Volume 14 | Issue 4

 Abstract

Fraud detection has become a critical challenge in modern digital ecosystems due to the exponential growth of data and increasingly sophisticated fraudulent activities. Traditional rule-based systems are no longer sufficient to detect complex fraud patterns in real-time environments. This research paper explores the integration of Big Data Analytics and Machine Learning techniques to enhance fraud detection capabilities. The study focuses on scalable data processing frameworks such as Hadoop and Spark, combined with machine learning models like Decision Trees, Random Forest, and Logistic Regression. The proposed approach leverages large-scale structured and unstructured datasets to identify anomalies and predict fraudulent transactions with improved accuracy. Experimental analysis demonstrates that machine learning-based models significantly outperform traditional systems in terms of precision, recall, and scalability.

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 Keywords

Fraud Detection, Big Data Analytics, Machine Learning, Anomaly Detection, Credit Card Fraud, Apache Spark, Predictive Modeling, Neural Networks

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India's aspiration to become a global inspirator depends significantly on the development of a future-ready workforce that is not only skilled but also socially responsible and sustainable. Although initiatives such as the Skill India Mission and various vocational training programs have expanded opportunities for skill development, challenges such as low employability, rural-urban skill disparities, and a deficiency in value-based education persist. In this regard, Mahatma Gandhi's Nai Talim (Basic Education) philosophy presents a transformative approach by integrating productive work, ethical values, and community orientation into the educational framework. This paper re-examines Nai Talim as a model for skilling India, emphasising its fundamental principles of experiential learning, the dignity of labour, self-reliance, and the comprehensive development of Head, Heart, and Hand. It contends that Nai Talim's work-centric and value-integrated approach can effectively bridge the gap between education, employability, and sustainability. The study also analyses the role of the National Education Policy (NEP) 2020, which embodies Gandhian ideals by promoting experiential learning, vocational integration from Grade 6 onwards, local crafts, and entrepreneurship incubation. The NEP's emphasis on Indian Knowledge Systems, skill laboratories, and community-based training centres aligns with Gandhian thought, rendering it a robust framework for inclusive and sustainable workforce development. The paper concludes that a hybrid model, integrating Nai Talim's ethical foundation with contemporary vocational frameworks under NEP 2020, can assist India in achieving the dual objectives of economic growth and social equity. Such an approach would not only foster a skilled and innovative workforce but also cultivate responsible citizens dedicated to inclusive and sustainable national development.

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 Keywords

Nai Talim, Gandhian Philosophy, Skill Development, NEP 2020, Indian Knowledge Systems (IKS), Inclusive Growth, Workforce Sustainability, Experiential Learning, Sustainable Development Goals

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This paper introduces Beautex - a smart system made for salons and parlours that runs on both web and mobile devices, aiming to make daily tasks smoother while boosting how customers engage. Instead of locking features behind sign-ups, people can freely check out local spots, what services they offer, and how much things cost. Logging in only kicks in when booking a slot, which needs at least half the price paid upfront; going all-in early gives extra built-in rewards setup. The customer side helps users find services easily, see package deals, get auto alerts for appointments, while feedback only shows up after a real booking. Different ways to pay like scanning a code at the shop keep payments clear and smooth. On top of that, people can check out beauty items suggested by the salon, order them straight from the app, then grab an e-invoice right away, giving salons another way to earn. From day-to-day admin tasks, Beautex gives salon head one main screen to check money flow, track spending, keep tabs on stock, understand client habits, set staff shifts while also helping hire new team members. Instead of guessing, owners get smart tools that support quick choices plus big-picture strategy. A chatbot run by AI takes care of booking requests and answers common questions without delay. Coming updates will let salons handle refunds directly, launch an app for iPhones, even show clients how hairstyles might look using artificial intelligence making the project ready to grow alongside changing needs.

