Volume 13 | Issue 4

 Abstract

This project focuses on the design and development of a grains screening machine intended to efficiently separate grains based on size, quality, and cleanliness. The machine utilizes a series of vibratory sieves and mechanical filters to automate the sorting process, thereby reducing manual labor and improving productivity. The goal is to enhance post- harvest handling by ensuring only clean and uniform grains are selected for packaging and sale. This system is particularly beneficial for small to medium-scale farmers and grain processing units, aiming to increase efficiency, reduce waste, and maintain high-quality standards in grain output. screening plays a crucial role iimproving product quality, storage longevity, and market value, making it an integral part of modern post-harvest grain handling systems.

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 Keywords

1 Grain Separator 2 Grain cleaning 3 Grain quality control 4 dust control

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 Abstract

GreenBot is a Bluetooth-controlled agricultural robot made to help farmers with small farming tasks and reduce manual work. This project has two main parts: one is the robotic vehicle, and the other is a solar charging station. The robot works using an ATmega328P microcontroller and performs operations like soil ploughing, moisture level checking, and obstacle detection. When the soil becomes too dry, the system alerts using a buzzer. It also has an ultrasonic sensor to avoid obstacles during movement. The robot is controlled through Bluetooth using an Android mobile phone, which makes it simple to operate from a distance. The second part of the project is the solar charging station, which charges the robot using solar energy. A small solar panel is used along with a battery to store and supply power. This makes the system useful in rural or remote areas where electricity is not always available. GreenBot is a low-cost, eco-friendly solution that supports smart farming and helps save time and energy in the field.

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 Keywords

Smart Farming, Bluetooth Robot, ATmega328P, Soil Monitoring, Solar Charging

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In forensic investigations, the relationship between the criminal, the victim, and the crime scene can be firmly established through the detection of latent finger marks. Latent fingerprints are one of the most frequently found evidence in crime scenes and are widely recognized as a tool for human personal identification. The purpose of this research is to investigate the feasibility and effectiveness of using unconventional powder in forensic investigations, as well as to emphasize their contributions to sustainable and ecologically conscientious crime scene analyses. In this research paper, a new method for the development of Latent fingerprints is used that's Animal hair powder. The fine particles of animal hair Powder get combined with the fatty acid & oil present in the sweat of a fingerprint and the print is visible to our naked eyes. Animal hair, which is considered as animal waste, it is a much cheaper and readily available option. This paper presents a non-destructive powder dusting method which is simple, non-toxic, most convenient, easily preparable, not time - consuming and the powder is available in black color . Moreover, it was also good for environmental waste management. This research not only presents a novel approach to fingerprint development but also highlights the potential for the application of unconventional powders in forensic investigations. This study also opens up the scope of further study in this way.

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Latent Fingerprint, Unconventional Powder, Animal Hair, Non-Destructive.

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UV-visible spectroscopy is a widely employed analytical technique in pharmaceutical analysis, ensuring the quality and regulatory compliance of dosage forms. This comprehensive review focuses on the principles, advancements, and applications of UV-visible spectroscopy in method development for quantitative analysis of active pharmaceutical ingredients. The review also explores the versatility of UV-visible spectroscopy in analyzing various dosage forms, including tablets, capsules, and solutions. The review emphasizes the importance of analytical method validation in accordance with ICH guidelines and regulatory requirements. Various methods such as the calibration curve method, absorptivity value method, and multicomponent analysis are reviewed.

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UV-visible spectroscopy, Analytical method development, Method Validation, Pharmaceutical Analysis.

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ABSTRACT: Reproductive tract infections, adding burden to the morbidities in women especially in developing countries. Women, who are in reproductive age group, are at higher risk of contracting RTIs easily. Bacterial vaginosis, candidiasis and trichomoniasis are the commonly reported RTIs in India. Hence this study was planned to find the prevalence of self reported symptoms of RTIs and the prevalence of RTIs of public health importance in women of reproductive age group 18-45 years in a rural area.

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Keywords : Reproductive Tract Infection ,Married Women, Village.

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This paper presents the fabrication of an electric-powered go-kart designed to achieve an optimal balance between lightweight construction, durability, safety, and high performance. Engineered specifically for racing on flat circuits, the go-kart features a streamlined design that includes four wheels, a seat, a steering mechanism, and a braking system, intentionally excluding suspension and differential components to minimize weight and mechanical complexity. Powered by a lead-acid battery, the vehicle offers an eco-friendly alternative to conventional fuel-driven models. The design focuses on four primary attributes: durability, safety, minimal weight, and enhanced performance. The chassis is constructed using mild steel pipes, chosen for their strength-to-weight ratio, ensuring a rigid and secure frame. Special attention has been given to material selection and structural design to ensure mechanical integrity and stability under race conditions. The result is a dependable, efficient, and race-ready electric go-kart, well-suited for high-speed performance on smooth tracks.

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Electric Go-Kart, Fabrication, Lightweight Chassis, Lead-Acid Battery, Differential Drive Elimination

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 Abstract

Kasturba Gandhi Balika Vidyalaya (KGBV) aims at providing free education and support to girls in grades 6 through 12 in the form of residential institutions. KGBV mission is to reduce gender disparities in education and promote holistic development. Kasturba Gandhi Balika Vidyalaya (KGBV) program empower girls who are from underprivileged backgrounds, especially those from Scheduled Castes (SC), Scheduled Tribes (ST), Other Backward Classes (OBC), and minority groups. Although the program is succeeding in its mission of enhancing academic motivation and fostering both academic and non-academic skills, but there are still some challenges like high dropout rates, inadequate infrastructure and societal barriers that become obstacles in girls' education.

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 Keywords

KGBV, Gender Disparities, Academic Motivation, Girls' Education, Empowerment, Infrastructure.

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 Abstract

Phishing attacks pose a significant cybersecurity threat, necessitating innovative solutions for detection and prevention. Traditional server-side defenses have limitations, prompting the need for client-side protection. This project introduces PhishCatcher, a machine learning-powered tool designed to detect and mitigate evolving web spoofing threats. By transforming raw URLs into numerical lexical data, PhishCatcher enables precise identification of malicious URLs using advanced classification techniques. It operates within controlled environments to analyze attack patterns, entry points, and tactics employed by cybercriminals. Strengthening the CIA triad, PhishCatcher enhances authentication standards and fortifies cybersecurity defenses. Unlike conventional approaches, it offers real-time protection without requiring modifications to targeted websites. Users benefit from enhanced online safety, reducing the risk of identity theft and fraud. By integrating machine learning-driven classification with behavioral analysis, PhishCatcher provides a comprehensive strategy to counter phishing attacks, safeguard user privacy, and protect organizations against emerging cyber threats.

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 Keywords

CyberSecurity, Machine Learning Algorithm, Confidentiality, Integrity, Availability.

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This project proposes an AI-based Petition Analysis and Grievance Management System that automates public complaint handling. The system, employing NLP and ML, categorizes petitions, identifies urgency, and directs them to the right departments. Dashboards for real-time tracking promote transparency and accountability, while sentiment analysis prioritizes crucial issues. Manual effort is minimized, response time is enhanced, and data-driven governance is enabled through actionable insights into public concerns.

