Volume 14 | Issue 7 |

 Abstract

The brain is responsible for regulating vital functions, including memory, vision, cognition, and decision-making. Brain tumors, which result from the uncontrolled growth of abnormal cells within the brain, are among the major causes of mortality worldwide and require early detection to improve treatment outcomes. Conventional diagnosis through manual examination of MRI scans is labor-intensive, time-consuming, and susceptible to errors due to the large volume of imaging data and the diverse characteristics of tumors. Consequently, automated brain tumor detection has become an important area of research for enhancing the accuracy and efficiency of medical diagnosis. This study presents a machine learning-based framework for brain tumor detection using MRI image processing techniques. The proposed approach combines effective feature extraction with classification algorithms to accurately identify abnormal tumor regions, thereby reducing the workload of radiologists and improving diagnostic consistency. Experimental findings indicate that the proposed method achieves superior detection performance compared with conventional classification techniques, demonstrating its potential as a reliable clinical decision support tool.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Brain Tumor, Machine Learning Techniques, Image Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nutrition plays a vital role in the growth and development of under-five children. Adequate nutrition during early childhood helps in maintaining good health, preventing illnesses, and supporting normal physical and mental development. Nutritional deficiency disorders such as anemia, protein-energy malnutrition, vitamin and mineral deficiencies are commonly seen among under-five children, especially in rural areas. Mothers are the primary caregivers and their knowledge regarding nutrition is essential in preventing these disorders. Providing proper health education can help mothers understand the importance of balanced diet and early prevention of nutritional deficiencies.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nutritional Deficiency Disorders, Mothers of Under-Five Children, Effectiveness, Under-Five Children, Anganwadi Centres

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this communication, the education system of women during Mughal reign is presented. Throughout the Mughal era, women's status was tiered. In the Mughal era, women's education was regarded as less significant than that of males by both Muslims and Hindus. Studying Literature and Education was only available to women from royal class and noble class. Getting education for middle class and lower class women was very stringent due to Purdah System, Early marriage, social taboos and many more reasons. Many Mughal Princess such as Gulbadan Begum, Mumtaz Mahal, Noor Jahan, Jahan Ara, Roshan Ara, Sati al-Nisa Khanam and many more were taught literature and language. Many of them were well versed in poetry. During the Mughal era, women also made significant contributions to education through the establishment of institutions, in addition to their literary works. Some princess and women belonged to noble families had constructed mosque and attached Madrasas to it for imparting education.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Women, Education, Mughal Empire, Social, Culture, Political, Religion, Islam

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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

 Keywords

Keywords-- Papercrete, Plastic Waste, Pavement Block, Sustainable Construction, Recycling, Green Building Materials

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Olanzapine, Schizophrenia, Samidorphan, Stability indicating, HPLC.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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