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 Keywords

Centralized platform for parlors and saloon , loyalty point system , chat bot

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The proposed project is about creating a complete machine learning system that helps industries predict how much energy they will use and also check how efficiently a smart grid system is working. The main aim of the project is to manage energy in a smarter and more efficient way. It combines energy prediction and efficiency checking into one connected system. This helps industries reduce energy waste and improve their overall performance. The system uses modern technologies like machine learning and data analysis to make accurate predictions. It is designed to be practical and useful in real industrial environments, especially in industries like steel manufacturing where energy usage is usually very high. By predicting energy usage early, companies can plan their operations better and avoid sudden increases in electricity demand. This also helps reduce electricity costs and maintain stable power systems. In the first stage of the project, machine learning models are trained using a dataset collected from a steel industry. This dataset contains information about how machines use electricity during different operations. The system uses two important models called Random Forest and Long Short-Term Memory (LSTM). These models are good at finding patterns in complex data and understanding how different factors affect energy usage. For example, they analyze values such as load type, power factor, voltage, current, and other electrical measurements. These values change over time, and the models learn how these changes influence energy consumption. The Random Forest model works by combining many small decision trees to produce accurate predictions. It is useful for handling large amounts of data and identifying the most important factors that affect energy usage. The LSTM model is a type of deep learning model that is specially designed to understand time-based data. It can remember past information and use it to predict future values. This makes it very suitable for predicting energy consumption because energy usage usually follows patterns over time. Using both models together improves prediction accuracy. After training the models, the system predicts short-term energy consumption in units called kilowatt-hours (kWh). These predictions show how much energy the industry will likely use in the near future. The predicted energy value is then added to another dataset called a synthetic smart grid dataset, which represents how electricity flows through a smart grid system. A smart grid is a modern electricity network that uses digital technology to monitor and control energy distribution. In the next step, the system calculates an Energy Efficiency Score based on the predicted energy consumption. This score shows how efficiently the grid is using energy. A higher score means better efficiency, while a lower score means energy is being wasted. The system uses fixed threshold values to classify the performance as either efficient or inefficient. This makes it easy for users to quickly understand the condition of the grid. Another machine learning model called a Random Forest Classifier is then used to predict grid efficiency using factors such as power demand, renewable energy contribution, weather conditions, temperature, and environmental data. The model analyzes these factors and predicts whether the system will operate efficiently. It also shows which factors are most important in affecting energy efficiency, helping engineers make better decisions to improve performance. Finally, the entire system is connected to a web application . Users can view results through dashboards and charts in a simple and user-friendly interface. Overall, this project provides a smart and scalable solution for energy management by helping industries save energy, reduce costs, improve system stability, and support sustainable energy practices.

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 Keywords

Keywords: Energy Forecasting, Smart Grid, Machine Learning, Random Forest, LSTM, Energy Efficiency, Time Series Prediction, IoT, Predictive Analytics, Sustainable Energy

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This research presents a Hybrid Classical-Quantum Investment Recommendation Framework aimed at improving retail investor profitability through flexible, multi-asset portfolio structuring. To avoid the processing limits and fractional-share restrictions found in traditional models like Modern Portfolio Theory, this new system uses a Two Phase Dynamic Market Screener. This tool assesses high-momentum stocks alongside safer capital assets, specifically Digital Gold and Fixed Deposits. For accurate short-term price prediction, the platform employs a combination of classical machine learning algorithms, such as ARIMA and LSTM. At the same time, it integrates Generative AI (Gemini) to evaluate real-time macroeconomic news sentiment, turning complex market information into clear and understandable investment reasons. A key feature of this system is an automated Risk Profiler that continuously adjusts asset allocation based on user demographics. One example is a mandatory "Capital Protection Mode" aimed at protecting the investments of senior citizens. To handle complex portfolio construction, the framework uses the Quantum Approximate Optimization Algorithm (QAOA). By framing the allocation task as a 0-1 Knapsack combinatorial problem, the system ensures mathematically superior and fully executable whole share distributions. Supported by an integrated Smart Tax Advisor focused on automated tax loss harvesting, this system acts as a responsive and investor-oriented model for future FinTech ecosystems.

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Portfolio Optimization, Quantum Computing, Risk Analysis, Behavioral Finance, Hybrid Classical-Quantum Algorithms, Financial Forecasting.

