Volume 13 | Issue 5 |

 Abstract

The project Image Processing for Skin Lesion is to create an AI-powered system for classifying skin lesions using Convolutional Neural Networks (CNNs) that could potentially help in the early diagnosis of skin diseases, including melanoma which is often fatal. The model is trained on the HAM10000 dataset and classifies dermatoscopic images into the categories Melanocytic Nevi, Melanoma, and Basal Cell Carcinoma. To increase the value of diagnosis, the system also has a severity scoring module that captures and predicts possible lesion progression based on stratified risk features. Lesions are guided by the camera in real time, which optimizes image focus and quality, enhancing the feedback that users receive during the procedure. The system also enables users to capture progression and comparison images of skin lesions over time, thus facilitating longitudinal lesion tracking through progression charts. With users, the voice-operated chatbot communicates naturally, providing health advice and safety tips on various skin problems and interacting with them interactively using instructions. The incorporation of real-time deep learning into health interaction makes this system become very user-friendly while providing effortless assessment of skin health.

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 Keywords

Skin Lesion Classification, Deep Learning, Convolutional Neural Networks (CNNs), HAM10000 Dataset, Severity Scoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Elections play a pivotal role in shaping the political and social landscape of India. While they are essential for democratic governance, elections often give rise to political polarization, communal tensions, and misinformation, thereby influencing peace and social harmony. This study examines how political campaigns, party rhetoric, and the role of media and technology contribute to instability during elections. It also explores the effectiveness of institutional mechanisms, including law enforcement and election monitoring, in maintaining peace. Through an analysis of political, social, and institutional factors, the report aims to provide a comprehensive understanding of the challenges and propose strategies for fostering more peaceful electoral processes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Elections, Political Polarization, Communal Tensions, Misinformation, Institutional Response

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Deepfake technology enables the creation of highly realistic manipulated media, posing serious threats to security, privacy, and trust in digital communications. This paper proposes a real-time deepfake detection system using advanced machine learning techniques. The system identifies subtle visual and audio anomalies in synthetic media by combining ResNext for spatial feature extraction and LSTM networks for temporal analysis. Trained on a diverse dataset, the model achieves high accuracy and generalization. The approach aims to support global efforts to combat deepfake misuse and protect digital integrity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deepfake Detection, Machine Learning, Deep Learning, ResNext, LSTM, Synthetic Media, Real-Time Detection, Audio-Visual Analysis, Digital Integrity, Media Authentication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract This research paper analyses the complex link between women and nationhood via the works of Sahar Khalifeh, a notable Palestinian writer. Khalifeh's tales illustrate the twin fight for emancipation encountered by women under national oppression and patriarchal structures. The introduction delineates a framework for comprehending Khalifeh's creative achievements, highlighting her dual function as a writer and public intellectual who questions conventional historical narratives that marginalize women's experiences. Khalifeh's books thematically examine the stark reality of women's lives, demonstrating how their identities are influenced by social expectations, cultural heritage, and the effects of war and relocation. The study examines how Khalifeh's female protagonists man oeuvre through restrictive frameworks, highlighting their resilience and agency in the face of adversity. This study examines key works, including "Wild Thorns," "The Inheritance," and "The Last Woman," highlighting Khalifeh's substantial contributions to Palestinian literature and feminist debate, while also contesting Western preconceptions of women's role in Arab society. The research positions Khalifeh's work as an essential analysis of the interconnections among gender, identity, and nationality, elucidating the intricacies of women's experiences amid personal and social suffering.

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 Keywords

(identity, belongs, social suffering, liberation, Nationalism and Resistance)

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ABSTRACT : This study explores the role of Green Human Resource Management (Green HRM) practices in promoting organizational sustainability and long-term success at Toshiba JSW Power Systems Private Limited. The factors considered in this study are employee awareness, employee motivation and engagement, sustainable development and organizational success. Descriptive research design was employedin. The sample size is taken as 155 for this study and it is determined through Morgan table for sample size. Since the data deviates from normal distribution, non-parametric tools are used. The statistical tools used in this study are Mann Whitney U Test, Kruskal Wallis H Test, Spearman's rank correlation and Chi Square Test. The findings of this study confirmed a positive relationship between Green HRM practices and organizational sustainability and success. The study emphasizes the need for differentiated approaches in educating employees about sustainability. By integrating Green HRM into organizational culture through awareness, inclusive engagement, and supportive leadership the organization can enhance its sustainability footprint, drive innovation, employee satisfaction, and operational excellence.

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 Keywords

Keywords - Green HRM, Organizational sustainability, employee awareness, employee motivation and engagement, sustainable development and organization success.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study examines the impact of Cash Reserve Ratio (CRR) changes on the NIFTY 50 index using time-series econometric techniques. Monthly data from April 2019 to March 2023, sourced from NSE and RBI, was analyzed using the Vector Autoregression (VAR) model, with stationarity confirmed through the Augmented Dickey-Fuller (ADF) test. The results indicate that CRR Granger-causes NIFTY 50, and an increase in CRR leads to a short-term decline in stock prices, confirming that monetary tightening reduces market liquidity. Variance decomposition and impulse response functions show that CRR's influence grows over time. The Lagrange Multiplier (LM) test confirmed no serial correlation, validating model robustness. These findings highlight the need for policy adjustments that balance financial stability and market growth, with CRR serving as a leading indicator for investors.

