dhavale, Prof. Soniya Dhotre, Prof. Tejal Rane, Dr. Sandeep Kadam.
Page No : 1-11
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AI-Enabled Intelligent E-Library Ecosystem: A PWA-Based Framework with Predictive Risk Analytics and Dual-Portal Architecture
Abstract
The evolution of digital learning environments requires library systems that move beyond basic catalog automation
toward intelligent, user-centric ecosystems. This research introduces an Intelligent E-Library Ecosystem developed as a Progressive Web Application (PWA) to enhance accessibility, automation,
and operational transparency within academic institutions.
The proposed system incorporates several advanced features
that are rarely unified within conventional library platforms,
including a formalized lost-book reporting workflow, real-time
seat reservation management, automated due-date reminders,
an integrated online PDF reader, a centralized digital notice
board, predictive borrower risk analysis, and scheduled email
notification services. A secure dual-portal architecture separates
administrative control from student interaction, ensuring structured access management and data integrity.
A Python–Flask-based machine learning microservice performs behavioral risk assessment to support proactive decisionmaking. Offline functionality is enabled through Service Worker
implementation, ensuring continuity of access in low-connectivity
environments. Experimental validation demonstrates improvements in process efficiency, user engagement, monitoring accuracy, and administrative responsiveness when compared to
traditional manual and semi-digital library systems.
Index Terms—Library Automation, Progressive Web App
(PWA), Machine Learning, E-book Management, Late Fee Calculation, Lost Book Claim, Role-Based Access Control, Notice
Board.
| 95 |
Author(s):
Pranshul Nikam.
Page No : 1-11
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ENHANCING SOCIAL WELFARE OUTCOMES THROUGH AN AI-DRIVEN DUAL-PLATFORM FRAMEWORK FOR GOVERNMENT AND NON-GOVERNMENTAL ORGANIZATIONS
Abstract
In the contemporary landscape of digital governance, the equitable delivery of state-sponsored welfare schemes to intended beneficiaries remains a persistent and structurally complex challenge, particularly within demographically diverse and socio-economically stratified nations. Despite the conceptualization and robust fiscal allocation of numerous governmental welfare programs, the marginalized populations they are specifically designed to serve are frequently excluded from their benefits due to compounding systemic barriers: pervasive information asymmetry, convoluted eligibility documentation requirements, the absence of proactive eligibility inference tools, and a profound lack of spatial intelligence available to field intervention agencies. This research presents Sabal, a unified, cloud-native, dual-platform technological ecosystem engineered to systematically dismantle these intersecting barriers through the application of modern artificial intelligence, asynchronous web architecture, and geospatial analytics.
The Sabal ecosystem comprises two architecturally distinct yet data-synchronized platforms. The first, Sabal Setu, is a citizen-centric portal that employs a rule-based demographic eligibility engine to proactively match individual citizens against a structured repository of over fifty-seven active governmental schemes. It further incorporates a multimodal AI document vault powered by the Google Gemini 2.0 Flash API to automate the extraction of identity and demographic data from uploaded civic documents, eliminating the manual transcription bottleneck that causes a disproportionate rate of application rejection among low-literacy demographics. The second platform, Sabal AI, functions as an enterprise-grade intelligence dashboard designed for Non-Governmental Organizations and administrative coordinators. It leverages interactive geospatial mapping via a react-leaflet rendering engine to visualize civic service gaps across geographic zones, and computes a proprietary Social Return on Investment metric to empirically optimize on-ground resource deployment decisions. Field directives are further enriched by a generative AI layer that synthesizes zone-specific demographic statistics into actionable natural-language Field Intelligence Briefs.
The complete system is built upon a highly scalable monorepo framework utilizing React 18 and TypeScript on the frontend, Node.js with Express.js on the backend, and PostgreSQL managed through the Prisma ORM for type-safe, ACID-compliant relational data persistence. Security across the platform is enforced via JWT stateless authorization and bcryptjs cryptographic password hashing. Experimental evaluation using synthetic demographic datasets demonstrates the ecosystem's capacity to accurately surface eligible scheme recommendations, autonomously extract structured identity data from heterogeneous civic documents, and generate empirically ranked geographic intervention priority lists for NGO deployment. This paper presents the complete architecture, computational methodology, and functional outcomes of the Sabal ecosystem, advancing a scalable and empirically grounded paradigm for next-generation inclusive digital governance.
| 96 |
Author(s):
Roma Chaurasia .