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The rapid growth of online shopping has significantly transformed consumer behavior, introducing new trends and challenges in purchasing habits. This study examines the impact of online shopping on consumer behavior by analyzing key factors such as frequency of purchases, spending patterns, and customer satisfaction. A survey of 200 consumers from Nashik was conducted to explore demographic influences, including age and gender, on shopping preferences and habits. Findings indicate that convenience, product variety, and competitive pricing are major drivers of online shopping, while challenges such as delivery delays and lack of physical inspection affect consumer satisfaction. The study reveals a growing inclination toward online shopping across diverse age groups, with younger consumers demonstrating higher engagement levels. This research contributes to understanding the dynamic relationship between technology adoption and consumer decision-making, offering insights for businesses to tailor their strategies in an increasingly digital marketplace.

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Online shopping behaviour, Consumer behaviour, E-commerce, Online shopping trends, Consumer preferences, Digital shopping, E-commerce platforms, Customer satisfaction, Shopping frequency, Consumer purchasing decisions, Shopping patterns, Product variety, Brand loyalty, Consumer trust, Payment methods, Delivery satisfaction, Consumer reviews and ratings, Product quality, Customer service, Return and exchange policies, Consumer complaints, Convenience in online shopping, Demographic factors, Consu

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Teacher education plays a pivotal role in shaping the quality of future educators, but the growing demands on teacher educators themselves often go unrecognized. This study investigates the phenomenon of burnout among teacher educators working in Colleges of Education, with two primary objectives: (1) to assess the level of burnout among these professionals, and (2) to compare burnout levels between male and female teacher educators. Burnout, characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, has been identified as a significant factor affecting the efficiency, motivation, and mental well-being of educators. Using a descriptive survey method, data were collected from a representative sample of teacher educators across various institutions through a standardized burnout inventory. The results indicate that a substantial proportion of teacher educators experience moderate to high levels of burnout. Gender-based analysis reveals that female educators tend to experience higher emotional exhaustion, while male educators report greater de personalization. The study also explores potential causes, including workload, institutional pressures, role ambiguity, and lack of administrative support. The impact of burnout extends beyond individual educators, influencing the overall quality of teacher education programs and the learning experiences of future teachers. The findings emphasize the need for institutional strategies such as workload management, mental health resources, and professional support systems to address and reduce burnout among teacher educators.

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Teacher Education, Burnout, Teacher Educators, Emotional Exhaustion.

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 Abstract

Anita Desai's Cry, the Peacock creates a subtle literary field to analyze the interrelations between ecological imperialism and psychological fragmentation. The protagonist, Maya, is a complex character whose mental instability can be closely related to her alienation of the natural world. Her disintegrated consciousness is an expression of a personal traumas as well as a larger cultural situation that is defined by the instrumentalization and domination of nature. Desai creates a story in which nature ceases to be a caring entity with the heavy use of image, symbolic landscapes and the air of oppressive isolation in which the author creates a world where nature is no longer the mother. This domination of the natural space resembles imperialistic forms of domination, as land as well as subjectivity is dominated and shattered. The depletion of Maya as inner being becomes symbolic, therefore, of a greater epistemic and ecological crisis.

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Anita Desai, Ecological, Imperialism, Maya, Exploitation.

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The "ELITZ VEHI: Online Vehicle Service & Modification Management System" is a web-based platform designed to simplify and digitize the process of vehicle service booking and modification management. The main objective of this system is to provide a seamless connection between vehicle owners and service providers, enabling users to request services such as general maintenance, vehicle modifications, and emergency assistance through an easy-to-use interface. The system is developed using modern web technologies, with a backend powered by Node.js and Express.js and a database managed using MongoDB to ensure efficient data storage and retrieval. The platform supports multiple users, including customers and service centers. Customers can register, log in, and book services based on their requirements and location, while service providers can access a dashboard to view and manage incoming service requests. One of the key features of this system is location-based service availability, which helps users find nearby service centers quickly, improving response time and convenience. All service requests are stored in the database and displayed in real time to the respective service providers, ensuring smooth communication and efficient workflow management. Overall, the system enhances user convenience, reduces manual effort, and improves service accessibility by providing a centralized digital solution for vehicle service and modification management. It aims to deliver faster, reliable, and user-friendly service experiences, making it an effective tool for both customers and service providers.

