Volume 13 | Issue 6 |

 Abstract

The ever-evolving threat landscape in cyberspace necessitates advanced and adaptive security mechanisms. Traditional Intrusion Detection Systems (IDS) often struggle to detect novel and stealthy attacks due to their dependence on static rules or labeled data. This paper explores the integration of Generative Adversarial Networks (GANs) into autonomous IDS frameworks to enhance their ability to detect complex and unknown cyber threats. GANs, through their adversarial learning paradigm, enable the simulation of sophisticated attack patterns, which can be used to augment training datasets and improve the robustness of detection models. We propose a novel adversarial intelligence-driven IDS that dynamically evolves by learning from synthetic attack behaviors generated by the GAN. The experimental results demonstrate significant improvements in anomaly detection accuracy and false positive rate reduction, showcasing the potential of GANs to revolutionize intelligent threat detection. This approach provides a scalable and proactive defense mechanism, aligning with the future of autonomous cybersecurity systems.

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 Keywords

Adversarial Intelligence, Generative Adversarial Networks (GANs), Intrusion Detection System (IDS), Cybersecurity, Autonomous Systems, Anomaly Detection, Threat Simulation, Deep Learning

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The objective of this paper is to review articles related to psychological and psychiatric disability that have been published in the Indian Journal also added up some more relevant literature in the area of mental disability of Indian psychiatric patients. Finally the article ends up with discussion related to challenges associated with mental disability, persons with Disability Act and future directions in the area of psychological and psychiatric disability.

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IDEAS, Mental illness, Disability

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Concrete is a most common material which is commonly used in all types of civil engineering works. The demand for aggregates is more in current situation and on the other side the industries are producing a large number of tanning wastes from the leather industries cause of this dumping and contamination of these tannery wastes it affects the environment and causes some disposal problems. So, the experimental studies carried on the tannery solid wastes to introduce it with concrete and make it as a partial replacement in reinforced concrete. In this study, by partially replacing of fine aggregate with tannery sludge with some limited percentage 0%, 10% ,20%, 30% in volume. The concrete grade M25 selected and specimens are casted and tested for both grades. The size of concrete cube is 150mm by 150mm by 150mm, the size of concrete cylinder is 300mm height ,150mm diameter, the size of concrete prism is 150mm by 150mm by 500mm.. So, these specimens are been casted and cured for 28 days and these specimens are tested to found the mechanical properties by compressive strength test for cube is done on 7th day, 14th day and 28th day respectively, split tensile test for cylinder is done on 28th day, flexural strength test for prism is done on 28th day, flexural strength test for RC Beam is done on 28th day respectively. And to compare the results with the normal conventional concrete with tannery sludge replaced concrete where the specimens undergo heavy loading conditions. So as a result, the compressive strength and tensile strength of 30% replacement of tannery sludge is a bit higher than the normal conventional concrete.

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An Experimental Investigation On M25 Grade Of Concrete By Partial Replacement Of tannery Sludge With Fine Aggregate

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Stock markets are more sensitive to market information whether it is positive or negative it will significantly impact on stock price. The aim of this paper is to analyze stock price on corporate action that is particularly dividend announcement that effect on stock price. Thus, this study attempts to analyse impact on dividend announcement on stock price of Reliance Industries Limited (RIL). For the purpose of study secondary data was collected from Bombay Stock Exchange ( BSE). Data are collected for 30 prior to dividend announcement and 30 days after dividend announcement and shorter window of 5 days are also analyzed. The study employs various tools such as Event study Methodology, Market Model and t test. This research paper concludes that the stock market reacts to dividend announcement in the post announcement period. Index Term - Dividend, Event Study, Market Model, Abnormal Return

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Dividend, Event Study, Market Model, Abnormal Return

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 Abstract

Nasha mukti evam yoga

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Nasha mukti evam yoga

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: Structured Teaching Programme, Computer Vision Syndrome, Nursing Students, Knowledge Assessment, Chi-square Test

