Volume 14 | Issue 4 |

 Abstract

The Student Complaint Portal is a web-based application designed to create an efficient, transparent, and user-friendly system for handling grievances in educational institutions. The main goal of this system is to give students a secure digital space to submit complaints or feedback about academic, administrative, infrastructure, or faculty-related issues. Each complaint is recorded, categorized, and assigned to the right department to ensure timely and fair resolution. The portal includes secure authentication, rolebased access, and an easy-to-use dashboard for both students and administrators. It allows realtime tracking of complaints and sends automated notifications through email alerts, which enhances transparency and accountability throughout the grievance process. The system also keeps a central database of resolved and pending complaints for analysis and policy improvement. By using modern web technologies and strong database management systems, the Student Complaint Portal removes the problems of traditional paper-based grievance handling. It improves how schools respond, encourages open communication, and builds a culture of trust and fairness on campus. Overall, the system provides a scalable, data-driven, and clear way to manage student grievances effectively in the digital age

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 Keywords

Online Complaint Management, Paperless Complaint System, Student Complaint Portal, Web Technology Integration, Online Grievance Handling, Complaint Resolution.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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A Study to Assess the Effectiveness of a Structured Teaching Program on Enhancing the Interpretation of Arrhythmias in Electrocardiograms Among Healthcare Workers at Lions Hospital, Mehsana.

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 Abstract

Over the past decade, the number of learning management systems available to institutions has grown considerably, but most comparisons between them never get past feature lists or vendor- backed reviews. Very few studies actually look at how real students behave on these platforms under controlled conditions, and that gap is what pushed us to run this study. We took four platforms-- Moodle, Google Classroom, Coursera, and Edu-Platform, a MERN-stack system we built and deployed ourselves--and put them side by side across six dimensions that we think matter most when an institution genuinely needs to control how students move through a course: whether learning paths can be locked in sequence, how clearly students can see their own progress, what approval mechanisms exist for content, how long setup actually takes for a busy instructor, how engaged students end up becoming, and how much technical effort the whole thing demands to configure. The numbers we use come from a four-week live deployment at D.Y. Patil Technical Campus, Talsande, where 48 undergraduate students across two Computer Science and Engineering courses used the platform for real coursework--not a controlled lab exercise, but actual teaching happening in real time. Engagement metrics include course completion rate, average days to completion, module access frequency, and a System Usability Scale (SUS) score. The study finds that structured sequential delivery with real-time progress feedback produces a completion rate of 87.3%, compared to 50.5% in a group using unstructured cloud-folder delivery--a statistically meaningful gap that aligns with prior MOOC attrition research. Qualitative dimensions of the comparison reveal that existing platforms trade governance and engagement depth for scale and ease of onboarding, a trade-off that may be inappropriate for institution-controlled curricula.

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 Keywords

MERN stack, learning management system, personalized learning paths, role-based access control, real-time progress tracking, online education platform, e-learning, React.js, MongoDB, blended learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This report explains how the structure of cellulose has been studied over time. Early models based only on diffractometry could not explain all results, so newer methods like Raman spectroscopy and carbon-13 NMR were used to develop more accurate models. These improved models help scientists better understand how cellulose structure changes with its source and processing, which affects its material properties. At the same time, there is a growing need for sustainable materials in industries like construction to protect the environment and meet sustainability goals. While synthetic fibres such as CFRP and GFRP are widely used, their environmental impact has led to increased interest in natural fibres. These natural fibres can improve the strength and flexibility of building materials while being more eco-friendly.

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cellulose Fibre Reinforced Concrete

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 Abstract

Agriculture remains the backbone of India's economy, yet farmers continue to face challenges such as limited access to expert guidance, language barriers, excessive use of chemical inputs, and unreliable internet connectivity. AgroLingua: AI-Powered Multilingual Farming Assistant addresses these issues by integrating artificial intelligence, IoT sensors, computer vision, and natural language processing. The system provides real-time insights on soil health, crop suitability, pest and disease detection, weather conditions, yield expectations, and market trends. With its voice-enabled interface supporting Telugu, Hindi, and English, AgroLingua ensures inclusivity and accessibility, even for farmers with low literacy levels. Its offline-first architecture further supports rural areas with inconsistent connectivity, while the platform's organic advisory module promotes sustainable and eco-friendly farming practices. The methodology behind AgroLingua follows a structured and modular approach that combines data collection, machine learning, and intelligent advisory generation. IoT sensors capture soil moisture, temperature, pH, and nutrient levels, while computer vision analyzes crop images to detect pests and diseases. The collected data undergoes preprocessing and feature extraction before being processed by rule-based and machine-learning-ready prediction modules. The system then generates personalized recommendations based on soil conditions, predicted crops, and regional context. A multilingual NLP layer enables voice-based queries and responses, and offline caching ensures uninterrupted use. This end-to-end pipeline ensures accurate predictions, localized insights, and scalable deployment, making AgroLingua a reliable and farmer-friendly decision support system.