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AI, Machine Learning, Natural Language Processing, Grievance Redressal,

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This dissertation examines the changing nexus of Artificial Intelligence (AI) and corporate legal practice in India, providing a detailed analysis of how AI technologies are reconfiguring the functioning, delivery, and regulation of legal services in the corporate space. As India undergoes a rapid digitalization across industries, the legal sector--historically considered conservative and process-oriented--is increasingly adopting AI-led innovations to boost efficiency, precision, and decision-making. The research commences by situating the worldwide rise of AI technologies and chronicles their development in legal frameworks, with specific reference to the Indian business legal context. It discusses the implementation of AI-based tools across the most significant legal procedures like contract analysis, legal research, due diligence, litigation planning, fraud detection, and regulatory compliance. By citing particular platforms such as CaseMine, Prarambh (formed by Cyril Amarchand Mangaldas), Anuvaad, and global systems such as IBM Watson and COIN by JPMorgan Chase, the research brings forth the real-world application of AI within corporate law practice. Using doctrinal and comparative legal research approaches, the dissertation examines the role of AI in improving speed, lowering costs, and enhancing risk mitigation in corporate legal processes. It also considers the law and ethics aspects of AI embedding--data protection issues, prejudice through algorithms, professional negligence, and erosion of human judicial wisdom. The legislative framework is viewed critically in light of a comparison of legal instruments and governmental interventions across the United Kingdom, United States, European Union, China, and Australia, and offers India's path to regulation valuable lessons. An important value added to this work is the in-depth analysis of Indian legal laws--that include the Companies Act, 2013; SEBI legislation; the Information Technology Act, 2000; and incoming data protection acts--and how these intersect with applications of AI for legal purposes. The research ends on a note proposing a strategic map for the Indian legal profession and suggesting regulation amendments, moral benchmarks, and professionalism guidelines in place to see the use of AI in the corporate legal fraternity used responsibly, fairly, and openly. In conclusion, this dissertation presents a timely and forward-looking analysis of the ways in which AI can enhance, supplement, and possibly change legal practice within India's corporate world, such that technological development is in tune with constitutional principles, client interest, and the fundamental values of justice.

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 Keywords

Artificial Intelligence (AI), Corporate Legal Sector, Legal Technology, AI in Indian Law, Legal Research Automation, Contract Analysis, Due Diligence, Compliance Monitoring, Litigation Management, Companies Act 2013, SEBI Regulations, Legal Ethics, Algorithmic Bias, AI Governance,

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Knee osteoarthritis (KOA) is a degenerative joint condition that affects millions globally, especially older adults. Timely and accurate diagnosis is essential to slow disease progression. This paper presents a deep learning-based system for automated KOA detection using X-ray images, graded according to the Kellgren and Lawrence (KL) scale. Four convolutional neural networks--ResNet-34, VGG-19, DenseNet-121, and DenseNet-161--are fine-tuned through transfer learning and combined using an ensemble strategy. To model the ordered nature of KOA severity, Conditional Ordinal Regression (CORN) is employed. The system integrates Explainable AI (XAI) using Eigen-CAM visualizations to highlight diagnostic regions in the X-ray images. Evaluation on the Osteoarthritis Initiative dataset shows state-of-the-art results, with 98% accuracy and a Quadratic Weighted Kappa (QWK) score of 0.99. The final model is deployed via a Streamlit web application, offering an accessible interface for real-time diagnosis. The approach provides a reliable and interpretable tool for assisting radiologists in KOA assessment.

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Knee Osteoarthritis, Deep Learning, Kellgren-Lawrence Grading, Explainable AI.

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In modern educational institutions, effective communication is a key pillar of administrative efficiency. Traditional notice boards, often paper-based and manually updated, pose significant limitations in terms of scalability, timeliness, and environmental sustainability. This paper presents the design and implementation of an AI-powered smart notice board system that addresses these challenges through automation, multimedia integration, and conversational AI. The proposed system is built around a Raspberry Pi 4 platform, functioning as a compact and affordable local server. It hosts a Django-based web application that allows authorized administrators to upload and manage notices in the form of text, images, and videos. These notices are dynamically rendered on a connected HDMI display in a continuous loop. The system is further enhanced by the integration of a Rasa-powered chatbot, embedded within the display interface, which enables real-time interaction with users. The chatbot is trained to handle frequently asked academic queries, including examination schedules, project deadlines, and placement updates, thereby reducing repetitive student-faculty interactions. Designed to operate fully offline, the system is ideal for deployment in environments with limited network infrastructure. It emphasizes paperless communication, user interactivity, and real-time responsiveness. Extensive testing confirms the system's stability, ease of use, and potential for scalability across departments and institutions. This work contributes to the ongoing digital transformation of educational infrastructure, combining IoT, web technologies, and natural language processing into a cohesive smart campus solution.

