Volume 13 | Issue 9 |

 Abstract

The emergence of face-swap-based deepfake videos poses a serious challenge to digital authenticity, facilitating misinformation, identity fraud, and social manipulation. The goal of this research is to create an AI/ML-powered solution to identify such deepfakes with precision, ensuring media integrity. The system blends Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) to learn spatial-temporal features, detecting subtle inconsistencies in facial expressions, illumination, and blending artifacts typical of manipulated videos. Besides an LSTM network is incorporated to process frame sequences, enhancing detection performance by locating temporal inconsistencies in facial motions. The Prototype is instructed on various, widespread datasets specifically Face Forensics++ to corroborate robustness in defiance disparate resolutions and deepfake practices . Experimental outcomes show an over 95 percent accuracy that excels conventional CNN-based detectors and displays resilience against more recent face-swapping procedures. Sophisticated methods such as multimodal analysis, audio-video synchronization verification, and emotion consistency identification are combined for more accurate detection. A light-weight, edge-friendly variant with model compression, adaptive dataset growth, and adversarial training ensures real-time operation and robustness against new deepfake techniques.

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 Keywords

--Deepfake detection, face-swap videos, Face Forensics++, Facial expression analysis, CNN-based Detectors, Vision transformers (ViTs).

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Kidney disease poses a significant healthcare challenge worldwide, with millions of patients at risk of chronic complications and kidney failure. Early and accurate detection is essential for improving survival rates and reducing treatment costs. However, conventional diagnostic techniques often rely heavily on manual interpretation, which can lead to variability and delays in diagnosis. To address these limitations, this project introduces a deep learning-based system for automated kidney disease classification. The work integrates advanced workflow management tools--Data Version Control (DVC) for dataset tracking and reproducibility, and MLflow for systematic experiment management, hyperparameter tuning, and model deployment. This integration ensures that the proposed framework is not only accurate but also scalable and transparent, making it suitable for real-world healthcare applications. The core methodology employs a convolutional neural network (CNN) trained on medical imaging data to distinguish between healthy and diseased kidney conditions. The pipeline includes automated data preprocessing, feature extraction, training, validation, and deployment, all while maintaining consistent version control. Experimental results indicate that the system achieves high classification accuracy with reliable performance across multiple test scenarios. Beyond kidney disease detection, this research highlights the potential of combining deep learning models with workflow management tools to create reproducible, maintainable, and adaptable solutions for a wide range of medical imaging challenges.

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 Keywords

Kidney Disease Classification, Deep Learning, Convolutional Neural Network (CNN), Medical Imaging, Data Version Control (DVC), MLflow, Experiment Tracking, Model Deployment, Reproducibility, Healthcare AI, Automated Diagnosis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Snakebite envenomation remains a major health challenge in rural and tribal areas of India. However, beyond its biomedical dimension, snakebite is deeply rooted in cultural beliefs, indigenous knowledge systems, and notions of fate. In many rural areas, health centers are miles away and treatment is getting delayed due to bad roads, limited access to effective antivenom therapy including exorcists leads fatality. This paper explores how tribal communities perceive and respond to snakebites, analyzing the traditional healing methods, mythologies associated with snakes, and the fatalistic attitudes toward recovery or death. Drawing from ethnographic studies and field reports, the paper highlights the critical intersection of health, culture, and spirituality, advocating for culturally sensitive health interventions.

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 Keywords

Snakebite envenomation, Exorcists, Antivenom, Traditional healing, Tribal

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An infestation of stored grains is a very important issue because different stages of the life cycle of insects damage the economy and lower the quality of food grains and food products. The use of chemical pesticides against insect pests of stored food has adverse effects on both health and the environment. These issues forced researchers to create substitute techniques with less negative impacts. Since the plant-based biopesticides are safe for humans and other living things, environmentally friendly, biodegradable, and have a narrow target spectrum. Essential oils and plant-based extracts have been very useful in controlling insects that display a range of insecticidal mechanism. Due to their distinct qualities, they can also serve as viable substitutes to protect grains from pest infestations (repellent, deterrent, and insecticidal). Among the stored grain pests Sitophilus oryzae (Rice weevil), Acanthoscelides obtectus (Bean weevil) and Tribolium castaneum (Red flour beetle) are considered as destructive pests in India. So, this review focused on the insect repulsive and insecticidal power of Ruta chalepensis (Fringed rue), Lantana camara (Lantana) and Ocimum basilicum (Basil) essential oil (EO) the ultimate is to develop a biological and ecological control strategy against pests.

