Volume 13 | Issue 4 | April 2025

 Abstract

Oracle Cloud integration with third-party systems is the core element of enterprise IT architecture in the current era, allowing continuous data streams and increased interoperability between systems. However, despite increasing adoption of cloud computing and the rich feature set of Oracle Cloud, there are significant barriers to efficient and secure integration with third-party systems. These are data silo management, ensuring data quality and consistency, reducing security risks, and coping with the disparity in data model complexity. The literature presents several integration strategies, such as API-based, event-driven architectures, middleware solutions, and microservices; however, there are lacunae in understanding how to integrate these approaches in an overall manner to get the best performance. There are fewer studies on how the newer emerging technologies such as artificial intelligence, blockchain, and low-code/no-code solutions can assist in overcoming the integration complexities. The research gap identified relates to the need for the creation of more adaptive, automated, and scalable integration solutions that can suitably address the changing needs of businesses operating in multi-cloud and hybrid environments. While the existing literature largely focuses on different integration frameworks and tools, there is a substantial shortage of research into their practical application, particularly in large, multinational organizations. This review synthesizes and examines existing research between 2015 and 2024, identifying key strategies for preventing integration issues while also suggesting areas that need further research. It emphasizes the need to utilize interdisciplinary approaches that combine automation, security, and compliance frameworks to enable efficient, secure, and scalable Oracle Cloud integrations with external systems. Bridging these gaps is important to drive advancements in cloud integration approaches and to promote more integrated and interoperable enterprise environments.

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 Keywords

Oracle Cloud, third-party system integration, data flow, systems interoperability, cloud integration challenges, API integration, event-driven architecture, middleware, microservices, automation with AI, blockchain security, low-code platforms, hybrid cloud, multi-cloud, data synchronization, integration frameworks, enterprise systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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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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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 Keywords

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

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition