Volume 14 | Issue 7

 Abstract

The recruitment and placement process in educational organizations most of the times are separated, slow, and they don't have standard methods for evaluating candidates. Students, colleges, and employers each work from their own separate platforms, and this causes a lack of coordination, less opportunities for interview practice, and different methods of assessing candidates. In order to solve these problems, this article suggests an AI-based integrated platform that brings all the parties together into one single system characterization. Artificial intelligence is used by the system so that it can simulate the interviews in real-time and also carry out automatic evaluation of the candidate based on their technical knowledge, communication skills, and confidence.It is developed with an expandable MERN-P architecture, which includes React. js for the frontend, FastAPI for the backend asynchronous processing, MongoDB for the data storage with high flexibility, and Supabase for the authentication and management of structured data. The platform has separate portals for the different categories of users like students, companies, and even administrators which allow them to interact with each other without any hindrance, manage the data in a centralized manner and keep track of the performance.Automated feedback and analytics are two of the great tools that can be used to boost student preparedness as well as the decision-making process. The solution put forward by this paper is one that will indeed bring in the factor of rise in productivity, giving room for growth and ensuring that the evaluation is carried out from the standpoint of fairness, thereby making this system the right fit for the contemporary recruitment.

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Artificial Intelligence, Recruitment System, Mock Interview FastAPI MERN Stack NLP,Real-Time,Systems,StudentPortal,Placement System.

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

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

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

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

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Olanzapine, Schizophrenia, Samidorphan, Stability indicating, HPLC.

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

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

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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

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

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This study examines the Dadgari weekly market in Hatisar village, located in Assam's Chirang district, as a site of informal cross-border governance along the Indo-Bhutan border. Drawing on fieldwork, vendor and consumer interviews, and market-committee records, the study describes the market's spatial organization, its community of buyers and sellers, and the largely self-regulating system that sustains it. It analyzes the market's economic significance for local Bodo, Rajbongshi, Nepali, and Adivasi communities, as well as for Bhutanese consumers from the southern foothills who make up most of its customers. The study highlights the market's near-total dependence on this Bhutanese customer base and it's distinctive dual-currency operation where both Indian rupees and Bhutanese ngultrum are accepted in daily transactions.This article argues that Dadgari functions as a form of unofficial, community-managed cross-border governance operating largely outside formal state structures, yet remains vulnerable to disruptions originating in national-level political and economic events on either side of the border. The article concludes with recommendations for infrastructural and institutional support for the same.

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Dadgari, Border market, Indo-Bhutan trade, informal economy, dual-currency

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Static Random Access Memory (SRAM) plays a vital role in modern digital systems due to its fast data access and compatibility with high-performance processors. However, continuous CMOS technology scaling has increased leakage current and standby power consumption, limiting its suitability for low-power applications. This work proposes a SAPON-based 4T2M SRAM architecture that integrates four CMOS transistors with two memristors to improve energy efficiency while maintaining reliable memory functionality. The memristors provide non-volatile data storage, and the SAPON leakage reduction technique minimizes standby leakage current without affecting normal read and write operations. The proposed design is implemented and evaluated using the Cadence Virtuoso design environment with Spectre simulation. Simulation results demonstrate that the proposed SRAM cell reduces power consumption from 404 nW to 379 nW compared with a conventional 6T SRAM cell. Although the propagation delay increases from 10.06 ns to 15.13 ns, the architecture maintains correct functionality and offers improved energy efficiency, making it suitable for IoT devices, embedded systems, and other low-power VLSI applications

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Keywords: SRAM, 4T2M SRAM, Memristor, SAPON Technique, Leakage Power Reduction, Low-Power VLSI, Cadence Virtuoso, Spectre Simulation, IoT Applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

राव शेखा व उनके वंशजों की भूमि शेखावाटी, ऐतिहासिक और सांस्कृतिक दृष्टि से न केवल राजस्थान प्रदेश में ही अपितु भारत और विश्व में भी अपना विशिष्ट महत्व रखती है। यह क्षेत्र शौर्य और बलिदानों की गौरव गाथा सुनाते गढ़-महल, त्याग और बलिदान की प्रतीक छतरियाँ, जल स्रोतों के प्रतीक कुएं, बावड़ी, तालाब, सेठ साहूकारों के वैभव को प्रदर्शित करती भव्य एवं विशाल हवेलियों और भित्ति चित्रकला के साथ-साथ अपनी गहरी धार्मिक जड़ों के लिए भी जाना जाता है। भौतिक पीड़ा से मानव जब उद्विग्न होता है तो वह अपने मानसिक शांति के लिए इन्हीं देवताओं की शरण में जाकर आत्म निवेदन करता है, मंदिर एवं देवालय उसकी श्रद्धा के प्रतीक हैं। प्रस्तुत शोध पत्र राजस्थान के सीकर जिले की आध्यात्मिक और सांस्कृतिक विरासत पर केंद्रित है। इस अध्ययन में शाकंभरी, खाटूश्यामजी, जीण माता और हर्षनाथ जैसे प्रमुख मंदिरों के ऐतिहासिक महत्व, स्थापत्य कला और पर्यटन की संभावनाओं का विश्लेषण किया गया है। शोध का निष्कर्ष यह दर्शाता है कि धार्मिक सांस्कृतिक स्थलों का पर्यटन न केवल स्थानीय अर्थव्यवस्था को बल देता है, बल्कि सांस्कृतिक संरक्षण में भी महत्वपूर्ण भूमिका निभाता है।

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शेखावाटी, सीकर, आध्यात्मिक विरासत, पर्यटन, स्थापत्य कला।

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Investors and traders consistently explore innovative methods to achieve success in stock market trading. Leveraging technical analysis tools, particularly for Nifty, is crucial in anticipating market trends and price fluctuations. A combination of leading and lagging indicators provides valuable insights, aiding decision-making in trading and investments. This study focuses on prominent technical indicators such as machine learning moving average, the SuperTrend, and the Relative Strength Index (RSI), analyzing their parameters and evaluating their potential to create reliable and profitable trading strategies for Nifty.

