Volume 14 | Issue 4 |

 Abstract

Landslides are dangerous natural disasters caused by heavy rainfall, soil saturation, ground movement, and environmental instability, where early detection is difficult due to continuously changing conditions. This project proposes a Smart IoT-Based Real-Time Landslide Monitoring and Prediction System using ESP32 and Artificial Intelligence. Multiple sensors monitor soil moisture, rainfall, ground vibration, and accelerometer values in real time, while cloud integration enables remote monitoring. Sensor data is transmitted through Wi-Fi to the Blynk cloud platform for continuous data collection and analysis. A Random Forest-based AI model, along with a Weighted Risk Score mechanism, analyzes real-time and historical data to classify regions as safe or landslide-prone. When high-risk conditions are detected or when sensor values exceed safe thresholds, the system immediately generates visual alerts and sends real-time notifications to users through a web dashboard and mobile application. Enabling early prediction of landslides, reducing response time, and significantly improving disaster preparedness and safety in vulnerable areas.

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 Keywords

IoT, Landslide Monitoring, ESP32, Blynk Cloud, Artificial Intelligence, Random Forest Algorithm, Weighted Risk Score, Real-Time Monitoring, Hazard Prediction, Early Warning System

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Micro, Small and Medium Enterprises (MSMEs) form an essential part of India's economic activity, yet banks still face difficulties in assessing their creditworthiness due to inconsistent documentation, limited financial history, and high variability in business performance. Traditional machine-learning models and rule-based systems often fail to capture these complex patterns. This study aims to build a comparative deep-learning framework that evaluates multiple neural-network architectures--such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid models--to identify the most suitable model for MSME loan approval prediction. A large MSME-related dataset containing financial, behavioural, and operational impact factors is used for training and evaluation. The study follows a structured workflow: data preprocessing, feature selection, model development, model comparison, and feature-importance analysis. Performance is measured using accuracy, F1-score, and ROC-AUC. The goal is not only to select the best-performing network but also to highlight the factors that influence MSME loan approval outcomes. This research is expected to help financial institutions adopt more reliable and data-driven credit assessment techniques for MSME borrowers

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 Keywords

MSME Loan Approval, Deep Learning, ANN, CNN, LSTM, Credit Risk Prediction, Neural Network Comparison.

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 Abstract

Academic stress has emerged as a major psychological concern in higher education, significantly affecting students' well-being and academic performance. Sports students represent a unique subgroup within the student population, as they are required to simultaneously manage academic responsibilities and athletic commitments. This dual-role engagement often leads to increased stress due to time constraints, physical fatigue, and performance expectations.

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 Keywords

Academic stress, sports students, student-athletes, higher education, dual-role conflict, Bengaluru

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Traffic congestion and road accidents are major challenges in modern urban environments, leading to delays, increased fuel consumption, and safety risks. This case study explores the design, implementation, and effectiveness of a Traffic Alert System that utilizes real-time data collection, communication technologies, and intelligent algorithms to inform road users about traffic conditions. The system integrates data from sensors, GPS devices, and mobile applications to detect congestion, accidents, and route disruptions. Alerts are then transmitted to users through mobile notifications, digital signboards, and navigation systems. The study evaluates system performance based on response time, accuracy, and user satisfaction. Results indicate that the Traffic Alert System significantly improves traffic flow, reduces travel time, and enhances road safety. The case study also highlights challenges such as data reliability, infrastructure limitations, and privacy concerns, while suggesting future improvements using artificial intelligence and smart city integration.

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 Keywords

Traffic Alert System , Intelligent Transportation Systems (ITS) , Real-Time Traffic Monitoring ,GPS Technology ,Road Safety, Traffic Congestion , Smart Cities, Mobile Notifications, Data Analytics Accident Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

भारतीय ज्ञान परंपरा केवल अतीत की स्मृति नहीं है, बल्कि जीवन को समझने और संतुलित ढंग से जीने की एक दीर्घकालिक पद्धति है। यह मनुष्य को केवल ज्ञान अर्जित करने वाला प्राणी नहीं मानती, बल्कि उसे एक ऐसे जीवित चेतन अस्तित्व के रूप में देखती है जिसमें शरीर, मन, बुद्धि, भाव, आचरण और आत्मिक संवेदना सभी का महत्व है। इसी कारण भारतीय परंपरा में ज्ञान का अर्थ केवल सूचना या तथ्यों का संचय नहीं, बल्कि जीवन के साथ उसका संबंध स्थापित करना है। आज के समय में यह दृष्टि और अधिक महत्त्वपूर्ण हो जाती है, क्योंकि आधुनिक समाज में बाहरी प्रगति के साथ-साथ भीतरी असंतुलन, मानसिक तनाव, मूल्यहीनता, सांस्कृतिक दूरी और सामाजिक विखंडन जैसे संकट बढ़ते दिखाई दे रहे हैं। विशेष रूप से युवा और विद्यार्थी इन परिस्थितियों से गहरे रूप में प्रभावित हैं।यह शोध भारतीय ज्ञान परंपरा की आज की आवश्यकता, उसकी प्रासंगिकता और उसके व्यवहारिक प्रयोग की संभावनाओं को समझने का प्रयास करता है। अध्ययन का एक प्रमुख उद्देश्य यह भी है कि भारतीय ज्ञान परंपरा को केवल दार्शनिक विमर्श तक सीमित न रखा जाए, बल्कि शिक्षा, परिवार, समाज और नीति के स्तर पर उसे लागू करने योग्य रूप में सामने लाया जाए। इसी दिशा में इस अध्ययन में एक क्रियान्वयन रूपरेखा को प्रस्तुत किया गया है, जिसमें पाठ्यक्रम, शिक्षण पद्धति, भाषा, शिक्षक प्रशिक्षण, मूल्यांकन, डिजिटल माध्यम, परिवार और सामाजिक जीवन सबको एक साथ जोड़ा गया है।अध्ययन का निष्कर्ष यह है कि भारतीय ज्ञान परंपरा आज के भारत के लिए केवल सांस्कृतिक गौरव का विषय नहीं, बल्कि शिक्षा को पुनः मानव निर्माण की प्रक्रिया बनाने का आधार बन सकती है। यदि इसे सरल, सुगम, जीवनोपयोगी और समकालीन संदर्भों के अनुरूप प्रस्तुत किया जाए, तो यह व्यक्ति के भीतर नैतिकता, आत्मानुशासन, सामाजिक उत्तरदायित्व, मानसिक संतुलन और सांस्कृतिक आत्मविश्वास विकसित कर सकती है। इस प्रकार यह एक संतुलित, नैतिक और समरस समाज के निर्माण में महत्वपूर्ण भूमिका निभा सकती है।