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 Keywords

Artificial Intelligence (AI), Natural Language Processing (NLP), Multilingual Systems, Precision Agriculture, Voice-Based Interaction, Intelligent Advisory Systems, Real-Time Query Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The technology sector is characterized by high-velocity Mergers and Acquisitions (M&A), where the realization of synergistic value is highly contingent upon the seamless Post-Merger Integration (PMI) of financial systems. Traditional PMI literature heavily favors cultural and strategic alignment, often marginalizing the deep technical and mathematical complexities of financial data synchronization. This paper proposes a rigorous quantitative framework for integrating disparate accounting architectures. By utilizing vector space modeling for Chart of Accounts (CoA) harmonization, defining a composite Data Quality Index (DQI), and applying machine learning algorithms for entity resolution, this research provides a deterministic approach to preserving financial data integrity. We model the algorithmic complexities of large-scale Extract, Transform, Load (ETL) processes unique to high-transaction-volume technology firms, ultimately offering a scalable blueprint to mitigate regulatory (SOX) and operational risks during system convergence.

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Data Quality, MDG

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This review presents a comprehensive analysis of the formulation and evaluation of a cold cream containing Tagetes erecta extract

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Creative Commons Attribution 4.0 and The Open Definition

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Marigold cold cream, formulation, skin healing properties

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's digital era, excessive smartphone usage has become a major factor influencing individual productivity, focus, and overall well-being. While existing applications provide basic screen-time statistics, they fail to deliver meaningful insights or predictive analysis regarding user productivity. This paper presents an AI-powered Smartphone Usage Analytics and Productivity Prediction System that leverages real-world usage data combined with behavioral factors such as sleep patterns, stress levels, and focus time. The system utilizes a deep learning model to classify productivity levels into Low, Medium, and High categories and also generates a quantitative productivity score. By integrating machine learning with data visualization and database systems, the proposed solution provides personalized insights and actionable recommendations to users. The system is implemented as a web-based application using Streamlit, ensuring accessibility and ease of use. Experimental analysis demonstrates that combining behavioral attributes with usage patterns significantly improves prediction accuracy, enabling users to make informed decisions that enhance productivity.

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 Keywords

Smartphone Usage Analysis, Productivity Prediction, Deep Learning, Behavioral Analytics, Machine Learning, Streamlit, Data Visualization, User Behavior Modeling, AI-based Recommendation System

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: The contemporary student job search is deeply hindered by opaque Applicant Tracking Systems (ATS) that frequently reject candidates without providing actionable feedback. To resolve this structural inef- ficiency, we developed an AI-powered career navigation platform that transcends standard job boards. The proposed system features an automated resume analyzer utilizing Natural Language Processing (NLP), com- pletely eliminating manual data entry and reducing candidate onboarding time by 82%. We employ a hybrid recommendation engine that calculates transparent, explainable match scores, ensuring users understand the specific semantic variables driving their suggestions. Furthermore, the platform integrates an interactive skill gap dashboard paired dynamically with targeted educational course recommendations. During an 8-week beta deployment processing over 15,000 resumes, the system achieved an 87.3% matching precision--a 107% improvement over legacy ATS keyword scanners. Crucially, 76% of candidates actively engaged with the recommended skill-gap courses, leading to a 41% increase in subsequent interview shortlisting rates. By aggregating real-time market trends and actively mitigating algorithmic bias, our system drastically reduces job-search fatigue and provides highly actionable pathways for continuous professional development

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Resume Parsing, Explainable AI, Recommendation System, Skill Gap Anal- ysis, Career Guidance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The AI-Powered Stock Market Insight Application is an intelligent Android-based solution developed to help users understand stock market behavior, technical indicators, and short-term price movements more effectively. Although digital trading platforms have made stock market access easier, many retail investors still struggle to analyze live market data, interpret candlestick charts, and make informed trading decisions due to volatility, data complexity, and limited expert support. To address this issue, the proposed system combines real-time stock data processing, machine learning-based short-term prediction, interactive chart visualization, and AI-assisted guidance within a single mobile application. The application is developed using Java/XML for the frontend and Firebase Realtime Database for secure real-time synchronization and storage. It offers features such as live stock updates, trend analysis, candlestick chart interpretation, forecasting support, and an NLP-based chatbot that answers user queries and explains trading concepts in a simple and user-friendly manner. By integrating financial analytics with mobile accessibility, the system reduces the gap between complex market data and practical user understanding. The application aims to support both beginner and intermediate investors in making smarter, data-driven, and more confident investment decisions

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-Powered Stock Market Insight, Stock Market Prediction, Machine Learning, Android Application, Firebase Real-time Database, Candlestick Chart Analysis, NLP Chatbot, Real-Time Data Analytics, Financial Forecasting, Technical Indicators

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Creative Commons Attribution 4.0 and The Open Definition