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 Keywords

Essential oil (EO), Sitophilus oryzae, Acanthoscelides obtectus, Tribolium castaneum, Ruta chalepensis, Lantana camara, Ocimum basilicum.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Human society is essentially anchored in the concept of law and legal responsibility. It serves as the cornerstone controlling our interactions and preserving justice and order. While legal responsibility relates to the obligation to operate inside the boundaries of the law and to be accountable for one's actions, the law creates a framework of rules and guidelines that both people and institutions are required to follow. Law's basic goal is to build a system that supports fairness, equality, and protection of personal liberties. It provides a basis for suitable behaviour and a way to resolve problems and arguments. Without laws, society would collapse and there would be no way to control human behaviour. The legislation is a major instrument for social transformation, not only a set of guidelines and rules. Legal systems have been absolutely important historically in forming societies, preserving peace, and defending rights. Particularly for underprivileged and marginalised groups, the law is absolutely important in modern democratic systems in promoting social justice and empowerment. Fundamentally, social justice advocates the equitable distribution of resources, opportunities, and benefits inside a society; empowerment, on the other hand, emphasises on arming people and groups to exercise their rights and participate completely in social, political, and financial spheres. Law combined with these values forms the basis of an equitable and inclusive society. Many times, the idea of justice is associated with the belief that it entails within the legal framework equal rights, access, and fair treatment. A welfare state cannot be without social justice since it is fundamental. The several clauses of the Indian Constitution really reflect the idea of social justice. Terms like "Socialist," "Social and Economic Justice," and "Equality" in the Preamble of the Constitution suggest that the state will be very important in the welfare of its people and aim to produce an egalitarian society. Furthermore, a particular part of the Directive Principles of State Policy addresses the welfare responsibilities of the government, so delineating the guidelines for excellent government meant to advance the welfare of the people. Through Public Interest Litigations (PILs), historic decisions, and creative legal interpretation, the court has greatly helped social justice to develop. Law has promise, though, in guaranteeing social justice, not without challenges. Many times, ignorance of legal issues, financial constraints, complex processes, and society prejudices limits access to justice, so making empowerment an unmet need for many people. The aim of this research article is to look at how law might help to create social justice and empowerment. It will examine the contributions made by legislation and the court in this field and highlight the difficulties still standing in order to realise these objectives.

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LAW AS A TOOL FOR SOCIAL JUSTICE AND EMPOWERMENT

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 Abstract

This paper presents the Hand Gesture Recognition Using OpenCV And Python (HGRUOP), Hand gesture recognition (HGR) is an emerging field in Human-Computer Interaction (HCI) that enables users to communicate with machines through intuitive, touch-free gestures. This project presents a real-time hand gesture recognition system using OpenCV and Python stands [1] the system leverages computer vision techniques such as background subtraction, contour detection, convex hull, and feature extraction to identify and classify gestures made by the human hand. By capturing live video feed through a webcam, the program preprocesses each frame, segments the hand region, and detects finger positions to interpret different gestures. [2] personalized recommendations, and automating routine tasks. These recognized gestures can be mapped to specific commands, enabling applications in virtual mouse control, gaming, robotics, and sign language interpretation. The proposed approach emphasizes accuracy, low computational cost, and ease of implementation, making it suitable for real-time applications. [3](keeping the conversation coherent). Overall, the system provides a step toward creating more natural and efficient ways of human-computer interaction (HCI)without the need for additional hardware. This project demonstrates how OpenCV, combined with Python's simplicity and flexibility, can be effectively used to build vision-based interaction systems. The results highlight the potential of gesture recognition technology in bridging the gap between humans and machines, paving the way for innovative solutions in assistive technology, automation, and immersive user experiences.

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 Keywords

Hand Gesture Recognition, Human-Computer Interaction (HCI), OpenCV, Python, Computer Vision (CV), Image Processing (IP), Real-Time Recognition (RTR), Contour Detection (CD), Convex Hull (CH), Feature Extraction (FE), Sign Language Recognition (SLR), Virtual Mouse Control (VMC), Gesture-Based Interfaces (GBI), Machine Vision (MV), Assistive Technology (AT), Pattern Recognition (PR).

