Volume 13 | Issue 6 |

 Abstract

Students' academic success is shaped by a range of psychological and environmental factors, one of which is their interest in specific subjects. Social Studies, in particular, plays a significant role in influencing students' motivation, engagement, and overall academic performance. This study investigates how students' interest in social studies effects their overall academic achievement among secondary school students in Arsikere, Hassan District. The current study looks at the impact of social studies interest on academic achievement among secondary school students in Hassan District, Karnataka. A stratified random sample of 90 Class IX students was drawn from a variety of schools to ensure that both boys and girls were represented. Self-developed the Interest Scale in Social Studies to examine the students' interest in social studies and academic achievement scores were obtained from school office records. The data was analyzed using Karl Pearson's correlation and the independent samples 't'-test. The study found a substantial positive relationship between social studies interest and academic achievement. Furthermore, students from government schools outperformed those from private aided and unaided schools, demonstrating that both social studies interest and school type are important factors in students' academic achievement. These findings highlight the need of increasing student engagement in social studies and improving the academic environment across all school types.

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 Keywords

Social Studies, Interest, Academic Achievement, Secondary School Students, Influence

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c++ the classic system programming language which is the extension of c is a highly accepted and used language , like c cannot become an obsolete language, so is c++ A Language which is the language of masses,c++ continues to hold its prominence it is used in AI and machine learning,game development, desktop applications A language which cannot be ignored is both c and c++ languages

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obsolete, masses, prominence

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This paper emphasises on the symbiotic relationship between media literacy and cultural hegemony. It attempts to focus on the contextual meaning of both media literacy and culture by giving a brief historical overview of societal norms from media perspective and bring to the fore the interrelatedness of culture and media. Media Literacy and cultural hegemony are interconnected notions that emphasises on how media influences societal values and customs. Media literacy empowers individuals to critically analyse media messages, while cultural hegemony refers to the dominance of certain perspectives and values within a society often reinforced by media. Media literacy helps individuals resist hegemonic influences by fostering critical thinking and media analytical skills. Media plays a vital role in shaping and maintaining cultural hegemony. By controlling the narratives and perspectives presented in media, powerful groups can influence public opinion and reinforce their dominance.

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 Keywords

Media Literacy, Cultural Hegemony, Subalterns, Marxists economics

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As the urban fleet of vehicles is growing rapidly, parking management by intelligent and automated means has become more important. This paper proposes a real-time smart parking system utilizing computer vision and deep learning methods for vehicle detection, dynamic assignment of slots, and automatic billing. The suggested system uses the YOLOv8 object detection model to detect license plates from images with high accuracy followed by Optical Character Recognition (OCR) with added image pre-processing and luminosity normalization to ensure enhanced accuracy in extracting text. After identifying a license plate, the system auto-assigns available parking slots, captures entry and exit times, and computes charges for the stay duration. All parking transactions, including encoded plate images and timestamps, are recorded in an Excel-based database. The suggested framework presents a scalable, cost-efficient, and low-cost solution to urban traffic and parking management issues.

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 Keywords

Smart parking system, License plate recognition, YOLOv8, Optical character recognition (OCR), Dynamic parking slot allocation, Automated billing, Urban traffic management

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ABSTRACT Purpose This paper investigates the psychological biases and behavioural patterns influencing individual investment decisions in India. It particularly examines how demographic factors, confidence levels, and information sources shape the way investors behave in financial markets. Design/Methodology/Approach A cross-sectional descriptive survey was conducted among 25 individual investors across different age groups and educational levels. Data were gathered through a structured questionnaire, and statistical analyses, including chi-square tests and correlation coefficients, were applied to identify significant patterns and relationships. Findings The study found that overconfidence and herd behaviour were the most prominent behavioural biases. A moderate correlation was observed between self-reported market confidence and herd-following tendencies. Education level did not show a statistically significant relationship with susceptibility to these biases. Diversification emerged as the most frequently used strategy to counter bias-driven risks. Practical Implications The results underscore the importance of incorporating behavioural insights into investor education programs and financial advisory services. By addressing common cognitive biases, these interventions can promote more rational and informed investment behaviour. Originality/Value This study contributes region-specific empirical data to the growing field of behavioural finance, highlighting the unique characteristics of Indian retail investors. It bridges the gap between theoretical frameworks and practical financial behaviour in emerging markets.

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 Keywords

Behavioural finance, investor psychology, overconfidence bias, loss aversion, investment decision-making.

