Volume 14 | Issue 4 |

 Abstract

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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 Keywords

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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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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 Keywords

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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 Keywords

Social Media Advertisements, Problems faced by customers and Perception

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 Abstract

From the Kothari Commission to NEP 2020: Continuity and Change

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From the Kothari Commission to NEP 2020: Continuity and Change

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Creative Commons Attribution 4.0 and The Open Definition

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A case report details the physiotherapy rehabilitation procedure of a 48 yrs old man having multiple traumatic fractures from a fall injury, fracture is seen involving L3 and L4 vertebral bodies with about 10-20% reduction in vertebral body height. Fracture seen involving the posterior vertebral elements at these levels. The note is made of a fracture involving the right proximal femur. linear undisplaced fracture lines, involving right sided inferior pubic ramus. Fracture is seen involving D7 to D11 right sided transverse process. Fracture is seen involving L2 to L4 right sided transverse process. Left sided 8th rib fracture is seen in its posterior aspect. Fracture involving proximal humerus is seen. After open reduction and internal fixation surgery, an 8-12-week physical treatment regimen was put into place. Exercises for passive and active range of motion, isometric and progressive resistance training, and gait training were provided. The rehabilitation goals were pain relief, increased range of motion, muscle strength, flexibility, endurance, and functional independence. Pain levels, range of motion, muscle strength, and general function all significantly improved between pre- and post-rehabilitation evaluations. Early mobilization and structured physical therapy were crucial in achieving these outcomes, highlighting the importance of tailored rehabilitation protocols for post operative recovery.

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 Keywords

Polytrauma,physiotherapy rehabilitation,lisfranc fracture,pelvic fracture, functional independence measure, barthel index.

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This paper presents Dr.AI, a comprehensive AI-driven multimodal healthcare chatbot platform designed to democratize medical guidance through advanced natural language processing, Retrieval-Augmented Generation (RAG), and real-time emergency response capabilities. Dr.AI integrates Meta's llama-4-scout-17b-16e-instruct language model accessed via the Groq LPU inference engine, a curated ChromaDB vector store of 3,760 medical knowledge chunks, OpenAI Whisper for speech-to-text transcription, and Google Text-to-Speech (gTTS) for audio output. The system supports over 16 languages including Hindi, Hinglish, Marathi, Tamil, and Telugu, enabling voice-based and text-based medical consultation across diverse linguistic populations. Key features include a 5-step AI symptom checker, emergency SOS with real-time GPS hospital finder, medicine reminder scheduling, family health profile management, AI-powered medical report analysis for X-rays and lab results, webcam image capture, and a downloadable PDF health report. The platform is deployed on Render (backend) and Vercel (frontend) with Firebase Firestore for persistent data storage. Experimental results demonstrate that RAG-enhanced responses achieve approximately 85-90% factual grounding accuracy compared to 65-70% without RAG on domain-specific medical queries. The paper describes the system architecture, RAG pipeline design, deployment infrastructure, multilingual capabilities, and emergency detection mechanisms, establishing Dr.AI as a scalable, accessible, and clinically aware AI health companion.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

speech recognition, Whisper AI, Llama 4 Scout, medical image analysis, text-to-speech, gTTS, disease detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence has significantly transformed modern software engineering practices, particularly in the domain of automated software testing. Large Language Models (LLMs) are increasingly used to generate automated test cases by understanding source code, system requirements, and documentation. Although these models can generate test cases quickly and improve testing productivity, they often suffer from issues such as hallucination and incorrect assertions. Hallucination refers to the generation of logically incorrect or non-existent information that appears syntactically valid but does not accurately represent the expected behavior of the software system. In the context of test case generation, hallucinated outputs may include incorrect expected results, fabricated APIs, irrelevant assertions, or invalid boundary conditions. These problems reduce the reliability of AI-generated test cases and require manual verification by developers.

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 Keywords

Artificial Intelligence, Automated Test Case Generation, Large Language Models (LLMs), Software Testing, Test Automation, Hallucination in AI, Assertion Validation, Mutation Testing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, the healthcare sector has generated large volumes of patient data that can be effectively utilized to predict diseases at an early stage. This project focuses on developing a Disease Risk Prediction System using Machine Learning techniques to analyze patient health data such as age, lifestyle habits, medical history, and clinical parameters. The proposed system predicts the risk level of diseases such as diabetes, heart disease, or hypertension and provides early warnings to patients. This system helps in preventive healthcare, reduces late diagnosis, and supports doctors in clinical decision-making. The model improves healthcare efficiency by enabling timely medical intervention and personalized care.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Disease Risk Prediction, Healthcare Analytics, Early Diagnosis, Predictive Modeling, Diabetes Prediction, Heart Disease Prediction, Hypertension Detection, Patient Data Analysis, Clinical Decision Support System, Preventive Healthcare, Data Mining, Artificial Intelligence in Healthcare, Health Monitoring, Risk Assessment

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The aim of the research was to determine how gamified learning platforms impact the academic performance of college students (i.e. performance) and their participation in a college environment compared to traditional online environments. An approach of mixed methods was taken in which data was collected within a time span of 12 weeks on 50 undergraduate students pursuing Management and Computer Science courses. The group of students who took part in this study was divided into the Experimental group or Control Group. In the case of the Experimental Group, the gamified platforms employed at this time were Kahoot! Quizizz, Duolingo, and Classcraft. In the Control Group, non-gamified platforms that were used at this time were Google Classroom and Moodle. The quantitative findings have shown that Experimental Group scored significantly higher on an average post-test result as compared to Control Group (Experimental: 18.4 pts; Control: 10.3 pts). The Experimental Group had much greater indices ofstudent engagement in the areas ofstudent attendance, activity completion and activity participation as compared to Control Group. The Experimental Group did not lose their drive throughout the 12 weeks and they only experienced a slight decline in engagement. In the Control Group, the participation reduced significantly during the study. This study could be useful in finding the usefulness of gamified methodology within the framework of traditional, full-time degree program courses in an education environment that is quickly transforming due to digitization.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

e-learning, gamified, non-gamified, gamification, education, kahoot!, Quizizz, Duolingo, Classcraft, platforms, teachers, technologies.

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Creative Commons Attribution 4.0 and The Open Definition