Volume 14 | Issue 2 |

 Abstract

Cardiovascular disorders pose a serious threat to public health due to their high mortality rate and late-stage diagnosis. Identifying heart disease at an early stage can significantly reduce complications and fatal outcomes. Recent advancements in machine learning have enabled automated analysis of clinical data to assist physicians in diagnosis. This study introduces an ensemble-based prediction model that evaluates cardiovascular risk using patient health parameters. The Cleveland Heart Disease dataset is employed for experimentation. After data preprocessing, balancing, and feature refinement, an ensemble model based on gradient boosting is developed. The proposed system achieves an overall prediction accuracy of 96%, demonstrating improved performance over conventional classifiers. The results confirm the effectiveness of ensemble learning for clinical decision support systems.

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 Keywords

Cardiovascular Risk Prediction, Ensemble Learning, XGBoost, Clinical Data Mining, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: Women have reproductive role. But basically they face deprivation from the aspect of gender discrimination in all the sphere as well as in the sphere of work force. This paper is basically focused on the position of women in work force in India and West Bengal. Women more participate in unorganised sector. The study focused on the rural and urban scenario of participation of women in work force in India and West Bengal. The study found that rural women are more active than urban women and they are more engaged in unorganised sector. To overcome the situation is need to make the social environment positive towards the span the area of workforce to women and remove the patriarchal barriers.

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 Keywords

gender discrimination, work force, women's participation, gender gap

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Abstract The exponential growth of digital communication networks and cloud-based infrastructures has significantly increased the exposure of critical systems to cyber threats. Modern networks generate massive volumes of heterogeneous traffic, including benign user activities and increasingly sophisticated malicious behaviors. Traditional network intrusion detection systems (IDS), which primarily rely on rule-based, signature-driven, or shallow machine learning approaches, struggle to cope with the scale, diversity, and dynamic nature of contemporary cyberattacks. In particular, advanced threats such as botnets, distributed denial-of-service (DDoS) attacks, phishing campaigns, malware propagation, and multi-step intrusions often evade detection by mimicking legitimate traffic or unfolding gradually over time. These challenges highlight the urgent need for intelligent, adaptive, and context-aware security mechanisms capable of learning complex temporal patterns from network data. Recent advancements in Generative Artificial Intelligence (AI) and deep learning architectures, especially Transformer models, have opened new possibilities for intelligent threat detection. Transformers, originally developed for natural language processing tasks, have demonstrated remarkable capability in modeling long-range dependencies using self-attention mechanisms. Unlike recurrent neural networks (RNNs) and long short-term memory (LSTM) models, which process sequences sequentially and suffer from limited memory and delayed response, Transformers analyze entire sequences simultaneously. This property makes them particularly suitable for network security applications, where understanding relationships between distant network events is essential for detecting evolving and multi-stage attacks.

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 Keywords

Keywords: Network Security, Intrusion Detection System (IDS), Generative Artificial Intelligence, Transformer Architecture, Self-Attention Mechanism, Sequential Network Modeling, Multi-Step Attack Detection, Botnet and Malware Detection, Explainable AI, Cyber Threat Intelligence

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Supervisory Control and Data Acquisition (SCADA) systems are essential for real-time monitoring and control of industrial processes. However, an excessive number of alarms can overwhelm operators, degrade system performance, and compromise safety and productivity. This paper presents a structured approach to optimizing SCADA alarm systems using the DMAIC (Define, Measure, Analyze, Improve, Control) framework of Lean Six Sigma. The goal is to reduce alarm frequency, improve system responsiveness, and enhance operational safety

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 Keywords

Optimization of SCADA Alarm Systems

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Literature has long functioned as a space where human emotions, cultural values, and social realities are expressed and preserved. Women's experiences, in particular, have found representation in literature through different forms and traditions across time. This paper examines the transformation of women's voices from collective moral expression in folk literature to personal and psychological articulation in confessional poetry. By comparing Silappathikaram, a classical Tamil folk epic, with selected poems of Kamala Das, a prominent confessional poet, the study explores how women's grief, suffering, and identity are expressed in distinct literary modes. While folk literature presents women's emotions within moral and social frameworks, confessional literature foregrounds individual truth and emotional honesty. The paper highlights how both traditions, despite their differences, serve as powerful modes of resistance against patriarchal structures. This comparative analysis reveals the continuity of women's struggle for voice and agency across historical and cultural contexts.

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 Keywords

Folk Literature, Confessional Poetry, Women's Voice, Grief, Identity, Resistance

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Importance of Yoga Practice for Mental Health Management in the Current Perspective – A Critical Study

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Importance of Yoga Practice for Mental Health Management in the Current Perspective – A Critical Study

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This paper examines Mikki Kendall's Hood Feminism: Notes from the Women That a Movement Forgot as a critique of widespread feminism and a call for a more comprehensive, equitable feminist movement. Through a close reading of Kendall's work, this paper argues that mainstream feminism has historically marginalized and erased the experiences of women of colour, poor women, and other vulnerable populations. By centering the voices of marginalized women, Kendall's Hood Feminism offers a necessary corrective to mainstream feminism and a vision of a more just and equitable feminist future.

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 Keywords

Mikki Kendall, Hood Feminism, intersectionality, mainstream feminism, feminism, women of color, marginalized voices, feminist critique, social justice

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The study aims to explore how artificial intelligence-based biometric systems enhance the security of financial transactions. The research focuses on identifying key determinants such as AI Algorithm Efficiency, Biometric Accuracy, System Integration and Usability, and Data Privacy and Encryption Measures, which significantly influence transaction security. Using a descriptive cross-sectional research design and a non-probabilistic sample of 200 respondents from financial service providers, the study employs frequency and descriptive analysis to identify major parameters, correlation and regression to measure relationships, and non-parametric tests to assess differences in perceptions among demographic groups. The findings are expected to provide valuable insights into how technological, operational, and privacy factors collectively contribute to secure financial systems. This research emphasizes the importance of integrating AI and biometric mechanisms to ensure trust, accuracy, and efficiency in modern financial authentication frameworks.

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 Keywords

AI-Enabled Biometric Authentication, Financial Transaction Security, Data Privacy, System Integration, Regression Analysis

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ABSTRACT: Rural Development is a multi-dimensional process which includes the development of socio-economic conditions of the people living in the rural areas, and ensures their participation in the process of development for complete utilization of physical and human resources for better living conditions . In order to provide the rural people with better scopes and prospects for economic development, increased participation of the people in the rural development programmes, decentralization of planning, progressive land reforms and greater access to credit are envisaged. This paper discusses about the people's participation in rural development activities. Key words- Activities, development, participation, people, rural.

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words- Activities, development, participation, people, rural.

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Creative Commons Attribution 4.0 and The Open Definition

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Flood susceptibility mapping is essential for disaster risk management, especially as climate change increases flooding. Recent studies show that combining machine learning (ML) with GIS greatly improves prediction accuracy. Interpretable ML models, along with socioeconomic data, help clearly identify vulnerable areas. For example, in Hefei, China, fine-scale mapping supported better local decision-making. Many modern approaches also use multidimensional assessments that integrate physical flood data with factors like population, infrastructure, and climate. This results in accurate, transparent, and community-focused risk evaluations that strengthen urban planning and disaster preparedness.

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 Keywords

Flood susceptibility mapping,Disaster risk management,Climate change,Machine learning (ML),Geographic Information Systems (GIS),Interpretable ML models.

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Creative Commons Attribution 4.0 and The Open Definition