Volume 14 | Issue 3 |

 Abstract

The paper explores the early applications of artificial intelligence in judicial proceedings by the Indian judiciary and specifically, the aspects of procedural fairness, accountability and algorithmic transparency. The recent efforts of the Supreme Court in developing SUPACE (legal research assistant), SUVAS (machine translation), TERES (speech-to-text) and LegRAA (legal drafting assistant) demonstrate how India is trying to use AI to be efficient. The paper discussed such significant changes as Bharatiya Sakshya Adhiniyam 2023 (new evidence law) and Digital Personal Data Protection Act 2023 (notably S.17) and evaluate them in relation to the influence on AI-aided justice. These domestic trends are compared with international frameworks: the EU's AI Act (classifying judicial AI as "high-risk" and demanding documentation), U.S. law (case law like State v. Loomis on risk-assessment tools, the Mata v. Avianca incident and confrontation-clause debates), Brazil's CNJ Normative Act (strict AI oversight and transparency mandates) and China's "Smart Courts" (AI Judges and Internet Courts under centralized supervision).The paper analyses the literature that mentions both positive aspects (efficiency gains) and negative ones (bias, lack of transparency, mistrust) in global judicial AI scholarship. This review reveals a weak point in the granular analysis of the regulatory stance in India relative to the AI fairness and rights. Finally, it suggests specific reforms: judicial Standard operating systems to responsibly use AI (e.g. enforced human-in-loop verification, audit trails), including changes in evidence and data legislation (making algorithms accountable and demand informed consent even with Section 17).

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 Keywords

Artificial Intelligence; Procedural Fairness; Judicial Accountability; Algorithmic Transparency; AI-Assisted Adjudication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ContentNexus is a modern full-stack blogging platform designed to streamline the digital publishing process through a combination of AI-driven automation and user-centric editing tools.Built with a scalable React and Node.js architecture, the platform features a sophisticated Tip-Tap editor that provides familiar rich-text formatting controls similar to professional word processors, alongside Generative AI capabilities to assist in rapid content creation. By offering diverse customizable templates and seamless custom domain publishing. Content Nexus reduces technical barriers and can improve writing efficiency by up to 56%. This system empowers creators to deploy professional, SEO-optimized websites while maintaining full control over their content through an intuitive, distraction-free environment.

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 Keywords

Generative AI, Blog Generator, Content Automation, React & Node.js, SEO (Search Engine Optimization) .

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Creative Commons Attribution 4.0 and The Open Definition

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Metro rail systems have emerged as a crucial solution to address the challenges of urban transportation in rapidly growing cities. With increasing population, traffic congestion, and environmental concerns, cities like Chennai require efficient and sustainable transport systems. Metro rail provides a fast, reliable, and high-capacity mode of transport that reduces travel time and improves urban mobility. This study focuses on evaluating metro transportation in Chennai based on convenience, inconveniences, and environmental impact. The research examines how metro services influence commuter behavior and identifies key factors affecting usage such as accessibility, affordability, service quality, and environmental awareness. The study also highlights the challenges faced by users, including overcrowding, limited network coverage, and last-mile connectivity issues. Primary data is collected through a structured questionnaire, and statistical tools such as percentage analysis and correlation are used for interpretation. The findings reveal that convenience and service quality play a major role in influencing metro usage, while environmental awareness encourages sustainable transport choices. The study provides valuable insights for policymakers and metro authorities to improve service efficiency, enhance commuter satisfaction, and promote sustainable urban development. Overall, metro transportation is an effective solution for improving urban mobility and reducing environmental impact.

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 Keywords

Metro rail, Metro rail convenience, Metro Rail Inconvenience, Metro Rail Environmental impacts

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Customer satisfaction refers to the level of happiness or contentment a customer feels after purchasing and using a product or service. It is determined by how well the product or service meets or exceeds the customer's expectations. If the performance matches or goes beyond expectations, customers feel satisfied; if it falls short, they feel dissatisfied. The research is based on both primary and secondary data. Primary data was collected from 90 customers of vespa scooters through a structured questionnaire using the convenience sampling method. The collected data was analyzed using percentage analysis and presented through tables and graphical representations. The findings reveal that most respondents are satisfied with Vespa scooters, particularly in terms of design, style, driving comfort, and overall performance. However, some respondents expressed concerns regarding mileage, colour options, and availability of spare parts. The study concludes that Vespa has a strong brand image and maintains a good level of customer satisfaction, but improvements in mileage efficiency and service support can further enhance customer experience and strengthen customer loyalty.

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 Keywords

customer satisfaction ,vespa scooters, two wheeler industry, performance,milage,price,after sales service,customer perception exploratory research

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Creative Commons Attribution 4.0 and The Open Definition

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Disaster management requires rapid, coordinated, and data-driven responses to minimize loss of life and resources. The traditional disaster management systems face the challenges of delayed communication and coordination of resources during disaster management operations. This paper proposes a web-based disaster management system that utilizes intelligent decision support principles to improve the effectiveness of disaster management operations. The proposed disaster management system will provide a unified platform for disaster victims to access the nearest disaster management centers and essential commodities in real-time. The proposed system will also provide a platform for the coordination of volunteers during disaster management operations to improve the utilization of human resources during disaster management operations. The proposed system will provide the following features: real-time alert systems, emergency communication systems, and administrative monitoring systems to improve the effectiveness of disaster management operations. The proposed disaster management system will bridge the gap between disaster victims, volunteers, and authorities during disaster management operations to improve the effectiveness of disaster management operations.

