Volume 14 | Issue 4 |

 Abstract

Gramin Unnayane Panchayati Raj Pratisthan: Paschim Bardhaman Jelar Baidyanathpur O Ukhra Gram Panchayat Elakakendrik Ekti Kshetrasamikshamulak Paryalochana

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Gramin Unnayane Panchayati Raj Pratisthan: Paschim Bardhaman Jelar Baidyanathpur O Ukhra Gram Panchayat Elakakendrik Ekti Kshetrasamikshamulak Paryalochana

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Brand Image, Purchase Intention, Price Sensitivity, Samsung Smartphones, Consumer Behaviour, Chi-Square Test, Kanpur, India

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Creative Commons Attribution 4.0 and The Open Definition

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Career goals and choices are important parts of a person's growth. They are influenced by things like society, education, culture, personality, and work settings. Career decisions can greatly affect a person's life, for better or worse. These choices are often shaped by parents, friends, and the environment. Picking the right career is crucial for students starting professional education because it affects their future success. Research shows that gender plays a big role in career choices. Giving young people the right knowledge and tools helps them plan their futures realistically. This study looks at how gender norms affect career goals in middle school girls. It explores how gender norms shape career goals at a critical stage when societal expectations meet self-perception. It suggests a way to understand how stereotypes, reinforced by family, teachers, and media, limit girls' career choices, often making them ignore STEM fields. The paper calls for a broad approach to break these barriers, stressing the need for inclusive education, role models, and awareness programs. Using data from teenagers aged 13-18 from 1992 to 2025, this study examines changes in career goals, focusing on gender and race/ethnicity differences.

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Career aspirations, Gender norms. Middle school girls

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Alumni engagement plays a significant role in strengthening institutional reputation, supporting student career development, and fostering long-term academic growth. However, many educational institutions continue to manage alumni information using fragmented and manually maintained systems, resulting in outdated data and weak engagement. This paper proposes an artificial intelligence-driven digital platform for centralized alumni data management and engagement. The system integrates automated profile synchronization, intelligent job recommendation, mentorship matching, targeted communication, and real-time analytics within a secure and scalable architecture. The proposed solution improves data accuracy, enhances alumni participation, and reduces administrative effort, thereby enabling sustainable and value-driven alumni-institution relationships.

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Alumni Management System, Artificial Intelligence, Career Services, Data Analytics, Centralized Database.

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Creative Commons Attribution 4.0 and The Open Definition

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Web applications are continuously exposed to advanced cyber threats such as SQL injection, Cross-Site Scripting (XSS), and zero-day attacks. Traditional Web Application Firewalls (WAFs) mainly rely on static rule-based or signature- based detection methods. These systems are reactive in nature and often fail to detect new or unknown attack patterns. To overcome these limitations, this project proposes a Transformer- based end-to-end Web Application Firewall pipeline that uses deep learning for intelligent anomaly detection. The proposed system utilizes a Transformer Auto encoder model to learn the structural and contextual patterns of legitimate HTTP traffic. During the training phase, the model is trained only on benign web server logs collected from platforms such as Apache and Nginx. Dynamic fields like timestamps, session IDs, and user-specific values are normalized to ensure consistent learning. By understanding the normal "grammar" of web requests, the system can identify deviations from expected behavior. In real-time deployment, incoming HTTP requests are processed through pre processing, tokenization, and inference stages. The Transformer model reconstructs each request and calculates a reconstruction loss value. If the loss exceeds a predefined threshold, the request is flagged as anomalous and treated as a potential attack. This method enables the detection of unknown and zero-day exploits without relying on predefined signatures.

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XSS, WAF, SQL Injection, HTTP traffic.

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Social media has become an integral component of contemporary communication and is increasingly shaping teaching and learning practices in higher education. Platforms such as Facebook, Twitter, LinkedIn, blogs, and YouTube offer opportunities for interactive engagement, collaboration, and knowledge sharing, supporting innovative pedagogical approaches like flipped classrooms, collaborative learning, and peer interaction that can enhance student motivation and deepen understanding. However, the incorporation of social media remains uneven, as some educators readily adopt these tools while others face challenges related to limited digital literacy, insufficient institutional support, and concerns about privacy, copyright, and accessibility; moreover, disciplinary differences and cultural contexts further influence patterns of adoption. While existing research has largely focused on student engagement and general usage trends, comparatively little attention has been given to teachers' perspectives and experiences. Addressing this gap, the present study examines how educators perceive and utilize social media in their teaching practices, the challenges they encounter, and the influence of contextual factors such as infrastructure and policy frameworks on its effective use. The findings are expected to provide meaningful insights into the role of social media in higher education and inform the development of more effective, context-sensitive, and sustainable strategies for its incorporation, ultimately enhancing teaching and learning outcomes.