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Online Vehicle Service System - Vehicle Modification Management - Web-Based Application - MERN Stack - Service Booking System - MongoDB Database ? Clean ? Same style as your friend's paper ? Ready to copy-paste

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 Abstract

???????? ??? ???? ?? ???????? ?????? ????? ?????? ?? ???????, ?????-?????? ????????? ??? ???????? ?????? ?? ???????? ???? ?? ??? ????????? ?????? ???? 2020 ?? ???? ?????? ?? ???????? ???? ??? ?????? ????? ??????, ??????? ??????, ???????, ?????????? ?????, ?????????? ????? ??? ?????????? ???? ?? ?????? ?? ?????? ??, ????? ?????? ???? ??????? ????? ???? ????? ?????? ??????? ?? ?????????? ?? ????? ????? ?? ????????? ???? ??? ????????? ?????? ???? 2020 ?? ?????? ?? ????????, ????-??????, ???????? ??? ????? ????? ?????? ????? ?? ?? ???? ??? ?? ?????? ?? ?? ??? ?????? ????? ?????? ?? ????? 2020 ?? ?????????? ?? ???? ??????? ?????? ??? ??????? ??????????? ?? ???????? ???? ??? ?????? ?? ?????? ???? ?? ?? ??????? ?????? ??????? ???????? ?? ?? 21??? ??? ?? ?????????? ???? ????? ??????? ?? ?????, ?????????? ?????, ??????????? ??? ?????????? ?????? ?? ?????? ??? ?????????? ????? ?? ???? ????

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??????? ?????? ??????, ????????? ?????? ???? 2020, ?????? ????? ??????, ??????????, ??????? ?????, ??????? ???????

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Psoriasis is a chronic, immune-mediated skin disorder marked by hyperproliferation of keratinocytes and inflammation. Conventional therapies often involve corticosteroids and immunosuppressants, which may cause adverse effects with prolonged use. Herbal actives such as turmeric (Curcuma longa), aloe vera, salicylic acid, and lactic acid offer promising alternatives due to their anti-inflammatory, keratolytic, and skin-repairing properties. This review explores the pharmacological basis, mechanism of action, formulation strategies, and clinical relevance of a topical herbal gel designed to alleviate psoriatic symptoms. The article also discusses gel-forming agents, preservative systems, and evaluation parameters for stability and efficacy.

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Herbal Anti-Flake Gel for Psoriasis

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The present study aims to examine the mental health status of undergraduate students and to identify gender differences in their mental health levels. Mental health plays a vital role in the overall development, academic performance, and emotional stability of students in higher education. In recent years, increasing academic pressure, social expectations, career uncertainty, and lifestyle changes have significantly influenced the psychological well-being of university students. Considering the importance of mental health in students' academic and personal lives, the study was conducted to compare the mental health of male and female undergraduate students. The research adopted a descriptive survey method to collect and analyze data. The population of the study consisted of undergraduate students studying in colleges affiliated with Rabindranath Tagore University, Raisen, Madhya Pradesh. A sample of 400 undergraduate students was selected using a random sampling technique, which included 173 male students and 227 female students. A standardized Mental Health Scale was used as the research tool for measuring the mental health status of the participants. The collected data were analyzed using statistical techniques such as mean, standard deviation, standard error of mean, standard error of difference, and z-test. The findings of the study revealed that female undergraduate students obtained higher mental health scores compared to male students. The calculated z-value indicated that the difference between male and female students was statistically significant. The study highlights the importance of promoting mental health awareness and providing psychological support services in higher education institutions. It also emphasizes the need for universities to implement counseling programs, stress management workshops, and mental health education initiatives to support the overall well-being and academic success of students.