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With the growing integration of digital technology in education and daily life, smartphones have become indispensable tools, particularly among university students. However, this convenience comes with consequences--prolonged use of smartphones has been increasingly associated with musculoskeletal issues, including altered cervical posture. One of the most common postural abnormalities observed in frequent smartphone users is Forward Head Posture (FHP), a condition where the head is positioned anteriorly in relation to the body's vertical axis. FHP has been linked to increased cervical spine strain and may contribute to upper limb nerve compressions.

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Forward Head Posture, Smartphone Addiction, Upper Limb Nerve Compression, University Students, Ergonomics, Neural Tension

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Eco printing is a sustainable surface embellishment technique that involves the direct transfer of plant shapes, structures, and sometimes pigments onto textiles through contact and heat. This study explores the eco-printing potential of pine tree leaves on cotton and silk fabrics, pre-treated with four different mordants: alum, copper sulphate, katha, and harda. The experiment evaluates how each mordant affects the clarity, contrast, and aesthetics of the resist-based prints. The results highlight the unique visual impact of each mordant-fabric combination, with silk generally producing clearer and more refined prints than cotton. The study underscores the role of mordants in influencing eco-print outcomes and provides insights into plant-based textile surface design. Keywords: Eco printing, pine leaves, natural dyes, mordants, cotton, silk, sustainable textiles, contact printing.

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Keywords: Eco printing, pine leaves, natural dyes, mordants, cotton, silk, sustainable textiles, contact printing

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The diagnosis of rare genetic disorders remains a significant challenge in modern medicine due to their complex nature, low incidence rates, and the scarcity of clinical data. Traditional diagnostic methods, including genetic testing and clinical evaluation, often require significant time and resources, leading to delayed diagnoses and suboptimal patient outcomes. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer transformative potential for overcoming these limitations by enabling more efficient, accurate, and early detection of such disorders. This paper explores the application of AI and ML algorithms in integrating multi-modal data comprising genomic sequences, medical imaging, and clinical histories to improve the early detection and diagnosis of rare genetic diseases. We present a framework that leverages the power of multi-modal data fusion, where genomic data like DNA/RNA sequencing, medical imaging, for example, X-rays, MRIs, and CT scans, and clinical data like patient history, lab results, and family background) are systematically integrated to form a comprehensive dataset for analysis. We demonstrate how deep learning techniques, particularly Convolutional Neural Networks (CNNs), can extract critical features from medical images, while advanced Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used for analysing sequential clinical data. Ensemble learning models, including Random Forests and Gradient Boosting Machines (GBM), are employed to classify genetic mutations and predict disease outcomes based on structured clinical data. Additionally, this paper emphasizes the importance of transfer learning and multi-task learning (MTL) techniques to address the challenges posed by limited data availability, particularly for rare genetic disorders and by utilizing pre-trained models and learning from multiple related tasks, the system is capable of improving its performance even when working with relatively small datasets. Furthermore, we discuss the integration of explainable AI (XAI) techniques, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model Agnostic Explanation) with Graph Neural Networks (GNNs), to ensure transparency and interpretability in AI-based decision-making, which is crucial for clinical adoption. Our results show that the combination of genomic, imaging, and clinical data not only enhances diagnostic accuracy but also enables earlier detection of genetic disorders that might otherwise go undiagnosed for years. We conclude that Artificial Intelligence and Machine Learning, when applied to multi-modal data integration, holds the potential to revolutionize the diagnosis of rare genetic disorders, ultimately leading to better patient outcomes, personalized treatment strategies, and more efficient healthcare practices.

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 Keywords

Multi-Modal Data Integration, Rare Genetic Disorders, Machine Learning in Healthcare, Explainable AI (XAI) with SHAP & LIME, Genomic and Clinical Data Fusion

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Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

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REAL TIME TRAFFIC SUREILLANCE AND DETECTION USING DEEP LEARNING AND COMPUTER VISION TECHNIQUES

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This study investigates the shift from traditional marketing methods to digital marketing strategies at Spangles Infotech, a tech-based company in Nagercoil, Tamil Nadu. Through a mixed-method approach involving a structured questionnaire administered to 168 respondents, the research identifies key preferences, marketing effectiveness, and consumer responses to both traditional and digital strategies. The results indicate a strong leaning toward digital mediums such as social media marketing and influencer campaigns, highlighting the growing importance of personalization, measurability, and cost-efficiency in the evolving marketing landscape.

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 Keywords

Digital Marketing, Traditional Marketing, Consumer Behaviour, Brand Awareness, Spangles Infotech.

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