Daily Archives: December 31, 2025

Authors: Pranav Dhondibhau Gawade, Sarthak Vivek Sagare, Sujan Anna Kambale, Piyush Vinod Chaudhary, Neelam N Kavale

Abstract: The proposed Smart Community Health Monitoring and Early Warning System for rural areas offers a transformative, cost-effective alternative to expensive, sensor-dependent technologies by prioritizing syndromic surveillance and community-led data collection. Recognizing that traditional IoT infrastructure often fails in remote regions due to high maintenance costs and power instability, this model empowers community health workers to act as "human sensors," manually reporting clinical symptoms like fever and diarrhea via an offline-capable mobile interface. By integrating these health reports with periodic, low-cost chemical water testing, the system utilizes a centralized analytical engine to run statistical aberration detection algorithms that compare real-time trends against historical baselines. This proactive framework identifies potential pathogenic outbreaks at their nascent stage, triggering a tiered Early Warning System (EWS) that alerts local authorities through automated SMS and voice calls. Ultimately, this research demonstrates that public health resilience is not solely dependent on high-tech hardware but can be achieved through strategic data management, community participation, and smart analytics, providing a scalable and sustainable blueprint for disease prevention in resource-constrained environments globally.

Published by: vikaspatanker

Authors: Priya Palanimurugan, Miss. R.Christy Alice, Dr.Thulasi Bharathi.M, M.sakthivel

Abstract: This essay delves into the complex confluence of emotional realism, cinematic expression, and mental health in the cinema of Balu Mahendra, India's most empathetic director. Through his sensitive explorations of human vulnerability, psychological trauma, and resilience, Mahendra remapped the way the Tamil cinema represented the human mind and its delicate complexities. Exceeding commercial norms, his films like Moondram Pirai (1982), Marupadiyum (1993), Sandhya Raagam (1989), and Veedu (1988) demonstrate a deep psychological realism that humanizes characters normally pushed to the periphery by social or emotional pain. This research uses a psychological model based on trauma theory, studies on empathy, and humanistic psychology to examine how Mahendra's film language turns pain into poetry and silence into emotional conversation. The study places Mahendra's films within the larger framework of Indian cinema's shifting approach to mental health, highlighting how his stories avoid the melodramatic spectacle commonly linked with psychological illness. Rather, his characters are characterized by a quiet dignity that mirrors the internal struggles of memory, loss, identity, and moral dissonance. The paper also investigates the aesthetic aspects of Mahendra's visual realism—his natural lighting, long takes, and close-ups—as methods that conjure emotional truth and ask viewers to enter a reflective psychological zone. Finally, this essay maintains that Balu Mahendra's films work as sympathetic case studies of the human mind, providing social commentary and emotional counseling. His world of film challenges viewers to see mental illness neither as weakness nor as supernatural affliction but as a vital aspect of human experience. In this process, Mahendra's body of work helps in the destigmatization of mental pain and promotes a different cinematic language based on compassion, realism, and psychological complexity.

Published by: vikaspatanker

Authors: Priya Palanimurugan, Mr Kalaiselvan S, Dr.Thulasi Bharathi M, M.sakthivel

Abstract: Mental health issues remain a global public health concern, especially among the youth and digitally active population. This study examines the effect of digital storytelling as an intervention tool to increase mental health awareness, reduce stigma and encourage positive behavior changes. A quantitative research design involving 300 graduate students aged 18–25 in diverse educational subjects was employed. Participants were divided into an intervention group, which reflects a control group that receives curate digital stories and traditional information-based materials reflecting real-life mental health experiences.Advanced statistical techniques were used to assess the results in three time points (Post, Post-up). Descriptive data briefly presented demographic data; Alpha of Cronback confirmed the reliability of the scale; Confirmation factor analysis (CFA) valid measurement construction; And the multi -comprehensive analysis of the covalent (mancova) identified important group differences. Repeated measures Anova and Structural Equation Modeling (SEM) further detected time-based reforms in the intention of mental health awareness and behavior, mediate by low stigma. Moderation and latent development analysis highlighted demographic effects and individual trajectory patterns. Conclusions suggest that digital storytelling improves mental health awareness and reduces stigma compared to traditional approaches (P <0.01). The narrative-based method was particularly effective among the pre-risk participants for high digital literacy and mental health materials. The study supports the integration of digital story stories in public health education and mental health advocacy programs. These results contribute to increasing evidence that creative digital equipment can change mental health communication, offering scalable, attractive and human-focused solutions.

Published by: vikaspatanker

Authors: Sakthivel S, Vikash P

Abstract: The rapid expansion of digital financial services has significantly transformed the global financial ecosystem by enabling fast, convenient, and seamless transactions. However, this transformation has also increased the vulnerability of financial systems to fraudulent activities such as credit card fraud, identity theft, phishing attacks, insider fraud, and money laundering. Financial fraud results in substantial economic losses, damages institutional reputation, and undermines customer trust in digital banking systems. Traditional fraud detection mechanisms primarily rely on rule-based systems and manual audits, which are reactive, inflexible, and often incapable of detecting complex and evolving fraud patterns in real time. Advancements in artificial intelligence (AI), machine learning (ML), and data analytics have paved the way for intelligent financial fraud detection systems capable of processing large volumes of transaction data efficiently. By learning patterns from historical transaction data and identifying anomalies, AI-driven systems enable early detection and prevention of fraudulent activities. This paper presents an AI-based financial fraud detection framework that integrates data preprocessing, feature engineering, and machine learning-based classification for real-time fraud analysis. The proposed system aims to improve detection accuracy, reduce false positives, and enhance the overall security of digital financial transactions. Experimental results and analysis demonstrate that intelligent fraud detection systems provide scalable, adaptive, and reliable solutions for modern financial environments.

Published by: vikaspatanker