Karthik. R, Dr. Thangavel Murugan

Vedic Astrology; Venus (Shukra Graha); Marital Life; Marriage Compatibility; Relationship Harmony; Love and Romance; Afflicted Venus; Navamsa Chart; Emotional Compatibility and Conjugal Happiness.

This article examines the role of Venus (Shukra Graha) in marital life according to Vedic Astrology. Venus is considered the planet of love, marriage, romance, harmony, beauty, and emotional fulfilment. The study explains how the placement and strength of Venus in different zodiac signs and houses influence relationships, compatibility, intimacy, and marital happiness. It also discusses the effects of afflicted Venus, including emotional conflicts, delayed marriage, and relationship instability. The journal further highlights spiritual aspects of marriage, compatibility analysis, and traditional Vedic remedies for strengthening Venus. Overall, the study emphasizes that a balanced Venus supports peaceful, affectionate, and harmonious married life.

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[5] Keeranur Natarajan, Jathaka Alangaram. Chennai, India: Narmadha Publications, 2007.

Karthik. R, Dr. Thangavel Murugan

Rahu–Ketu Dosha; Vedic Astrology; Marital Life, Marriage Astrology; Rahu in 7th House; Ketu in 7th House; Delayed Marriage; Love Marriage; Intercaste Marriage & Venus Affliction.

Rahu and Ketu occupy a unique and mysterious place in Vedic astrology. Unlike the visible planets, these lunar nodes are shadow planets that symbolize karmic forces, spiritual evolution, illusion, detachment, obsession, and transformation. In marital astrology, the influence of Rahu and Ketu becomes highly significant because marriage itself is considered a karmic institution in Sanatana Dharma. The placement of these nodes in the horoscope can create disturbances, misunderstandings, emotional instability, unconventional unions, delayed marriages, separations, or deep spiritual partnerships depending upon their dignity, house placement, conjunctions, aspects, and planetary strengths.This journal explores the concept of Rahu–Ketu Dosha in relation to marriage life according to classical Vedic astrology principles. It discusses the mythology of Rahu and Ketu, their astrological significance, effects on the 7th house, Venus, Jupiter, Moon, Navamsa chart, compatibility analysis, karmic relationships, psychological implications, remedies, and spiritual perspectives. The study also emphasizes the importance of holistic chart interpretation rather than fear-based predictions.

[1] Parāśara Maharshi, Brihat Parashara Hora Shastra, translated and edited by R. Santhanam, 2 vols. New Delhi, India: Ranjan Publications, 2008.
[2] Mantreswara, Phaladeepika: A Unique Classic on Hindu Predictive Astrology, translated and commented by G. S. Kapoor. New Delhi, India: Ranjan Publications, 2014.
[3] Vaidyanatha Dikshita, Jataka Parijata, translated and annotated by V. Subrahmanya Sastri, 3 vols. New Delhi, India: Ranjan Publications, 2004.
[4] Varahamihira, Brihat Jataka: English Translation with Original Slokas in Devanagari, translated by B. Suryanarain Rao, New Delhi, India: Motilal Banarsidass Publishers, 1986.

Karthik. R, Dr. Thangavel Murugan

Mars Dosha; Manglik Dosha; Kuja Dosha, Marital Life; Marriage Compatibility; Horoscope Matching; Planet Mars; Marital Harmony & Delay in Marriage

Mars Dosha, commonly known as Manglik Dosha or Kuja Dosha, occupies an important place in the study of Vedic Astrology. It is considered one of the most discussed astrological combinations affecting marriage, relationships, domestic harmony, and emotional compatibility. According to classical Vedic texts, the placement of Mars in specific houses of the natal horoscope creates energetic imbalances that may influence marital life. The aggressive, fiery, and forceful nature of Mars can create tension, impulsiveness, misunderstandings, conflicts, and delays in marriage if not harmoniously balanced.This journal explores the concept of Mars Dosha in detail from the perspective of Vedic Astrology. It discusses the astronomical and astrological significance of Mars, the houses responsible for the formation of Mars Dosha, the classical interpretations, psychological implications, effects on marital harmony, cancellation conditions, remedies, and modern interpretations. The study also highlights how Mars Dosha should not be interpreted with fear but with astrological wisdom, balance, and proper guidance.

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[3] Vaidyanatha Dikshita, Jataka Parijata, translated and annotated by V. Subrahmanya Sastri, 3 vols. New Delhi, India: Ranjan Publications, 2004.
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[8] S. M. Lundberg and S. I. Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, Vol. 30, 2017, pp. 4765–4774.
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[12] G. Kim, H. Lim, and Y. Lee, “An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic,” Electronics, Vol. 9, No. 10, 2020, p. 1583.
[13] K.K. Rakesh, Dr. A. S. Aneeshkumar, “Optimization of fuzzy logic-based Genetic Algorithm Technique in wireless sensor network Protocols”, International Journal of Intelligent Systems and Applications in Engineering, Vol.12(14), 2024, pp. 548–556.
[14] X. Guo, “Deep Learning-Based Network Intrusion Detection Using NSL-KDD Dataset,” Proceedings of the International Conference on Computing and Data Science, 2021, pp. 112–118.
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Kolle Likhitha, V. Vijayalakshmi

Intrusion Detection System; Deep Learning; NSL-KDD; Network Security; Multi-Class Classification.