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Social Media, Higher Education, Teacher Perspectives, Student Learning

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Plastic waste management remains a critical global challenge, requiring efficient tracking and processing systems to support circular economy goals. QR code-based tracking systems have emerged as a cost-effective solution for identifying and monitoring plastic waste across its lifecycle. However, these systems currently depend on manual scanning, resulting in tracking inaccuracies, data inconsistencies, and operational inefficiency.

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Artificial Intelligence, QR Code Tracking, Plastic Waste Management, Computer Vision, Deep Learning, Automated Test Case Generation, Waste Classification, Smart Waste Management, IoT, System Validation.

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With the increasing focus on patient centered healthcare, analyzing patient feedback has become essential for improving medical service quality. Traditional methods like manual surveys are time-consuming and fail to capture the full range of patient experiences. To address this, the proposed system uses Artificial Intelligence and Natural Language Processing techniques to automatically analyze unstructured feedback from multiple sources such as hospital forms, social media, and healthcare platforms. It applies sentiment analysis, opinion mining, text classification, and topic modeling to extract meaningful insights, enabling data-driven decision-making and targeted improvements. The framework also integrates predictive analytics to identify recurring issues and healthcare trends, supporting evidence-based policy development. This AI driven approach ensures real-time monitoring, scalability, and adaptability, ultimately enhancing healthcare quality, operational efficiency, and patient trust.

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Sentiment Analysis, Text Classification, Healthcare Analytics, Patient Satisfaction, Healthcare Quality Improvement.

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This study aims to assess the practices related to PCOS among young women in Tamil Nadu and to examine their association with selected socio-demographic. A descriptive research design was adopted for the study. Data were collected from young women aged 18-30 years across selected districts of Tamil Nadu using a non-probability convenience sampling method. A semi-structured questionnaire was used to gather information. The findings reveal that two-thirds of the respondents shows a moderate level of practices, while one-fourth reported low-level practices and less than one-fourth show high-level practices. The majority of respondents belonged to the 21-25 years age group. More than half of the respondents were postgraduates and students. Inferential analysis using non-parametric tests indicated that most socio-demographic variables such as age, employment status, income, awareness, and family history do not show a statistically significant association with practice levels. However, educational level shows a significant association with practices (p < 0.05), indicating better practices among more educated respondents. Overall, the findings highlight that although a moderate level of practices is observed among most respondents, there remains a need to strengthen consistent and effective health behaviours. From a social work perspective, the study emphasizes the importance of community-based interventions and institutional support systems to promote sustainable lifestyle practices and improve the well-being of young women.

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Polycystic Ovary Syndrome, health practices, lifestyle management, young women, preventive health, social work perspective

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The introduction of the Goods and Services Tax (GST) has significantly transformed the accounting landscape, necessitating the adoption of smart accounting systems to ensure transparency, accuracy, and regulatory compliance. This paper examines the role of advanced technological tools--such as cloud accounting, artificial intelligence (AI), machine learning, and automated compliance software--in reshaping accounting practices in the GST era. Smart accounting systems enable real-time data processing, seamless invoice matching, automated return filing, and improved audit trails, thereby reducing human errors and enhancing operational efficiency. The study highlights how digital integration between businesses and tax authorities has increased transparency and minimized tax evasion. It also explores the challenges faced by small and medium enterprises (SMEs) in adapting to these technologies, including high implementation costs, lack of technical expertise, and data security concerns. Furthermore, the paper evaluates the impact of e-invoicing, GST portals, and compliance tools on financial reporting and decision-making processes. By analysing recent trends and technological advancements, this research emphasizes the growing importance of intelligent accounting systems in ensuring timely compliance and fostering a culture of accountability. The findings suggest that organizations adopting smart accounting solutions are better equipped to handle complex GST requirements and maintain competitive advantage. The paper concludes that continuous technological innovation and adequate training are essential for maximizing the benefits of smart accounting systems in the evolving GST framework.

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 Keywords

Keywords: Innovation, Adopting, Compliance, Transparency, Implementation

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Creative Commons Attribution 4.0 and The Open Definition