Volume 14 | Issue 7 |

 Abstract

Artificial Intelligence (AI|) has emerged as a transformative force in education, reshaping traditional teaching and learning practices across educational institutions worldwide. The integration of AI Powered technologies such as intelligent tutoring systems, adaptative learning platforms, automated assessment tools, generative AI applications has created unprecedented opportunities for personalized learning and instructional innovations. Despite these advantages, the rapid adoption of AI has introduced numerous challenges for teachers. These challenges extended beyond technological adaptation and encompass pedagogical concerns, ethical dilemmas, and professional uncertainties. Teachers are required to redesign instructional strategies, acquired digital competencies, ensure ethical AI usages, and maintain academic integrity in increasingly technology - driven learning environments. This thematic article explores the pedagogical, technological, and ethical challenges faced by teachers in the era of AI. The article highlights the complexities of AI integration in educational settings and emphasizes the need for comprehensive teacher training, policy development and ethical frameworks to ensure responsible and effective AI implementation in education.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, teachers, pedagogical challenges, technological challenges, ethical challenges, educational technology, AI integration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Conventional municipal waste-bin systems suffer from three persistent shortcomings: refuse is dumped as a single mixed stream, full bins go unreported until they overflow, and decomposing organic waste emits foul odor for hours before any collection action is taken. This paper presents an enhanced smart-bin prototype that integrates four sensing modalities; a load cell, a soil-moisture probe, three ultrasonic rangefinders, and an MQ-135 metal-oxide gas sensor around a master-slave Arduino Uno architecture. Deposited refuse is automatically segregated into dry or wet streams; dry waste enters a gear-motor-driven rotating cleaning chamber whose perforated floor sifts soil and dust into a sub-tray, while wet waste is monitored continuously by the MQ-135. A NORMAL-priority SMS is dispatched through a SIM900A GSM modem when either bin reaches 80% capacity, and the gas reading cross-referenced with a 24-hour decomposition timer, it escalates the alert to HIGH-PRIORITY whenever ammonia-equivalent concentration exceeds 250ppm. Every alert is geo-tagged using a NEO-6 GPS receiver. Across thirty-five test scenarios, the prototype achieved 94.3% classification accuracy, 92.0% sieving efficiency on contaminated dry waste, and a 57% reduction in mean collection latency for malodorous wet waste relative to a fill-only baseline.

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 Keywords

Internet of Things, automated waste segregation, dry-wet separation, real-time bin monitoring, MQ-135 gas sensor, odor detection, soil-moisture sensor, HX711 load cell, Arduino Uno, gear motor, MOSFET Z44N, SIM900A GSM, NEO-6 GPS

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of Financial Technology (FinTech) has transformed traditional lending practices through innovative digital platforms, among which Peer-to-Peer (P2P) lending has emerged as an important alternative financing mechanism. By directly connecting lenders and borrowers through online platforms, P2P lending enhances financial inclusion, reduces transaction costs, and provides faster access to credit. Although the Reserve Bank of India (RBI) has established a regulatory framework for Non-Banking Financial Company-Peer-toPeer (NBFC-P2P) lending platforms, empirical evidence regarding their effectiveness from both lenders' and borrowers' perspectives remains limited. The present pilot study evaluates the feasibility and reliability of a research instrument developed to measure the effectiveness of selected P2P lending platforms operating in Bengaluru City. A structured questionnaire consisting of multiple constructs, including platform usability, customer satisfaction, trust, processing efficiency, customer support, cost-effectiveness, perceived risk, return on investment, and adoption behaviour, was administered to 50 respondents, comprising 25 lenders and 25 borrowers, using convenience sampling. Reliability analysis using Cronbach's Alpha demonstrated satisfactory internal consistency for all constructs, indicating that the questionnaire is suitable for large-scale empirical investigation. Preliminary descriptive analysis suggests favourable perceptions regarding platform usability and customer satisfaction, while perceived risk remains a concern among certain respondent groups. The findings confirm the feasibility of the research design and provide a validated instrument for conducting comprehensive research on P2P lending effectiveness. The study contributes to FinTech literature by offering an empirically validated framework for future investigations and provides practical implications for platform operators, regulators, and policymakers seeking to enhance digital lending services in India.

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 Keywords

Peer-to-Peer Lending, FinTech, Pilot Study, Digital Lending, Customer Satisfaction, Reliability Analysis, Bengaluru.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Olanzapine, Schizophrenia, Samidorphan, Stability indicating, HPLC.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Digital Stress; Psychological Immunity; Coping Competencies; Mental Health Promotion; Adults

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Success in campus recruitment usually calls for a good mix of aptitude, technical, and the ability to communicate well. Most students, however, struggle to judge how prepared they actually are and to pinpoint exactly which areas need more work before they sit for placement drives. To address this gap, the present work proposes an AI-based placement evaluation system that brings aptitude testing, technical review, and HR interview assessment together inside one web platform, all while offering each student guidance tailored to their own performance. Multiple-choice and objective-type questions are graded automatically, whereas responses given during the technical and HR rounds are examined through rule-based Natural Language Processing (NLP) methods. Scores gathered across all three rounds are then fed into a Decision Tree classifier, which labels a student as either Placement Ready or Needs Improvement. Building on this outcome, the system flags the student's weaker areas and puts together a tailored improvement plan. To judge how well the model performs, Accuracy, Precision, Recall, F1-score, and ROC-AUC were used as evaluation metrics, and the results point to consistent, dependable classification. Built on Python, Django, HTML, CSS, JavaScript, and SQLite, the resulting application lets students keep track of their progress and sharpen their placement preparation over time.

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Placement Intelligence, PrepEdge AI, Decision Tree, Machine Learning, Rule-Based Natural Language Processing (NLP), Placement Readiness Prediction, Skill Gap Analysis, Personalized Learning Roadmap, Django.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of social media platforms has significantly increased the spread of misinformation and fake news, posing serious threats to society, politics, healthcare, and financial systems. Traditional fact-checking approaches are insufficient to handle the large-scale dissemination of deceptive information. Consequently, researchers have adopted Machine Learning (ML) and Natural Language Processing (NLP) techniques to automatically detect fake news. This review paper provides a comprehensive analysis of existing approaches in fake news detection, focusing on classical machine learning models and advanced transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT). We systematically discuss the literature, datasets, dataset creation and collection, feature engineering methods, model architectures, evaluation metrics, accuracy evaluation, challenges, and future research directions. A dedicated comparative study of three widely used classical classifiers -- Support Vector Machine (SVM), Logistic Regression, and Random Forest -- is also presented to identify the best-performing classical model. The review consolidates findings from over 20 peer-reviewed publications (2017-2025) and highlights the superiority of contextualized language models like BERT in capturing semantic nuances, while addressing computational complexity, explainability, and ethical challenges in automated misinformation detection systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake News Detection, Machine Learning, Natural Language Processing, BERT, Deep Learning, Transformers, Misinformation Detection, Text Classification, TF-IDF, SVM, Logistic Regression, Random Forest, RoBERTa, ALBERT, Explainable AI, Social Media.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Sorbitol, Ultrasonic velocity, Adiabatic compressibility, Hydroalcoholic solutions, Viscosity, Density, Solute-solvent interactions, Temperature dependence, Sugar alcohols, Acoustic diagnostics.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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full adder, low-power VLSI, static CMOS, 90-nm technology, transistor sizing, Cadence Virtuoso, propagation delay, power-delay product.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Low-Power CMOS Comparator, Analog-to-Digital Converter (ADC), Dynamic Comparator, Regenerative Latch, Cadence Virtuoso.

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Creative Commons Attribution 4.0 and The Open Definition