The rapid expansion of networked computing systems has significantly increased the exposure of digital infrastructures to sophisticated cyber threats. Traditional intrusion detection systems, primarily based on signature matching techniques, are often ineffective in identifying emerging and unknown attack patterns. These limitations necessitate the adoption of intelligent and adaptive approaches capable of analyzing complex network behaviors. CyberShield AI is proposed as a deep learning–based intrusion detection framework designed to enhance the detection of both known and novel cyber-attacks through behavioral analysis of network traffic. The system utilizes the NSL-KDD dataset, which consists of network connection records described by forty-one features, including basic attributes, content-based features, time-dependent traffic characteristics, and host-based statistical measures. Each data instance is categorized as either normal or belonging to one of four major attack classes: Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). These diverse feature representations enable the model to learn complex intrusion patterns effectively. CyberShield AI employs a deep neural network architecture incorporating multiple fully connected layers, along with techniques such as batch normalization, dropout regularization, and Rectified Linear Unit (ReLU) activation functions to improve learning efficiency and prevent overfitting. The trained model demonstrates high accuracy and robustness in classifying network traffic across different attack categories. Furthermore, the system is integrated into a web-based dashboard that provides real-time intrusion detection capabilities for security analysts. It allows users to input network traffic data, obtain classification results with confidence scores, and receive actionable insights for threat mitigation. Additionally, a continuous monitoring and update mechanism ensures that the system adapts to evolving network conditions, maintaining consistent performance and reliability.

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Kamaluri Mubarak, V. Vijayalakshmi, Dr. S. Usharani

Cybersecurity; Machine Learning; Intrusion Detection; Random Forest; Django; Threat Detection; Network Security.

The rapid expansion of digital infrastructure across industries has significantly increased the vulnerability of systems to cyber threats such as Denial-of-Service attacks, unauthorized access, probing activities, and privilege escalation attempts. Traditional security mechanisms, which rely on signature-based detection, are increasingly ineffective against evolving and unknown attack patterns. This paper presents a Smart Cybersecurity Threat Detection Platform that leverages machine learning techniques for real-time identification and classification of network threats. The proposed system is developed using the Django web framework and Python, integrating a Random Forest classifier trained on a dataset modelled after the NSL-KDD benchmark. The platform processes network traffic parameters such as protocol type, connection duration, source and destination byte counts, connection flags, and connection frequency to classify activities into Normal, DoS, Probe, R2L, and U2R categories. In addition to detection, the system incorporates an Automated Threat Isolation System (ATIS) that identifies high-confidence threats and initiates isolation workflows. A real-time dashboard provides visualization of threat distribution, system performance metrics, and alert management. The system also maintains an audit trail of all activities and supports alert tracking and resolution workflows. The Smart Cybersecurity Threat Detection Platform offers a scalable and intelligent approach to network security by combining anomaly detection, real-time monitoring, and automated response mechanisms. It demonstrates an effective transition from reactive to proactive cybersecurity defense strategies.

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Bommisetti Reddi Lakshmi, Dr. S. Usharani

Artificial Intelligence; Credit Scoring; Django; Financial Risk Assessment; Fraud Detection.

The financial services sector is increasingly pivoting toward intelligent, data-driven technologies to enhance decision-making accuracy and mitigate loan default risks. Traditional manual evaluation processes are frequently constrained by time-intensive workflows and subjective biases. This paper presents an AI-Driven Loan Eligibility and Risk Assessment System designed to modernize credit evaluation through a rule-based artificial intelligence engine.Developed using the Django framework, the system automates the assessment of six critical financial dimensions: credit score, income-to-loan ratio, employment stability, debt burden, collateral, and asset holdings. The system generates a composite risk score, fraud anomaly alerts, and personalized loan recommendations, providing a transparent and explainable alternative to “black-box” machine learning models. By integrating role-specific dashboards and real-time notifications, the proposed solution optimizes the lending lifecycle while maintaining the auditable logic required for regulatory compliance.

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Boggala Prudhvi Teja, Dr.S.Usharani

Traffic Violation Detection; YOLOv8; Computer Vision; ANPR; Deep Learning; Smart Traffic System.