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Keywords Mental Health, Undergraduate Students, Gender Differences, Psychological Well-being, Academic Stress, Emotional Stability, Higher Education, Student Development, Mental Health Status, Counseling Services, Stress Management, Educational Psychology

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 Abstract

In many higher educational institutions, campus recruitment activities are often managed using fragmented com- munication channels, resulting in inefficiencies, missed oppor- tunities, and limited monitoring. Existing systems also lack the capability to proactively identify students who may face challenges during placements due to inconsistent attendance, weak academic performance, or insufficient preparation. This paper presents SMARTPLACE-AI, an intelligent and centralized placement support system that enhances recruit- ment management through automation and analytical evaluation. The system integrates a mobile-based interface with a scalable backend infrastructure to ensure efficient data processing and seamless user interaction. An automated screening mechanism evaluates student eli- gibility based on predefined company requirements. A multi- parameter evaluation module analyzes academic performance, attendance behavior, assessment outcomes, and skill attributes to determine readiness levels. Continuous monitoring identifies irregular participation and generates alerts. A dynamic analytical dashboard provides insights into place- ment trends and student performance, enabling data-driven decision-making.

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Placement Analytics, Student Readiness Evaluation, Attendance Behavior Tracking, Educational Data Analysis, Predictive Systems, Smart Notifications

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Abstract-- Weed management is one of the most critical agricultural practices required to maintain crop health and maximize agricultural productivity. Weeds compete with crops for essential resources such as nutrients, sunlight, water, and space, thereby reducing crop yield and quality. Traditional weed removal methods such as manual hand weeding require extensive labor and time, while chemical herbicides may cause environmental pollution, soil degradation, and health risks to farmers and consumers. This research focuses on the design and development of a mechanical agricultural weeder that can efficiently remove weeds while minimizing labor effort and operational costs. The proposed agricultural weeder is a manually operated machine consisting of a frame, handle, wheel, shaft, and cutting blade mechanism. The machine operates by pushing it forward across the field, allowing the rotating wheel to drive the blade that cuts or uproots weeds from the soil. The design emphasizes simplicity, durability, affordability, and ease of maintenance so that it can be used effectively by small and medium-scale farmers. Field testing was conducted to evaluate the efficiency and performance of the developed machine. The results showed that the mechanical weeder significantly reduced labor effort and time required for weed removal while improving soil aeration and crop growth. The study concludes that the developed agricultural weeder provides a practical, economical, and environmentally friendly solution for weed management in agricultural fields. With further improvements and technological integration, mechanical weeders can contribute significantly to sustainable farming practices.

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Agriculture weeder, Soil pollution, chemical harbicides

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This research paper examines the current status and future prospects of school library services in the context of the rapid transformation driven by Artificial Intelligence (AI) and Information and Communication Technology (ICT) in the 21st century. The integration of AI into the education system has significantly reshaped teaching-learning processes, improved access to information, and enhanced methods of knowledge dissemination. In this changing environment, school libraries are no longer confined to their traditional role as repositories of books; instead, they are expected to evolve into dynamic digital knowledge centers that support interactive and technology-enabled learning. The study focuses on EMRS Dahi Dhar, located in Madhya Pradesh, a residential educational institution established to serve tribal students. It critically evaluates the present condition of library services within the institution. The findings reveal several challenges, including inadequate infrastructure, a lack of modern technological resources, poor internet connectivity, and limited awareness among students and staff regarding the effective use of AI and ICT tools. These issues hinder the ability of the library to meet contemporary educational demands and restrict students' access to digital learning opportunities. In addition to assessing the existing limitations, the study explores the potential role of school libraries in promoting inclusive and community-based learning. It emphasizes that school libraries, particularly in rural and tribal areas, can extend their services beyond academic purposes and function as community knowledge centers. By providing access to digital resources, information services, and learning support, libraries can contribute to the educational development of nearby rural populations. Furthermore, the paper highlights the importance of adopting innovative approaches such as digital libraries, mobile library services, and AI-enabled tools to improve accessibility and enhance learning outcomes. The integration of such technologies can bridge the digital divide and ensure equitable access to information for all learners. The study concludes by offering practical suggestions for the modernization and digital transformation of school libraries. It stresses the need for improved infrastructure, better internet connectivity, training programs for users, and policy-level support to ensure the inclusive development of school libraries in rural India. These measures are essential for making libraries relevant and effective in the digital age.