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This study investigates the relationship between mathematics achievement and the family environment of secondary school students, as well as the influence of varying levels of family environment on mathematics achievement. The primary aim of the research is to examine the relationship between students' performance in mathematics and their family environment and to compare the mathematics achievement of students across different levels of family environment to test the stated hypotheses. The Family Environment Scale developed by Harpreet Bhatia and N.K. Chadha was used to assess the family environment of students. Mathematics achievement was measured using students' academic scores in mathematics, as recorded in school records. The sample comprised 210 Class IX students selected from a varied schools (government, private aided and unaided) in Bengaluru Urban District. Data analysis was conducted using Karl Pearson's Product Moment Coefficient of Correlation and one-way ANOVA (F-test), followed by Scheffe's Post Hoc Analysis where the F-value was found to be significant. The statistical analysis was carried out using SPSS software and MS Excel, with significance levels set at 0.05 and 0.01. The findings of the study provide valuable knowledge into how the family environment influences students' achievement in mathematics. The correlation analysis revealed a significant positive relationship between mathematics achievement and the family environment. Additionally, the ANOVA results indicated a significant difference in mathematics achievement among students with varying levels of family environment. Students from a high-quality family environment scored significantly higher in mathematics compared to those from moderate or low family environments. This may be attributed to the fact that a conducive and supportive family environment plays a crucial role in enhancing students' performance in mathematics.

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Family Environment, Mathematics, Achievement, Secondary School Students

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Fish farming is one of the fastest growing manufacturing businesses in many parts of the world as the demand for fish and processed fish feed is increasing day by day. Fish farming is also known as aquaculture, breeding, rearing, collecting fish and many other organisms. It is also defined as a breeding species that thrives in an aquatic environment under controlled conditions. In India the fish farms are still controlled and managed in the traditional way where the fish feeding and management of water is still handled manually. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. This proposed system design will remotely monitor the pH level, turbidity and temperature of water. Sensors are installed in this system to receive data. This IoT based system also consist of controlled feeding system and an aerator for management of water.

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Fish farm, Internet of Things,Real time monitoring and management

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This study investigates the correlation between fintech adoption and sustainable financial behaviors in Odisha, India. Driven by rapid fintech growth, marked by widespread digital payments, mobile banking, and online investment platforms, the research explores if fintech usage influences individuals' engagement in environmentally and socially responsible financial practices. Data from 470 respondents, stratified by demographics and geography, were analyzed using frequency, percentage, and Pearson correlation techniques. Results show a strong positive correlation (r=0.61, p<0.01) between fintech adoption and sustainable financial behaviors, notably in areas like ESG investments, ethical banking, and paperless transactions. Fintech adoption is particularly prevalent among educated, urban youth, aligning with a higher propensity for sustainability-conscious financial decisions. However, moderate engagement with digital lending and ESG investment indicates potential barriers, highlighting a need for enhanced educational initiatives and simplified fintech solutions. The study underscores fintech's critical role in promoting sustainable finance, bridging knowledge-action gaps through targeted interventions. These findings provide valuable insights for policymakers, fintech enterprises, and educational institutions aiming to leverage fintech as a strategic tool for achieving sustainable economic development goals in Odisha and similar regions, contributing significantly to existing fintech and sustainable finance literature.

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 Keywords

ESG Investments, Fintech Adoption, Financial Behavior, Odisha, Sustainable Finance

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The rapid advancement of deep learning technologies has led to a surge in deepfake media--synthetically generated images and videos that closely mimic real human appearances and expressions. These forgeries pose a significant threat to digital security, privacy, and the credibility of online content. This paper explores a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for feature extraction with Support Vector Machines (SVMs) for classification to enhance the accuracy and robustness of deepfake detection. CNNs are leveraged to automatically learn and extract high-dimensional spatial features from facial images and video frames, while SVMs provide a powerful mechanism for discriminating between real and manipulated media based on the extracted features. Experimental results across multiple deepfake datasets demonstrate that the hybrid CNN-SVM model outperforms traditional standalone models in terms of precision, recall, and F1-score, showcasing its effectiveness for real-world deployment. The proposed approach offers a promising direction for combating the proliferation of deepfakes through intelligent fusion of deep and classical machine learning techniques.

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 Keywords

Deepfake Detection, Hybrid Deep Learning, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Feature Extraction, Image Forensics, Multimedia Security, Fake Media Identification.

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Creative Commons Attribution 4.0 and The Open Definition