Volume 14 | Issue 6 |

 Abstract

Increased population and rampant urbanization has caused a tremendous amount and complexity in solid waste which poses a big problem to the traditional waste management structures. Conventional means of waste collection process is mostly manual, time-stated and inefficient and this geometrical overflow of bins, inadequate separation of waste, hiked up cost of operation and environmental pollution. In order to overcome these restrictions, in this paper, there was a review and conceptual implementation of a Smart Waste Management System with Raspberry Pi and IoT-based monitoring systems incorporated and coupled with enhanced waste sorting and real time alerts.

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 Keywords

A Review on IoT-Driven Intelligent Waste Monitoring System with Raspberry Pi Integration

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 Abstract

The growing agricultural field demands, integration of modern techniques to improve product quality. The artificial intelligence offenly called as AI found its applications in every field nowadays. The effective use of such technique can lead to increase output. The agricultural field experiences several challenges like crop prediction, crop health monitoring, and weather forecasting, disease prediction for plants and payment gateway systems for effective farming. The implementation of AI in agricultural field offer better flexibility, better stability, better performance and cost effectiveness. This paper highlighted exhaustive review of use of the AI techniques in agricultural field by focusing on higher productivity and economical considerations.

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 Keywords

Crop prediction, Crop health monitoring, Plant disease prediction, Weather forecasting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The existing conventional financial advice, based on traditional models, is not only liable to various biases but also not capable of dealing with the high degree of volatility of the Indian Market. The aim and objective of conducting this study are to bridge the gap between the existing shortcomings through developing an innovative web-based AI-Driven Portfolio Management & Advisory System for adaptive and complaint financial advice on each and every financial product listed on the exchanges of India, such as stocks, mutual funds, bonds, etc. The approach undertaken involves using a powerful multi-layered mechanism, such as reinforcement learning algorithms for adaptive portfolio allocation, NLP algorithms for incorporation of market sentiments, quant-based algorithms such as Monte Carlo and quant for risk analysis.

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 Keywords

AI Advisory, Black-Litterman, EDI, India Market, Monte Carlo Simulation, Portfolio Management, Reinforcement Learning, SEBI Compliance

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Creative Commons Attribution 4.0 and The Open Definition

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Mental health challenges such as stress, anxiety, and emotional imbalance are increasingly affecting students, youths, and working professionals in today's fast-paced society. Social stigma, lack of awareness, and limited access to professional support often prevent individuals from seeking timely help. This paper presents EmoCare, an AI-based web platform designed to promote emotional well-being through real-time emotion detection and personalized support. EmoCare utilizes Natural Language Processing (NLP) and Sentiment Analysis to analyze user text and identify emotional states such as positivity, negativity, stress, and anxiety. Based on the detected emotions, the system generates tailored responses in the form of motivational quotes, relaxation techniques, and supportive messages. Developed using React.js for the frontend, Node.js for the backend, and MongoDB for data storage, the platform ensures a secure, user-friendly, and privacy-focused environment. EmoCare provides a free and stigma-free digital space for emotional expression, aligning with global mental health initiatives by the World Health Organization (WHO) and Google. The proposed system demonstrates how artificial intelligence can effectively contribute to accessible mental health support and digital well-being.

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 Keywords

Mental Health, Sentiment Analysis, Emotional Support, AI Wellness Platform, Well-being

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Creative Commons Attribution 4.0 and The Open Definition

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This paper addresses the issue of admission status ambiguity during the centralized admission process. It reveals the process of deploying a unique way of adding transparency to the system which will benefit the prospective students, parents, admission team and even the management officials. This paper shows an innovative method of displaying the admission allocation and actually admitted students on the Institute website. In Maharashtra, the higher education Institutes admit students through Centralized Admission Process which is conducted by Government of Maharashtra. On the MH CET admission website, the list of allotted students to the Institute is available. There are few admission rounds conducted during the whole process where students get the chance to take admission in the allotted college or to opt for betterment. Some of the seats are available for Institute Level Admission. For these seats also the merit list needs to be displayed based on which the seats in each branch are filled. During this process, Parents and students have to visit various institutes for checking the seat availability in the institutes of their choice. The admission team in every college also have to face certain challenges such as completing the student registration, fees payment etc. including other formalities. This paper attempts to create a web portal which works as Admission Indicator for a particular institute. It displays a number of seats allotted for every branch. It also gives a real time tracking to the students and parents to check how many seats are filled and the count of vacant seats. This Indicator display will help all stakeholders in decision making thereby reducing ambiguity and confusion.