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Smart Notice Board, Raspberry Pi, Django, Rasa Chatbot, Artificial Intelligence.

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Adverse Drug Reactions (ADRs) resulting from drug-drug interactions are a major healthcare concern. While Graph Neural Networks (GNNs) effectively model these interactions, their one-dimensional processing limits complex feature extraction. This research introduces a novel extension by integrating a two-dimensional Convolutional Neural Network (CNN2D) to enhance ADR prediction. By converting drug interaction data into 2D matrices, CNN2D captures intricate spatial relationships, complementing the GNN's graph-based insights. This hybrid model achieves a superior prediction accuracy of 99.87%, significantly outperforming traditional methods like KNN and Decision Trees. The extension showcases the power of deep learning in advancing drug safety evaluation.

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Adverse Drug Reactions, Drug-Drug Interactions, Graph Neural Networks, Convolutional Neural Networks, Self-Supervised Learning, SMILES Representation, Deep Learning, Side Effect Prediction, Drug Safety, TF-IDF Vectorization.

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The growing burden of Non-Performing Assets (NPAs) in Indian Public Sector Banks (PSBs) poses a significant threat to financial stability and economic growth. This article evaluates the effectiveness of current legal and institutional mechanisms for NPA resolution, such as SARFAESI, DRTs, and the Insolvency and Bankruptcy Code (IBC). Despite their roles, persistent issues like judicial delays, enforcement gaps, and inadequate institutional coordination hinder their success. Through critical evaluation and comparison with global practices, this article proposes a strategic shift from reactive recovery to proactive reforms aimed at sustainable resolution.

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NPAs, Public Sector Banks, SARFAESI, IBC, DRT, Legal Reform, Sustainable Finance, Banking Law

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Background: Suicide among adolescents is a rising global concern, particularly in low- and middle-income countries. Adolescents often face stressors that predispose them to suicidal ideation and behaviour. Objective: To evaluate the effectiveness of a structured teaching program in improving knowledge about risk factors and prevention of suicidal behaviour among adolescents. Methods: A pre-experimental one-group pre-test post-test design was used. A total of 100 adolescents aged 10-16 years from selected schools in Bangalore Urban were selected through non-probability convenience sampling. A structured self-administered questionnaire was used to assess knowledge before and after the intervention. Results: In the pre-test, 67% had inadequate knowledge, 33% had moderate knowledge, and none had adequate knowledge. In the post-test, 76% had adequate knowledge, 24% had moderate knowledge, and none remained in the inadequate category. A significant increase in mean knowledge score was observed (pre-test: 8.35, post-test: 16.31), with a mean difference of 7.96 (t=31.09, p<0.05). Conclusion: The structured teaching program was effective in enhancing adolescents' knowledge regarding suicidal risk factors and preventive measures. Keywords: adolescent mental health, suicide prevention, structured teaching program, risk factors, nursing education.

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Keywords: adolescent mental health, suicide prevention, structured teaching program, risk factors, nursing education.

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Effective detection has become a critical challenge due to the rise of IoT devices, which has led to an increase in botnet attacks. This research extends traditional botnet detection models by incorporating advanced ensemble deep learning techniques to improve prediction accuracy. We integrate CNN, LSTM, and GRU in hybrid architectures such as CNN + LSTM + GRU and CNN + BiLSTM + GRU, which effectively capture both spatial and temporal patterns in IoT network traffic. Feature selection using Mutual Information optimises model performance, reducing computational complexity while improving detection efficiency. Additionally, a Flask is used to create a user-friendly front-end application, which allows for smooth testing and evaluation of the model. Secure user authentication protects sensitive information and ensures data integrity. The experiment's findings demonstrate that the suggested ensemble models achieve superior accuracy, surpassing 97%, in detecting botnet activity, highlighting their effectiveness in securing IoT environments.