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Alzheimer's disease (AD) is a progressive neurodegenerative illness that significantly impairs cognitive function, highlighting the need for early and accurate detection to enable timely intervention. Although a lot of research has concentrated on neuroimaging and biomarker-based diagnosis, these methods can be costly and less accessible in many clinical settings. Recent advances in machine learning (ML) have created new opportunities for leveraging clinical assessment data such as cognitive test scores, demographic information, and behavioral measures to detect AD in its early stages. This review synthesizes current developments in ML methodologies applied to noninvasive clinical data for Alzheimer's detection. We discuss a range of both supervised and unsupervised learning models, feature selection strategies, and evaluation metrics used in recent studies. The findings demonstrate that machine learning models trained on clinical and cognitive assessment data can achieve high accuracy in distinguishing early Alzheimer's from healthy aging, presenting a scalable and cost-effective alternative to imaging-based approaches. Challenges associated with data heterogeneity, model generalizability, and interpretability are also addressed, along with future directions for integrating ML into routine clinical screening for Alzheimer's disease.

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 Keywords

Machine learning, Alzheimer disease, Cognitive assessment test, mild cognitive impairment, Datasets, Transfer learning, Evaluation metrics

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 Abstract

In Ayurveda, the term Aashaya means spaces or places or occupancy. This place is occupied by essential ingredients needed for body functioning and also by waste materials which need to be excreted throughout the body. Aacharyas have noted seven Aashayas in the male body, and eight in the female. Vatashaya is the site where Vata is located and its natural functions are exhibited. Vata is considered as leader of three Doshas. It is responsible for all movements in the body. Vata gets aggravated at its own site by its various gunas and produces various diseases. In Ayurvedic classics there is no specific description that which organ is Vatashaya. To cure the diseases, it is necessary to understand the sites of Vata in body. In the numbering of Aashayas, their structural presentation and specific study is not sufficient in the Ayurvedic texts. Acharya Sharangdhar tries to clear that Vatashaya is between Agnashaya and Malashaya. According to Acharya Charaka the specific site of Vata is Pakvashaya. Anatomically the large intestine can be considered as the place of Vatashaya.

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 Keywords

Aashaya, Vata, Vatashaya,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Grameen Evam Shahri kShetra kiBalikayon Ki Samajik Swatantrata Ka Unki Shaikshik Akanksha Evam Shaikshik Uplabdhi Par Parne Wale Prabhav Ka Tulnatmak Adhyayan

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Grameen Evam Shahri kShetra kiBalikayon Ki Samajik Swatantrata Ka Unki Shaikshik Akanksha Evam Shaikshik Uplabdhi Par Parne Wale Prabhav Ka Tulnatmak Adhyayan

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Education 5.0 marks a significant transformation in the idea and methods of learning, with artificial intelligence (AI) increasingly occupying a central role. Discussions on a global scale often overlook the capacity of indigenous traditions to guide the ethical, inclusive, and holistic implementation of AI. This chapter looks at how Sanskrit educational ideas and AI-driven innovation are coming together, making India's cultural heritage the conceptual framework for Education 5.0. Drawing from the Upaniṣads, Bhagavad Gītā, Chanakya Nīti, Hitopadeśa, and Vedic texts, it underscores timeless principles such as self-realization (aham brahmāsmi), unity (tattvamasi), truth (satyameva jayate), and ethical pathways (śivāste panthānaḥ), demonstrating their congruence with AI's potential for personalization, accessibility, and global collaboration. Insights into parenting (lālayet pañca varṣāṇi), guru-centric paradigms (gurur brahmā), and cultural metaphors such as rivers are re-evaluated through the lenses of adaptive learning, collaborative pedagogy, and socioemotional intelligence. The chapter argues for an Education 5.0 ecosystem that combines digital innovation with Sanskrit wisdom to create a system that is technologically advanced, based on values, fair, and profoundly humanistic.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Education 5.0, Artificial Intelligence, Sanskrit Wisdom, Indian Knowledge Systems, Ethical AI

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Creative Commons Attribution 4.0 and The Open Definition