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Machine Learning,SuperTrend,Relative Strength Index (RSI),Moving Average using Machine Learning

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 Abstract

वैदिक ज्योतिष शास्त्र में समय की गति और मानव जीवन के कर्मफल का सटीक आकलन करने के लिए अनेक दशा प्रणालियों का अवधान किया गया है। इन प्रणालियों में 'योगिनी दशा' अपने त्रिआयामी तांत्रिक आधार, चक्रीय सममिति और सूक्ष्म फलादेश क्षमता के कारण अत्यंत अनूठी और प्रभावी मानी जाती है। प्रस्तुत शोध पत्र में योगिनी दशा की पौराणिक एवं दार्शनिक पृष्ठभूमि, रुद्रयामल तंत्र में वर्णित इसके रहस्य, और इसकी गणितीय संरचना का सांगोपांग विवेचन प्रस्तुत किया गया है। इसके अतिरिक्त, इस शोध पत्र में मुख्यधारा की दशा प्रणालियों- विंशोत्तरी, अष्टोत्तरी और जैमिनी चर दशा-के साथ योगिनी दशा का एक गहन तुलनात्मक विश्लेषण किया गया है। यह अध्ययन सिद्ध करता है कि गतिशीलता और त्वरित फल प्रकटीकरण के दृष्टिकोण से वर्तमान कलियुग में योगिनी दशा की प्रासंगिकता और अचूकता सर्वोपरि है।

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 Keywords

योगिनी दशा, वैदिक ज्योतिष, विंशोत्तरी दशा, अष्ट योगिनी

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Background: Financial inclusion serves as a critical policy priority for developing economies undergoes rapid digital shifts. The National Payments Corporation of India (NPCI) introduced the Unified Payments Interface (UPI) in 2016, modernizing the digital payment space by offering secure, immediate, and interoperable transaction models that bridge gaps to formal financial networks. Objectives: The study evaluates the growth patterns and trajectory of UPI transactions alongside advancements in Indian financial inclusion from 2016 to 2025. It aims to empirically determine the relationship between UPI transaction surges and established macroeconomic financial inclusion metrics. Methodology: Utilizing secondary data derived from the Reserve Bank of India (RBI), NPCI, TRAI, MoSPI, and the Department of Financial Services, the research reviews the financial years 2016-17 through 2025-26. The structural analysis utilizes descriptive statistics, annual growth rates, Compound Annual Growth Rate (CAGR), trend evaluation, Pearson correlation matrices, and multiple linear regression models. Findings: The findings highlight an exponential rise in both the volume and value of UPI transactions alongside steady gains in broader banking metrics, such as the RBI Financial Inclusion Index and Pradhan Mantri Jan Dhan Yojana (PMJDY) account volumes. Statistical evaluations confirm a strong positive correlation and a significant regression relationship between UPI expansion and improved formal financial access. Conclusion: The study demonstrates that UPI operates as a pivotal digital financial innovation in India, fundamentally reducing cash reliance and accelerating inclusive economic development.

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Unified Payments Interface, Financial Inclusion, Digital Payments, RBI Financial Inclusion Index, PMJDY, Digital Economy.

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Sanskrit has served as the foundational language of India's classical knowledge systems, preserving centuries of philosophical, literary, artistic, and scientific traditions. In the field of Indian classical dance, particularly Kuchipudi, Sanskrit occupies a central position as the language of the Natya Shastra by Bharata Muni and the Abhinaya Darpana by Nandikeswara, two seminal treatises that continue to shape dance pedagogy, performance aesthetics, and artistic interpretation. Despite the enduring significance of these texts, contemporary institutional dance education often places greater emphasis on practical training than on developing linguistic competence in Sanskrit. Consequently, many students learn theoretical concepts through translations, limiting their ability to interpret original slokas, comprehend technical terminology, and appreciate the philosophical depth of classical dance. This paper examines the need to integrate Sanskrit into the Kuchipudi curriculum as a pedagogical tool for strengthening theoretical understanding, enriching performance practice, and preserving India's cultural heritage. Adopting a qualitative and descriptive approach, the study draws upon classical treatises, contemporary literature on dance education, curriculum studies, and the principles of the Indian Knowledge Systems (IKS) framework. It explores the pedagogical relationship between Sanskrit and Kuchipudi, identifies existing curricular gaps, and proposes a structured framework for incorporating foundational Sanskrit learning into dance education. The study argues that Sanskrit proficiency enables students to engage directly with primary sources, fosters authentic interpretation of rasa, bhava, abhinaya, and hasta terminology, and strengthens the connection between theory and practice. Integrating Sanskrit into Kuchipudi education also contributes to the preservation of intangible cultural heritage by ensuring that the knowledge embedded in classical texts is transmitted accurately to future generations. The paper concludes with recommendations for curriculum developers, universities, and dance institutions to adopt interdisciplinary approaches that reinforce both academic excellence and cultural continuity.

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Kuchipudi, Sanskrit, Natya Shastra, Abhinaya Darpana, Dance Pedagogy, Curriculum Development, Cultural preservation, Indian Knowledge Systems.

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Creative Commons Attribution 4.0 and The Open Definition