Page No : 1-12
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ANALYZING THE EFFECT OF DATA IMPUTATION TECHNIQUES ON CLINICAL PREDICTION MODELING
Abstract
In the rapidly evolving landscape of healthcare AI, clinical prediction models hold immense promise for early disease detection, patient triaging, and personalized treatment. However, the real-world clinical datasets powering these models are notoriously imperfect frequently plagued by missing values due to irregular patient monitoring, disjointed electronic health records (EHR), or human error. How we handle these data gaps can ultimately make or break a model's clinical viability.
The current work examines systematically the effect that several data imputation procedures have on the efficiency, equity, and validity of predictive models. We examine a broad range of missing data handling techniques, starting from straightforward conventional ones (such as mean/median imputations), conventional statistical procedures like multiple imputation by chained equations (MICE), up to sophisticated algorithms for data imputation such as k-nearest neighbors (kNN), or even deep learning. To do this, we use a set of clinical data sets with several missing data patterns (MCAR, MAR, and MNAR) and then estimate the performance of the predictive models developed.
Our study shows that the imputation method is not only a pre-processing step, but an important design step that greatly affects the sensitivity of the model, be it bias present or absent, and finally the fairness of the predictions of the algorithm. Ultimately, our work provides researchers and data scientists with a way to choose the imputation technique that is most appropriate for their clinical case.
| 97 |
Author(s):
Kangna Gupta.
Page No : 1-12
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Career Aspiration Differences among Management Students: A Comparative Study of Finance, Marketing, and HR Specializations
Abstract
Career aspirations significantly influence students' educational decisions, specialization choices, and long-term professional growth. In management education, students opt for specializations like Finance, Marketing, and Human Resource Management (HRM), which shape their career objectives, expectations, and professional orientations. This study explores the variations in career aspirations among management students across these specializations and examines how career interest, career decision-making self-efficacy, outcome expectations, and social influence affect career aspirations. The study is based on Social Cognitive Career Theory, which posits that career aspirations are influenced by self-efficacy beliefs, anticipated outcomes, and environmental factors (Robert W. Lent et al., 1994; Lent & Brown, 2019). A quantitative research approach was employed, gathering data from 480 undergraduate and postgraduate management students through a structured questionnaire rated on a five-point Likert scale. Statistical methods such as descriptive statistics, correlation analysis, regression analysis, and one-way ANOVA were utilized to analyze the data. The results indicated notable differences in career aspirations among Finance, Marketing, and HR specializations. Finance students exhibited stronger salary-driven aspirations, while Marketing students favored dynamic and creative career roles, and HR students were more inclined towards people-oriented careers. Additionally, career interest and self-efficacy had a significant impact on career aspirations (Nguyen & Tran, 2022; Bakar & Abdullah, 2023), with outcome expectations being the most powerful predictor of career aspirations (Chen & Liu, 2024). Social influence also positively affected students' career goals (Kim & Park, 2022). The study adds to the career development literature and offers practical insights for educators, career counselors, and policymakers in creating specialization-specific career guidance programs.
Keywords
Career Aspirations, Academic Specialization, Finance, Marketing, Human Resource Management, Self-Efficacy, Outcome Expectations, Social Cognitive Career Theory
| 98 |
Author(s):
Rushikesh v. more.
Page No : 1-12
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Role of Artificial Intelligence in Modern Digital Marketing
Abstract
Artificial Intelligence (AI) has become a disruptive technology in the modern digital marketing which
helps the businesses to improve the efficiency, accuracy and customer engagement This research studies
the role of AI in digital marketing and its effect on marketing strategies and consumer behavior.
and its effect on marketing strategies and consumer behavior. The principal aim of this research is to
explore the use of AI tools and technologies to enhance marketing performance and deliver
personalized customer experiences.
The study relies on primary as well as secondary data. Primary data is collected by Surveys &
questionnaires, Secondary data is collected by Research articles, Journals & online sources. The
research uses a descriptive approach to investigate the use of AI in various marketing activities such as
customer segmentation, targeted advertising, content generation, and customer support..
| 99 |
Author(s):
Piyush Kumar, Mohit kumar, Rimmy Chhabra .