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 Keywords

Real Time Admission Count Indicator, Gradio, GUI, Python.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of counterfeit medicines presents a significant challenge to public health, supply chain integrity, and regulatory compliance in global healthcare systems. Conventional centralized supply chain management (SCM) systems suffer from limited transparency, susceptibility to data tampering, and single points of failure. To address these issues, this paper proposes MediLedger, a blockchain-based medicine traceability framework that ensures end-to-end visibility, authenticity, and security across pharmaceutical supply chains. The system utilizes blockchain's immutable distributed ledger, cryptographic hash functions, smart contracts, and QR-code-based verification to prevent the circulation of counterfeit drugs. Each medicine unit is assigned a unique digital identity recorded on the blockchain, enabling real-time verification by manufacturers, distributors, regulators, and consumers. A custom validation and consensus mechanism is employed to ensure data integrity, tamper resistance, and automatic recovery from malicious or faulty nodes. Experimental results demonstrate that MediLedger improves traceability, reduces verification time, and enhances trust among all stakeholders in the pharmaceutical ecosystem.

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 Keywords

Blockchain, Counterfeit Product Detection, Supply Chain Management, QR Code Authentication, Custom Blockchain, SHA-256.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Counterfeit products are a major problem in modern supply chains and cause financial loss, brand damage, and safety risks to consumers. Traditional centralized systems are not secure and can be easily modified or attacked. To overcome these issues, this paper proposes a custom blockchain-based decentralized system for anti-counterfeit product authentication in IoT-integrated supply chains. Each product is registered by the supplier and assigned a unique QR code. Product information is secured using SHA-256 hash generation and stored in a custom blockchain network. The company module verifies product movement, while end users can scan QR codes to check product authenticity in real time. If any mismatch is found, the system alerts users about possible counterfeit products. The proposed system improves transparency, data security, and trust among supply chain participants and provides an effective solution for counterfeit detection.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Counterfeit Product Detection, Supply Chain Management, QR Code Authentication, Custom Blockchain, SHA-256.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Automated Exoplanet Detection Using Artificial Intelligence and Machine Learning

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Automated Exoplanet Detection Using Artificial Intelligence and Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Education in the twenty-first century is undergoing rapid transformation due to advancements in information and communication technology. Traditional methods of teaching are increasingly supplemented with digital tools and online resources to create more effective and engaging learning experiences. Among the various innovative instructional approaches, blended learning has emerged as one of the most widely adopted methods in schools and higher educational institutions. Motivation for learning is a crucial factor influencing students' academic success. It refers to the internal and external forces that stimulate students' interest, enthusiasm, and willingness to participate in learning activities and achieve educational goals. Students who are highly motivated tend to demonstrate greater attention, persistence, effort, and commitment to their studies. The present study investigated the effect of the Blended Learning Approach on motivation for learning among elementary school students. A pre-test-post-test control-group experimental design was employed to examine the effectiveness of blended learning compared with conventional face-to-face teaching. The study was conducted in a purposively selected private school in Sirsa district, Haryana, India. A total of 120 eighth-grade students participated in the study and were randomly assigned to an Experimental Group (n = 60) and a Control Group (n = 60). The purposive sampling technique was adopted to select participants with similar socio-economic backgrounds, academic curriculum, and school facilities. The Experimental Group received instruction through a Blended Learning Approach, while the Control Group was taught using the Traditional Teaching Approach. Motivation for Learning was measured using a self-developed Motivation for Learning Scale. The findings revealed that students exposed to blended learning demonstrated significantly higher motivation to learn than those taught through traditional methods. The study concludes that blended learning is an effective pedagogical strategy for enhancing learners' motivation at the elementary school stage.

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 Keywords

Blended Learning, Motivation for Learning, Experimental Study, Elementary Education, Social Science, Eighth-Grade Students.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Juvenile delinquency in India is often misunderstood as an issue of individual wrongdoing, but a closer examination shows that it is largely shaped by social surroundings. Edwin H. Sutherland's Differential Association Theory offers a useful framework to understand how criminal behaviour is learned through interaction with family, peers, and environment. This paper examines the role of Juvenile Justice Boards (JJBs) in addressing such learned behaviour within the framework of the Juvenile Justice (Care and Protection of Children) Act, 2015. It also incorporates relevant provisions of the Bharatiya Nyaya Sanhita, 2023 (BNS) and Bharatiya Nagarik Suraksha Sanhita, 2023 (BNSS) , which now govern substantive and procedural criminal law in India. Through analysis of landmark judgments such as Sheela Barse v. Union of India , Salil Bali v. Union of India, and Pratap Singh v. State of Jharkhand , the paper highlights the judiciary's consistent emphasis on rehabilitation over punishment. The paper argues that JJBs are structurally designed to address learned criminal behaviour, but their effectiveness is limited by institutional weaknesses, lack of trained personnel, and social stigma. It concludes by suggesting reforms to strengthen their role in breaking cycles of criminal learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Juvenile delinquency, JJB'S, NCRB, Differential Association

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Creative Commons Attribution 4.0 and The Open Definition