The rapid increase in urban vehicle density has posed significant challenges to traditional traffic management systems, which rely heavily on manual monitoring and enforcement. These conventional approaches are often inefficient, prone to human error, and incapable of providing continuous surveillance across large-scale road networks. To address these limitations, this paper presents a Smart Traffic Violation Detection and Reporting System that leverages advancements in computer vision, deep learning, and web technologies to automate traffic law enforcement processes.The proposed system utilizes the YOLOv8 object detection model for real-time identification of vehicles and detection of traffic violations such as signal jumping, over-speeding, helmet violations, triple riding, and wrong-way driving. Automatic Number Plate Recognition (ANPR) is implemented using EasyOCR to extract vehicle registration numbers from captured frames. The system further integrates an AI-based event summarization module that generates structured and human-readable violation reports to assist enforcement authorities. A Django-based web application serves as the backend framework, enabling efficient data management, secure user authentication, and real-time monitoring through an interactive dashboard. The system also incorporates analytics features to identify traffic violation trends and hotspots, facilitating data-driven decision-making. The entire architecture is built using open-source tools such as Python, OpenCV, PyTorch, and Chart.js, ensuring scalability and cost-effectiveness. The proposed solution enhances traffic monitoring efficiency, reduces dependency on manual intervention, and contributes to improved road safety by enabling consistent and accurate enforcement of traffic regulations.

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B Lavany, Dr.S.Usharani

Predictive Quality Management; Production Defect Prediction; Random Forest Classifier; Process Optimization; Industry 4.0; Machine Learning.

In modern manufacturing systems, ensuring consistent product quality across high-speed production lines is a major challenge due to equipment wear, process fluctuations, operator variability, and batch inconsistencies. Traditional quality control methods rely on manual inspection and post-production sampling, making them reactive and inefficient since defects are detected only after production. Predictive QualityX is a machine learning-based defect prediction and process optimization platform designed to enable proactive quality management. The system uses a Random Forest classifier trained on 25,000 synthetic production records generated from realistic process parameters, including temperature, pressure, vibration, rotational speed, machine ID, and operator ID across multiple machines and operators. The platform implements an end-to-end data pipeline that includes synthetic data generation, model training, evaluation, database integration using MySQL, and an interactive analytics dashboard. The trained model identifies complex nonlinear relationships between process variables and defect occurrence, enabling accurate prediction of product quality outcomes. The system also processes both historical and future production data to provide retrospective analysis and forward-looking defect risk estimation. Results are stored in Excel and MySQL databases for reporting and enterprise integration. An interactive dashboard visualizes key insights such as machine-wise defect rates, parameter correlations, and temporal defect trends. This enables engineers and managers to identify high-risk conditions, optimize processes, and reduce production losses, making Predictive QualityX a comprehensive intelligent manufacturing decision-support system.

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Muuka Luyando Loverly, Dr. Siyumbwa Costa

Education Anxiety; Academic Performance; Senanga District; Zambia; AI Mental Health; Emotional Intelligence; Wellbeing Support; Test Anxiety.

Education anxiety encompassing test anxiety, performance anxiety, social anxiety in learning contexts, and generalised academic worry is a pervasive and academically consequential psychological phenomenon affecting learners across all educational levels and national contexts. In Senanga District, Western Province, Zambia, education anxiety among secondary school pupils represents a significant but largely unaddressed barrier to academic performance, school engagement, and psychological well-being. This article examines the effects of education anxiety on the academic performance of pupils at Senanga Secondary School, situating local findings within global scholarship on anxiety and learning, AI-powered mental health promotion platforms, emotional intelligence development, and digital wellbeing support in educational contexts. Drawing on a mixed-methods survey, findings confirm significant negative associations between anxiety levels and academic performance, with examination anxiety, teacher-learner relationship anxiety, and peer comparison anxiety identified as primary manifestations. The article argues that AI-powered early identification systems, mobile-based emotional intelligence development tools, and school-based wellbeing programmes offer evidence-based pathways for reducing education anxiety and enhancing academic performance. Policy recommendations are presented.

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Subhash Chandra Bose Y

Nawal El Saadawi; Feminist Theory; Postcolonial Feminism; Intersectionality; Subaltern Voice; Gender Performativity; Resistance; Patriarchy; Existential Freedom; Women’s Writing.

This paper presents an advanced feminist and postcolonial analysis of Woman at Point Zero (1975) by Nawal El Saadawi, examining the text through the intersecting lenses of feminist waves, existentialism, and contemporary feminist theory. The narrative of Firdaus, a woman sentenced to death, is interpreted as a radical articulation of resistance that challenges the epistemological, social, and institutional foundations of patriarchy. Moving beyond conventional feminist readings, this study incorporates the theoretical contributions of Judith Butler, Gayatri Chakravorty Spivak, and bell hooks to explore issues of performativity, subalternity, and intersectionality. The paper argues that Saadawi reconstructs the notion of freedom as an existential awakening achieved through resistance to both external domination and internalized oppression. Ultimately, Woman at Point Zero is positioned as a transformative feminist text that not only critiques patriarchal systems but also redefines the possibilities of agency, voice, and liberation in a global context.

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