Page No : 1-12
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TCP vs UDP
Abstract
The Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) constitute the two foundational pillars of the internet's transport layer, codified in IETF RFC 793 and RFC 768 respectively. Despite being standardized over four decades ago, both protocols remain central to modern networked applications ranging from cloud-scale distributed systems and financial trading infrastructure to real-time multimedia delivery and Internet of Things (IoT) deployments. Yet the choice between them is frequently misunderstood, made by convention rather than careful analysis, or deferred to framework defaults — leading to suboptimal system performance, unnecessary latency, or fragile reliability guarantees.
This paper presents a rigorous, multi-dimensional comparative analysis of TCP and UDP, examining them through five complementary lenses: (1) formal protocol architecture and state-machine semantics, (2) mathematical performance models grounded in queuing theory and information theory, (3) empirical benchmarking under diverse network conditions using a controlled testbed, (4) security threat landscape and known attack vectors, and (5) relevance to next-generation protocols including QUIC, MPTCP, DCCP, and SCTP.
Our experimental results demonstrate that TCP throughput degrades by up to 94% at 10% packet loss compared to near-linear degradation for UDP, while UDP's first-packet latency advantage ranges from 23% to 150% depending on connection frequency and RTT. We further quantify the head-of-line blocking penalty in TCP multiplexing environments, derive closed-form approximations for optimal buffer sizing, and present a structured decision framework mapping application requirements to protocol selection criteria.
This work aims to serve as a definitive reference for network engineers, systems architects, and researchers navigating protocol selection in an era of increasingly diverse application requirements and rapidly evolving transport-layer standards.
| 100 |
Author(s):
Anushka Shrivastava.
Page No : 1-13
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A Study on Digital Payments, Customer Convenience and Sales Growth of small vendors
Abstract
Abstract
The rapid diffusion of Unified Payments Interface (UPI) and related digital payment instruments across India's informal economy has opened up fresh avenues for investigating how technology adoption shapes business outcomes at the micro level. This paper examines the interplay between digital payment adoption, customer convenience, and sales growth among sixty small vendors operating in the Pitam Pura commercial district of North Delhi. Drawing on structured survey data and grounding the analysis in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study operationalises five adoption drivers — Perceived Ease of Use, Perceived Usefulness, Perceived Risk, Trust and Security, and Social Influence — and tests their effects on a carefully constructed Customer Convenience construct and on Sales Growth. Multiple regression and Baron-Kenny mediation analysis reveal that Customer Convenience is the single strongest predictor of Sales Growth (β = 0.791, p < 0.001) and partially mediates the adoption-performance relationship. Perceived Usefulness and Trust and Security emerge as the dominant positive drivers, while Perceived Risk exerts a significant negative influence on convenience perceptions. All seven hypotheses proposed by the study find empirical support. The findings carry concrete implications for vendors, payment technology providers, financial institutions, and policymakers seeking to deepen digital financial inclusion in India's large informal sector.
| 101 |
Author(s):
Dias Abdrakhmanov.
Page No : 1-13
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Intelligent Routing to Optimize Energy Consumption in Wireless Sensor Networks: A Distributed Approach with Reinforcement Learning
Abstract
Вот абстракт готовый к вставке в форму (195 слов, в рамках лимита 200):
Energy depletion heterogeneity — not total consumption — determines when wireless sensor networks fail: nodes at traffic hotspots exhaust while peripheral nodes retain 60–80% residual charge, collapsing coverage prematurely. Existing clustering protocols such as LEACH and HEED address efficiency through hierarchical organization, yet their periodic reformation cycles leave them structurally blind to topology changes between updates, and no existing distributed protocol simultaneously optimizes residual energy, transmission distance, and node load using only local information while adapting those weights online through reinforcement learning. This paper presents DEAR (Distributed Energy-Aware Routing), a protocol that makes per-transmission forwarding decisions via a three-term cost function C(n) = α·E_r(n) + β·d(n) + γ·L(n), requiring no global state. DEAR-RL extends this by embedding Q-learning at cluster heads to update weights each round based on observed network conditions. Both protocols were evaluated in MATLAB across six competitors, four network scales (N ∈ {50, 100, 200, 300}), and five environmental scenarios with 50 independent runs per configuration. DEAR raises Half-Node-Death (HND) from 938 to 1,127 rounds versus HEED at N=100 — a 20.1% gain (p<0.001, d=1.31) — while reducing energy balance variance by 44%. Under node mobility, the advantage grows to 35.2%. DEAR-RL adds 8.2% over DEAR in baseline and 13.2% under mixed conditions at only 1.1% computational overhead.