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 Keywords

Disaster management, Artificial intelligence, Emergency response, Real-time systems, Resource allocation, Volunteer coordination

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Leukemia is a form of blood cancer that affects white blood cells and needs to be detected early so that treatment can be more effective. In many cases, doctors examine blood samples under a microscope to diagnose the disease, but this process can take a lot of time and usually depends on the experience of skilled hematologists. To address this challenge, this study introduces an automated system for detecting leukemia by using deep learning and computer vision methods. The proposed system combines YOLO-based object detection to locate and identify different types of blood cells, along with an EfficientNet model that classifies lymphocytes as either normal or leukemia cells. In this framework, microscopic images of blood smears are first analyzed to detect various blood cell types. The system then isolates lymphocytes and uses a trained neural network model to determine whether they are healthy or cancerous. The experimental results show that this method increases detection accuracy while also reducing the need for extensive manual analysis. Overall, the system serves as an effective computer- aided diagnostic tool that can support medical professionals in screening and detecting leukemia more efficiently

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 Keywords

Leukemia Detection, Deep Learning, YOLO, EfficientNet, segment anything model ,Blood Cell Analysis, Medical Image Processing

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 Abstract

This study presents a comprehensive comparative analysis of preprocessing and segmentation techniques for automated bone fracture detection using X-ray images. The preprocessing stage includes noise removal methods such as median filtering, Gaussian filtering, and bilateral filtering, along with normalization techniques including Min-Max normalization, CLAHE, and Z-score normalization. The segmentation stage evaluates YOLOv7, YOLOv8, and YOLOv11 models for fracture localization. Experimental results demonstrate that median filtering provides superior edge preservation and noise reduction compared to Gaussian and bilateral filtering. Among normalization techniques, CLAHE and Min-Max normalization significantly improve contrast and model performance, while Z-score normalization shows limited enhancement. Furthermore, YOLOv11 achieves the highest detection accuracy and localization performance compared to YOLOv8 and YOLOv7. The proposed comparative analysis highlights the importance of selecting optimal preprocessing and segmentation techniques for improving fracture detection accuracy and reliability in medical imaging applications.

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 Keywords

Bone fracture detection, preprocessing, median filtering, CLAHE, normalization, YOLOv11, medical imaging.

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This research presents the development and implementation of a comprehensive Inventory and Assets Management System (IAMS) designed to address the critical challenges faced by organizations in tracking and managing their resources. Traditional manual methods using spreadsheets or paper-based systems are prone to errors, lack real-time visibility, and fail to provide adequate control over inventory levels. The proposed system leverages Python Flask framework for backend processing, MySQL for robust data storage, and modern web technologies including HTML5, CSS3, and Bootstrap for an intuitive user interface. The system implements role-based access control with three distinct user levels: Admin, Manager, and User, ensuring appropriate access permissions across the organization. Key features include real-time inventory tracking, automated low-stock alerts, asset lifecycle management, transaction recording, and comprehensive reporting capabilities. The system was tested with over 100 product entries and 50 asset records, demonstrating 98.5% accuracy in stock tracking and reducing manual inventory errors by 85%. User acceptance testing showed a 92% satisfaction rate among end-users, with significant improvements in operational efficiency. The results confirm that the proposed system effectively streamlines inventory and asset management processes, providing organizations with a cost-effective, scalable solution for resource tracking. Index Terms - Inventory Management, Asset Management, Web Application, Python Flask, MySQL, Role-Based Access Control, Stock Tracking

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 Keywords

Inventory Management, Asset Management, Web Application, Python Flask, MySQL, Role-Based Access Control, Stock Tracking

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

-- Rice is one of the most important food crops in the world and serves as a staple diet for millions of people, especially in countries such as India and China. However, rice production is frequently affected by several plant diseases including bacterial leaf blight, brown spot, leaf smut, and rice blast. These diseases significantly reduce crop yield and quality when they are not detected at an early stage. Therefore, timely identification of plant diseases is essential for effective crop management. This study presents an automated approach for detecting rice leaf diseases using a deep learning technique based on the MobileNet architecture. MobileNet is a lightweight convolutional neural network that is designed to perform efficient image classification while requiring fewer computational resources. The proposed model processes leaf images through preprocessing and feature extraction stages and classifies them into five categories, including healthy leaves and four disease classes. Compared with traditional deep learning models such as VGG and ResNet, MobileNet provides comparable classification accuracy while using fewer parameters and lower computational power. Experimental results show that the system can accurately detect different types of rice leaf diseases and can be implemented on mobile or embedded devices to assist farmers in real-time disease diagnosis

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 Keywords

Rice disease detection, Deep Learning, CNN, Mobile Net, Agriculture, Image Classification

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The paper aims to discuss the historicity and practice of Therukoothu, performed in Southern Andhra Pradesh and Northern Tamil Nadu regions, not just as an art form but as a cultural practice rooted in goddess worship. The paper elaborates on the association of Therukoothu with Draupadi Amman Worship and Draupadi cult, while examining how a character from the epic tale of Mahabharata has become a goddess with a cultic history. The paper further progresses to a critical analysis of the patronization and practice of Therukoothu during the 14th Century and how it was used as a political tool to create false consciousness within the communities belonging to the Draupadi worshipping cult.

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 Keywords

Therukoothu, Kattaikoothu, Draupadi Amman, Draupadi Cult, False Consciousness.

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Creative Commons Attribution 4.0 and The Open Definition