Volume 14 | Issue 4 |

 Abstract

A web-based reporting and analytics system is an essential tool for organizations that manage large volumes of operational and sales data. Traditional reporting processes often rely on manual data collection and spreadsheet-based analysis, which can be time-consuming, error-prone, and inefficient for large-scale operations. This paper presents the design and implementation of a web-based reporting and analytics application developed for a leading publication house operating across multiple states in India. The objective of the system is to provide management with accurate insights into sales performance through key performance indicators (KPIs), statistical analysis, and detailed reports. The system is implemented using modern web technologies including ReactJS for the user interface and ASP.NET Core Web API for backend services. Data is stored and managed using a MySQL relational database. The application provides interactive dashboards, graphical visualization of sales statistics, and export functionality for generating reports in Excel and PDF formats. The architecture follows a three-tier model consisting of presentation, application, and data layers to ensure modularity and maintainability. The implementation demonstrates how web-based reporting systems can simplify data analysis, improve accessibility to sales data, and support strategic decision-making in organizations with large operational footprints.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web Application, Reporting System, Data Analytics, Dashboard, Sales Analysis, Web API, Business Intelligence

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study examines the growth of small businesses in the era of globalization and how expanding global markets have created new opportunities and challenges for them. Globalization has enabled small enterprises to access international customers, advanced technologies, and diverse resources, thereby improving their competitiveness and innovation capabilities. The rise of digital platforms and e-commerce has further accelerated business expansion beyond local boundaries. However, small businesses also face intense competition from large multinational companies, fluctuations in global demand, and the need to comply with international standards. Access to finance, skilled labor, and technological adoption remain critical factors influencing their growth. Government policies, support programs, and infrastructure development play a significant role in strengthening small enterprises. The study highlights that globalization encourages entrepreneurship, enhances productivity, and promotes economic development. At the same time, it emphasizes the importance of strategic planning, quality improvement, and market adaptation for sustainable growth. Overall, small businesses can thrive in the globalized economy by leveraging innovation, maintaining competitiveness, and responding effectively to changing market trends.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Globalization, Small Businesses, Economic Growth, Entrepreneurship, E-commerce, International Market, Technology Adoption

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Data quality is essential for reliable data analysis and machine learning. Real-world datasets often contain missing values, outliers, duplicates and inconsistencies that impact results. This paper presents CleanSight: an interactive web-based tool developed using Python and Streamlit for automated data profiling and cleaning. The system supports CSV and Excel ingestion with automatic delimiter detection, statistical profiling, IQR-based outlier detection and flexible missing value imputation techniques including mean, median, mode, custom values and row removal. It also provides automated data preprocessing, interactive visualizations using Plotly and export options in CSV, Excel and PDF formats. The tool is designed for analysts, researcher, and students to perform efficient data preprocessing without programming knowledge. Experimental evaluation on multiple datasets demonstrates improved data quality and scalability. The deployed system is accessible online for real-time usage.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data Cleaning, Data Profiling, Streamlit, Outlier Detection, IQR, Data Quality, Data Preprocessing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ProConnect presents a novel Multi-Agent System (MAS) designed to automate and intelligently govern the end-to-end recruitment lifecycle, eliminating the inefficiencies, subjectivity, and ethical risks embedded in traditional hiring processes. The system deploys seven specialized, collaborating AI agents to manage talent acquisition comprehensively. The Resume Processing Agent leverages a Large Language Model (LLM) paired with an Explainable AI (XAI) layer to convert unstructured resumes (PDF/DOCX) into structured, queryable candidate profiles. The Candidate Selection Agent performs intelligent hybrid shortlisting by combining semantic vector search with a Skill Knowledge Graph, capturing meaningful relationships between technical competencies. A Bias Audit Agent independently monitors every hiring decision through blind screening and counterfactual testing, ensuring algorithmic fairness. A multi-specialist Debate Panel conducts structured deliberation across Technical, Cultural, and Risk evaluation perspectives before producing a consensus score. The Predictive Success Model forecasts candidate retention probability and time-to-productivity using a trained Random Forest classifier. The Career Coach Agent generates personalized upskilling roadmaps for rejected candidates. Together, these agents form a transparent, scalable, and ethically auditable intelligent recruitment platform.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Proconnect, Multi-Agent System, Langchain, Semantic Skill Matching, AI Agents, Professional Profiles, Talent Acquisition, Resume Parsing, Automated Interviewing, Career Development, Personalization.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Automotive wheel hubs are critical load-bearing components that ensure structural integrity and dynamic performance of vehicle systems. Finite element methods (FEM) have become essential tools for analyzing static strength, modal behavior, and fatigue life under complex loading conditions. This review presents a systematic synthesis of recent FEM-based studies on wheel hubs, focusing on modeling strategies, material selection, boundary Engineering conditions, and realistic loading scenarios. Emphasis is placed on lightweight design and topology optimization for performance enhancement without compromising safety. Additionally, the role of multiaxial fatigue analysis and experimental validation is highlighted. Key research gaps, including the need for integrated multi-physics modeling and real-world load spectrum representation, are identified to guide future developments in advanced wheel hub design.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Finite Element Analysis; Wheel Hub; Fatigue Analysis; Modal Analysis; Lightweight Design; Topology Optimization; Automotive Engineering

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

We all know that mowing the lawn is one of those never-ending chores. Push the mower under the blazing sun, deal with the noise, the fumes, and the constant back-and-forth -- it's tiring, time-consuming, and not exactly eco-friendly. That's exactly why we created the Automated Solar Grass Cutter -- a smart, fully autonomous robot that takes care of the grass while running completely in sunlight. The core idea is wonderfully straightforward: harness the sun's energy to do all the work. A solar panel mounted neatly on top of the machine continuously absorbs sunlight and converts it into electricity. This power is stored in a high-capacity rechargeable battery, giving the mower enough energy to run smoothly throughout the day. No plugging into the wall, no petrol cans, and zero harmful emissions -- just clean, renewable energy doing its job.At the heart of this intelligent machine is the powerful ESP32 microcontroller. Think of it as the robot's brain. It constantly makes decisions -- where to go, how to avoid obstacles, when to turn, and how fast to move. Working together with the L298N motor driver, it controls the DC motors with precision, allowing the mower to glide smoothly across uneven lawns, turn corners gracefully, and cover the entire area without missing spots or getting stuck. Below the chassis sits a sharp, high-speed rotating blade that delivers a clean, consistent cut every time. The height of the cut can be adjusted depending on the type of grass and your preference. The entire system is designed to operate autonomously, adapting naturally to different terrains -- whether it's a flat backyard lawn, a slightly sloped garden, or a small agricultural patch. One of the best parts? You don't have to babysit it. Just place the mower in the lawn, switch it on, and let it do its magic. It can work for hours on its own while you relax indoors, play with your kids, or focus on other important tasks. And when the job is done or the battery runs low, it can be easily brought back for the next day's charge. Beyond convenience, this project means a lot more. In a world facing rising fuel costs and climate challenges, it offers a practical, affordable alternative to traditional lawn mowers. It drastically cuts down on carbon emissions, reduces noise pollution, and lowers long-term maintenance costs. For homeowners, gardeners, and even small farmers, it's a game-changer that turns a boring, exhausting job into something effortless and sustainable.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ESP32, Solar Panel, L298N Motor Driver, Automated Grass Cutter, Renewable Energy, Embedded Systems, Sustainable Automation, Autonomous Robot, Eco-Friendly Technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing scale of digital recruitment has made manual resume screening inefficient, time consuming, and prone to bias. This paper presents an AI-Based Resume Scanner that employs Natural Language Processing (NLP) and Machine Learning (ML) for automated candidate shortlisting. The system extracts, parses, and analyzes resume content using TFIDF, cosine similarity, and transformer-based models like BERT to compute job-candidate matching scores. A Python-based backend handles text preprocessing and classification, while a React- Node.js interface provides seamless real-time interaction. By integrating contextual understanding and explainable AI (XAI) features, the system reduces human intervention while maintaining fairness and transparency. Experimental validation and literature evidence indicate up to 70% improvement in screening efficiency and notable bias reduction. The proposed approach demonstrates how AI-driven automation can enhance recruitment quality, accelerate decision-making, and support equitable, data-driven talent acquisition.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Resume Screening, Artificial Intelligence, Natural Language Processing, Deep Learning, Recruitment Automation, Emotion Recognition.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study aims to examine the effectiveness of teaching through computers in teacher education colleges. Relevant Indian and international studies were reviewed to provide a theoretical foundation. The study employed the descriptive survey method. A sample of 100 student teachers from teacher education colleges was selected using a stratified random sampling technique. A questionnaire was developed and administered to gather student teachers' opinions on the effectiveness of computer-based teaching. The collected data were analyzed using statistical techniques such as mean, standard deviation, t-test, and F-test. Scores of different groups were compared based on variables including gender, qualification, locality, and methodology. The findings were interpreted in the context of earlier research, and appropriate suggestions and recommendations for future studies were also provided.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Computer-assisted teaching, ICT in education, Digital learning,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Chandigarh, a Union Territory designated as a model city has consistently demonstrated competence in urban sanitation and solid waste management (SWM). This research assesses its SWM performance using Swachh Survekshan (SS) ranks and ratings from 2016 to 2024-25. Using secondary data from official MoHUA reports, city report cards and the District Environment Plan 2026, the research identifies strengths in waste processing (100% score), dumpsite restoration, door-to-door pickup (93%) as well as recurring shortcomings in source segregation (14%).The report builds on historical trends, evaluates literature on SS and SWM in India. The Chandigarh approach provides essential insights for long-term urban waste management under the Swachh Bharat Mission 2.0.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Solid waste management, Swachh Survekshan rankings, Chandigarh Municipal Corporation, source segregation at source, door-to-door waste collection, waste processing capacity

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

--Long waiting times and overcrowded outpatient departments are problems that almost every hospital deals with. The traditional methods--paper tokens, fixed appointment slots--simply do not work well when patient volumes keep changing throughout the day. In this paper, we present CareBridge AI, a queue optimization system that uses machine learning to make hospital queues smarter. The system has three ML models working together: one that predicts whether a patient will actually show up for their appointment (binary classification using XGBoost), one that estimates how long an ER patient will have to wait (regression using Gradient Boosting), and one that classifies patients into triage priority levels (multiclass Random Forest). All three models are trained on publicly available healthcare datasets and go through a standardized preprocessing pipeline. Their outputs are combined into a single Queue Priority Score that reorders the queue in real time. In our experiments, the no-show model achieved an AUC-ROC of 0.81, the wait-time model got an RMSE of 12.4 minutes, and the triage classifier reached a weighted F1 score of 0.88. When compared against a basic first-come-first-served system, CareBridge AI showed a projected 35-42% reduction in average waiting time. These results show that combining multiple focused ML models through a unified scoring formula is a practical way to improve how hospitals manage patient flow.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hospital queue optimization, machine learning, patient no-show prediction, triage classification, wait-time estimation, XGBoost, healthcare informatics

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Creative Commons Attribution 4.0 and The Open Definition