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?????? ????? ?????? ,??????? ???? ,??????????? ,????? ??????????? ??????? ,????????? ?????? ???? 2020 , ?????-?????? ?????? ,????? ?????

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research focuses on the sustainable design development of the paisley motif in fabric painting. It is an invention from ancient Persian and Indian art and has developed through changes in shapes, styles, and designs. Even though it has been studied extensively in the context of fashion and textile printing, its usage in fabric painting, particularly the ways in which paisley can be created by designers, has not been well researched. This paper will address the origination of paisley patterns, their adaptation to fabric painting, techniques used, and strategies employed in making them in surface design.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Paisley Motif, Sustainable Design Development, Fabric Painting.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the rapid growth of decentralized finance (DeFi) and peer-to-peer transactions on high-performance blockchains, managing trustless escrow for assets between untrusted parties has become essential for security, transparency, and efficiency. Traditional centralized escrow services are slow, expensive, and vulnerable to counterparty risk. This project proposes a Secure Solana Escrow Smart Contract System that automates escrow creation, fund deposit, conditional release, and dispute handling using on-chain logic. The system comprises two primary modules: (1) Escrow Creation and Management, and (2) Intelligent Transaction Execution and Release. In the first module, users create escrow accounts via a web-based dApp; the smart contract (developed in Rust using the Anchor framework) validates parties, amount, and release conditions. The second module automatically releases funds upon condition satisfaction or enables refund/cancellation. The contract leverages Solana's Program Derived Addresses (PDAs) for secure fund holding, Solana Web3.js for client interaction, and SPL token support for fungible/non-fungible assets. All transactions are recorded immutably on the Solana blockchain, enabling real-time monitoring and auditability. The system is developed using Rust, Anchor framework, Next.js/React frontend, and Solana RPC nodes. Experimental testing on Solana Devnet demonstrates that the system achieves transaction confirmation in under 400 milliseconds with 99.2% success rate and near-zero compute unit wastage compared to manual or Ethereum-based escrow solutions. This project delivers a scalable, low-cost, and tamper-proof escrow solution for modern DeFi ecosystems, contributing to the advancement of trustless Web3 applications.

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 Keywords

Escrow Bond, Solana Blockchain, Escrow Smart Contract, Anchor Framework, Rust, Decentralized Finance (DeFi), Trustless Transactions, Program Derived Addresses (PDA), Secure Asset Management.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Black hole attacks pose a significant threat to network security, particularly in Mobile Ad Hoc Networks (MANETs), where malicious nodes absorb and discard data packets. This paper proposes a Long Short-Term Memory (LSTM)-based deep learning model to detect and prevent black hole attacks by analyzing sequential network behaviour. The model leverages temporal dependencies in routing patterns to identify anomalies. Experimental results demonstrate that the proposed approach achieves high detection accuracy and reduces packet loss compared to traditional methods.

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 Keywords

LSTM, Black Hole Attack, MANET, Intrusion Detection, Deep Learning, Network Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Institution of prison is a mechanism used for punishing convicts and detaining offenders waiting trial and is an integral part of criminal justice system. With the advancement of the humanitarian movement the concept of prisons and the prison system from mere facilitator for the exclusion and segregation of Criminal offenders has been transformed into reformative and correctional institutions. Originally, prisons were used only to detain criminals until their trials began and to house inmates for the duration of their sentences now focus is on psychological treatment of the offender to reform the criminal thought of the convict and to help him to return back into society. The essay is an analysis of the evolution of the prison system in India from ancient to modern time in the light of contemporary humanitarian movement, highlighting the pivotal role of the Supreme Court of India in safeguarding inmates' human rights through the interpretation and expansion of fundamental rights enshrined in the Constitution of India, thereby developing a comprehensive jurisprudence of prisoners' rights. This paper endeavors to study the landmark judgments of the Supreme Court of India, considering the profound sensitivity surrounding the human rights issues related to prisoners, including the torture of detainees, the disappearance of suspects, and the prolonged detention of under-trials without trial.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Prisoners, Punishment, Rehabilitation, Supreme Court, Humanities.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Early prediction and prevention are essential to reduce mortality rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques have been widely used to predict cardiovascular risk using patient health data. However, many AI models act as "black boxes," making their predictions difficult to understand for medical professionals. Explainable Artificial Intelligence (XAI) provides transparency by explaining how AI models make predictions. This paper presents an explainable AI framework for cardiovascular risk assessment using machine learning models and interpretation techniques such as SHAP and LIME.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Explainable Artificial Intelligence (XAI), Cardiovascular Disease (CVD) Prediction, Machine Learning, SHAP and LIME, Healthcare Decision Support Systems.

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Creative Commons Attribution 4.0 and The Open Definition