Keywords: wireless sensor networks; energy-efficient routing; reinforcement learning; network lifetime; distributed algorithm; clustering protocol; Q-learning
| 102 |
Author(s):
Ayushi Singh.
Page No : 1-14
|
Cognitive Alignment in Multi-Agent Generative AI Systems: A Framework for Trustworthy Collaborative Intelligence
Abstract
The rapid evolution of generative artificial intelligence (AI) has led to the emergence of multi-agent systems capable of autonomous reasoning, collaboration, and decision-making. However, ensuring alignment among multiple AI agents and human intent remains a critical challenge. This paper introduces a novel concept termed Cognitive Alignment in Multi-Agent Generative Systems (CAMAGS), focusing on how multiple AI agents can maintain consistent reasoning, ethical alignment, and cooperative behavior. We propose a hybrid framework combining neuro-symbolic reasoning, alignment constraints, and self-reflective feedback loops. The study evaluates emerging risks such as hallucination propagation, agent conflict, and ethical drift. Results suggest that cognitive alignment mechanisms significantly improve trust, reliability, and scalability in collaborative AI ecosystems.
Keywords: Artificial Intelligence, Multi-Agent Systems, Alignment, Generative AI, Trustworthy AI, Neuro-symbolic AI
| 103 |
Author(s):
Mohammed Afwan.
Page No : 1-22
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AN AI-AUGMENTED HYBRID RENEWABLE ENERGY SYSTEM FOR INDIAN RAILWAY STATIONS: A CASE STUDY AT MUMBAI CENTRAL TERMINUS
Abstract
The Indian Railways consumes about 20 billion units of electricity in a year, constituting about 2.4 percent of the national electricity demand. The Railway Ministry has pledged to achieve net-zero carbon emissions from its operations by 2030. Most of the electricity consumed by Railways is for traction. However, considerable demand exists at the station level. Therefore, in the absence of any traction-related savings, an on-site renewable energy system at major railway stations can help the Ministry achieve its net-zero carbon emissions goal. The present study focuses on sizing and simulating an integrated renewable energy system at Mumbai Central Terminus. The system is designed to incorporate 1.41 MWp of rooftop solar PV, piezoelectric energy harvesting from footfalls of passengers, piezoelectric energy harvesting from rail vibrations, and thermoelectric energy recovery from the waste heat of the air conditioning system. The system is buffered by a 3.1 megawatt-hour lithium-iron-phosphate battery and connected through a 1.5 megavolt-ampere inverter to the grid. Each subsystem is sized from first principles using internationally accepted methods, with all governing equations and substituted values reported explicitly so the calculations can be reproduced. The same physical hardware is then simulated under two control strategies in MATLAB and Simulink. The conventional version uses today's standard controllers, while an artificial-intelligence-augmented version replaces them with a long short-term memory forecaster, neural-network maximum-power-point trackers, a Twin-Delayed Deep Deterministic Policy Gradient reinforcement-learning energy manager, and an XGBoost-with-One-Class-Support-Vector-Machine predictive maintenance pipeline. Across a 24-hour and 30-day benchmarking horizon, the AI layer raised annual renewable yield by 5.9 percent, reduced grid imports by 26 percent, cut daily battery cycling by 23 percent, and provided multi-week warning of four common failure modes. Renewable share of station load grew from 51 percent to 58 percent, all at zero additional capital cost on the physical plant. Replicated across more than two thousand already solarised stations on the Indian Railways network, the same software-only upgrade represents roughly 270 gigawatt-hours of additional renewable energy each year, comparable to a 150-megawatt utility-scale solar plant, with no extra panels, land, or batteries.
| 104 |
Author(s):
Parupalli sirisha.
Page No : 1-28
|
A STUDY ON EMLOYEES SATISFACTION AT RATTA AVANEE PVT LITS
Abstract
The rapid growth of social media platforms has significantly transformed the way businesses
communicate with consumers, making social media marketing a critical component of
modern marketing strategies. This study examines the impact of social media marketing on
customer purchase intention by analysing key factors such as content quality, interactivity,
trust, brand engagement, and electronic word-of-mouth. Using data collected from consumers
active on social media platforms, the research employs quantitative analysis to assess the
relationship between social media marketing activities and consumers’ willingness to
purchase. The findings indicate that social media marketing has a positive and significant
influence on customer purchase intention, with interactive content and perceived credibility
playing a particularly strong role. These results provide valuable insights for marketers
seeking to enhance customer purchase intention through effective social media marketing
practices.