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 Keywords

CRR, NIFTY 50, Monetary Policy, VAR Model, Granger Causality, Stock Market.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Denotified Communities (DNC) of Tamil Nadu has experienced a profound shift from historical stigma to gradual empowerment. Branded as "criminal tribes" under British colonial rule, these communities faced decades of systemic discrimination, legal persecution, and social exclusion. Their denotification in 1952 marked the beginning of their long struggle for rehabilitation and recognition. A significant milestone was the establishment of the DNC Welfare Board in the 1970s, which addressed grievances, promoted education, and facilitated political representation. Despite these efforts, challenges persist, including socio-economic disparities, educational barriers, and continued marginalization.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Denotified Communities, Tamil Nadu, Criminal Tribes Act, Social Stigma, Rehabilitation, Social Justice, DNC Welfare Board

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Natural products and traditional medicine have long been integral to healthcare systems worldwide, offering both preventive and therapeutic benefits. This review explores the historical and contemporary roles of natural products, derived from plants, marine organisms, animals, and microorganisms, in modern medicine. Traditional medical systems such as Ayurveda, Traditional Chinese Medicine (TCM), and Indigenous practices have significantly contributed to modern pharmacology, particularly in the discovery of bioactive compounds like alkaloids, flavonoids, terpenes, and polyphenols. Case studies of drugs like paclitaxel, artemisinin, and penicillin illustrate the successful transition of traditional remedies into mainstream medicine. The paper discusses the biological activities of natural products, including their antioxidant, anti-inflammatory, anticancer, and antimicrobial properties, and highlights their synergy with synthetic drugs. It also examines the integration of traditional and modern medicine in the treatment of chronic diseases and the global efforts, led by organizations like the WHO, to standardize and regulate traditional medicine practices. Despite challenges such as standardization, scientific validation, and ethical concerns, the advancement of technologies like genomics and AI holds promise for the future of natural product discovery and integrative healthcare. This review underscores the importance of collaboration between traditional practitioners and modern healthcare professionals to enhance patient care and global health outcomes.

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 Keywords

Natural products, Traditional medicine, Drug discovery, Traditional Chinese Medicine, Ayurveda.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study focus on the effectiveness of grievance redressal mechanism at the workplace. In this project broad research has been provided on how well the organization is handling the grievances. The effectiveness of employee grievance handling system is important for maintaining a harmonious and productive work environment. Effective grievance handling is an essential part of cultivating good employee relationships. The primary objective is to examine the effectiveness of grievance redressal mechanism in the organization. The secondary objective is to understand the general factors influencing the employee's grievances and know the satisfaction level of employees towards their grievance handling system. The study used simple random sampling technique. Primary data collection method were used. By using SPSS Non parametric tools such as ( U TEST, H TEST, CORRELATION, CHI SQUARE TEST AND WEIGHTED AVERAGE) were used for analysis of the data. The results on the analysis is the grievance redressal mechanism helps employees in providing a positive work environment

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 Keywords

Grievances, effectiveness, Employees

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The swift expansion of the Web and the Internet of Things, or IoT, has coincided with heightened apprehensions over cybersecurity. Artificial Intelligence (AI) currently serves a pivotal function in cybersecurity by creating advanced algorithms to safeguard networks & systems, including IoT gadgets. Cybercriminals are using AI by employing adversarial approaches to execute more sophisticated cybersecurity assaults. This project consolidates knowledge from numerous studies into IoT, AI, and AI-facilitated cyberattacks, aiming to forecast prospective attacks, detect weaknesses in IoT devices, and alert consumers through machine learning models. It amalgamates several network-connected devices to establish intelligent, adaptive services aimed at safeguarding user privacy against risks including eavesdropping, blocking, DoS assaults, and spoofing. Moreover, it examines how the integration of IoT systems with AI might improve device security and facilitate machine learning-driven access management. This project involves the creation of an advanced video surveillance system that can monitor and analyse real-time video feeds to identify and recognise objects, actions, and abnormalities. Utilising contemporary deep learning architectures like Convolutional Neural Networks (CNNs), the system executes object detection, monitoring, activity recognition, and anomalous event detection through the extraction of spatiotemporal data. This research tackles the difficulties of IoT security and seeks to enhance device safety through deep learning and machine learning methodologies.

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 Keywords

- Internet of Things (IoT) Security, Cybersecurity, Artificial Intellect (AI), Machine Understanding (ML), Deep Learning, ML, Adversarial AI, IoT Vulnerability Assessment, Threat Forecasting, Intelligent Surveillance, Object Recognition, Access Management, Privacy Safeguarding.

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Creative Commons Attribution 4.0 and The Open Definition