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Artificial Intelligence (AI), ICT, School Library Services, EMRS, Rural Education, Digital Library, Library Modernization, Community Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Punjabi naari naatakkaar ate naat-roop.

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Punjabi naari naatakkaar ate naat-roop.

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Accurate sugarcane maturity assessment is essential for optimizing harvest timing and improving yield. Traditional methods are labor-intensive and unsuitable for real-time use. This study presents a portable handheld system for rapid, non-destructive maturity prediction. The device uses optical sensors and embedded processing to estimate parameters such as Brix value and moisture content, with data analyzed using machine learning algorithms. It provides instant results to support field decisions. Experimental results show high accuracy compared to laboratory methods. The system is compact, cost-effective, and suitable for rural applications, helping improve productivity and reduce losses in the sugarcane industry.

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Sugarcane maturity, Handheld device, Brix prediction, Optical sensors, Machine learning, Non-destructive testing, Precision agriculture, Embedded system

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 Abstract

???????????? ?????????????????? ???????? ?????????? ???????? -"?????? ????????? ????????????? ???? ??????" ? ??? ??????? ???????, ??????, ??????????? ? ??? ????? ?????? ??????????? ???????????? ??????????-??????????? ????? ??????? ????????????? ?????? ???????-'???????????' '???????????' ??? ??????????? ??????????? ???????? ??????? ???? ???? ???-??????????? ????-?????????-????-????????? ? ???????????? ???????? ?????????? ???????????????? ????????????? ???????????? ??? ???????????????? ??????????????? ?????????????? ??????? ????????? ??????? ? ??????????????? ???? ?????????? ?????????? (?) ??????????? (?) ???????????? (?) ??????????? (?) ?????????? (?) ?????????? (?) ???????????? ????? ?????????????? ?????????? ???????????????? ??????????? ??????????????? ?????????? ??????

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????????????, ????????????, ?????????????????, ?????????????,

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Creative Commons Attribution 4.0 and The Open Definition

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The rising need for short-term work arrangements has created a requirement for an organized system to handle part-time job openings. The research study introduces TimeBank which functions as a web-based platform created to enable safe and efficient process of hourly job requests and candidate selection. The system was built using the MERN technology stack and offers various functions including user authentication, job matching, time tracking, secure payment processing, and a rating system. The testing of the developed system used functional test cases to assess both performance and reliability. The platform demonstrates its capacity to handle short-term work management needs by delivering a complete and expandable solution which enhances trust between job seekers and job posters.

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Gig Economy, Web Application, Job Matching, Time Tracking, Secure Payments, MERN Stack

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Creative Commons Attribution 4.0 and The Open Definition

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Social media plays a vital role in the advertising sector and has become increasingly important in enhancing customer perception and satisfaction, ultimately helping to build a loyal customer base, especially in the marketing of goods and services. Customer service is widely recognized as one of the most critical factors in establishing reputation and credibility among the public. This study aims to examine the service quality techniques through social media platforms and their impact on customer perception. It focuses on selected leading social media platforms within the study area. The primary objective is to investigate the factors influencing customer perception and their effect on satisfaction. Additionally, the study evaluates customer attitudes toward social media activities and identifies the challenges faced by customers when using social media advertisements to purchase products or services in the study area. It also analyzes the socio-economic profile of the selected respondents. Methodology refers to a systematic approach to solving a research problem. The study is conducted in Bangalore City, and the results and findings are based on the opinions of its customers; therefore, they cannot be generalized beyond this context.

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Creative Commons Attribution 4.0 and The Open Definition

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Social Media Advertisements, Problems faced by customers and Perception

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Creative Commons Attribution 4.0 and The Open Definition