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Botnet Detection, IoT Security, Deep Learning, CNN, LSTM, GRU, Hybrid Models, Ensemble Learning, Feature Selection, Mutual Information, Flask Framework, User Authentication, Cybersecurity.

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A common dental ailment called periodontitis is brought on by bacterial infection of the tooth's surrounding bone. To avoid serious consequences like tooth loss, early identification and accurate treatment are essential. Dental experts have historically diagnosed periodontal disease by manually identifying and labelling the condition, a procedure that takes a great deal of skill and involves tedious, time-consuming activities. The goal of this work is to use dental imaging datasets to automatically detect and classify periodontitis by utilising sophisticated neural network architectures. By effectively analysing photos for early-stage illness detection using deep learning techniques, the suggested method lessens the need for manual inspection. Multiple optimisation tactics inside the neural networks are compared to show how they affect detection performance. Results reveal that the suggested technique provides greater accuracy, with a 2D Convolutional Neural Network model having a detection accuracy of 96.93%. This high-performance solution highlights the promise of automated systems in strengthening diagnostic precision, efficiency, and scalability for periodontitis, thereby improving patient outcomes and streamlining clinical procedures.

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 Keywords

YOLOv8; Tooth Position Detection; Periodontitis; Bitewing Radiograph; Convolutional Neural Networks (CNN); Deep Learning; Dental Imaging; Automated Diagnosis; Medical Image Processing; Early Disease Detection; Diagnostic Accuracy; Neural Network Optimization.

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In today's era of information overload, accessing relevant and meaningful news has become increasingly challenging. This research presents a Personalized News Aggregator with Sentiment Analysis--a system designed to deliver user-centric news content tailored to individual interests and preferences. The platform aggregates news from diverse sources and leverages Natural Language Processing (NLP) techniques to analyze the sentiment of each article, helping users better understand the emotional tone and context of the information they consume. The system integrates machine learning models for sentiment analysis with recommendation algorithms to enable personalized content delivery. By offering features such as filtering, keyword search, and sentiment-based categorization, the solution addresses limitations found in traditional news platforms. This paper explores the technical implementation of the system, including data collection, preprocessing, model selection, and web-based deployment. It also highlights the system's potential in enhancing information accessibility and improving user satisfaction through a more refined, relevant, and engaging news experience.

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Personalized News Aggregator, Sentiment Analysis, Natural Language Processing (NLP), Machine Learning, Recommendation Algorithms, User Preferences, News Personalization.

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This research paper critically examines gender-based violence (GBV) in India, focusing on the legal frameworks, implementation gaps, and societal challenges affecting women's safety. Despite comprehensive legal measures, the persistence of GBV highlights systemic flaws and social impediments. This paper aims to evaluate existing laws, identify enforcement issues, and propose practical solutions to enhance women's safety.

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Gender-Based Violence, Women's Safety, Legal Framework, Implementation Gaps, Societal Challenges, India

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This paper explores the intersection of digital cultural heritage and internet governance, highlighting the opportunities and challenges of preserving cultural memory in the digital era. It examines the role of technologies like the metaverse in safeguarding heritage, while addressing critical concerns related to cybersecurity, intellectual property, algorithmic bias, and data sovereignty. Emphasizing the need for inclusive, ethical, and sustainable practices, the paper advocates for a comprehensive international framework that ensures the long-term protection and equitable representation of cultural heritage in an increasingly digitized world.

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Cultural heritage, Data Privacy, Security, internet governance

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