http://10.9.150.37:8080/dspace//handle/atmiyauni/487 2026-04-27T20:00:17Z http://10.9.150.37:8080/dspace//handle/atmiyauni/1737 Title: Physical Health Improvement in First Year Undergraduate Students Under Mentoring Scheme Authors: Dave, Nehal; Kavathiya, Hiren Abstract: For first-year undergraduate students, making the move from high school to college entails major changes in their academic, social, and personal lives. The preservation of physical health frequently takes a backseat among these modifications. Many academic institutions have implemented mentorship programs designed to help students completely in order to solve this issue. This study examines whether mentorship programs may improve the physical health of first-year college students. This study uses a mixed-methods approach to evaluate the association between mentorship programs and improvements in physical health by combining quantitative surveys and qualitative interviews. Through the use of a questionnaire, information was gathered to assess changes in physical health-related behaviors including exercise frequency and nutritional preferences. Results show that mentorship programs have a beneficial impact on first-year undergraduate students' physical health. Data from the survey show that participants' participation with healthy lifestyle choices has significantly improved, including their frequency of exercise and dietary habits. Mentorship may improve one's quality of life while also helping first-year undergraduate students succeed by boosting their physical wellness 2023-01-01T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/1705 Title: Analyzing the Impact of Social Media Dynamics Through Sentiment Analysis: A Case Study on Influencer-Generated Content and Response on Recommendation Systems Amidst the Lakshadweep-Maldives Situation Authors: Kiritbhai, Katira Madhuri; Ranpara, Ripal Abstract: Our Research helps user to analyze the impact of Influencer generated Content on recommendation systems. By Understanding how sentiments expressed on social media platforms may influence recommendation algorithms is crucial and for enhancing the effectiveness and relevance in our system it needs to explore. we focus on exploring the correlation between influencer-generated content on social media platform and user responses within the geopolitical situation of Lakshadweep-Maldives. The primary goal of this research is to analyze how sentiments expressed by influencers on various platforms influence public opinion and afterward effect on recommendation system outputs. We examining sentiments across diverse sources of studying influencer content related to the Maldives and Lakshadweep geopolitical situation. We identified significant correlations between influencer activities that can shifts search patterns in the recommendation system. By conducting a comprehensive case study we aim to uncover deeper understanding of how influencer dynamics in digital spaces influence recommendation systems to shape perceptions particularly in the context of geopolitical events. This research is essential as it provides valuable insights for enhancing travel recommendation systems and making them more transparent and contextually aware to the end user 2024-08-01T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/1694 Title: A Framework for Fine Grained Sentiment Analysis on Code-Mixed Language for Social Media User Behaviours Authors: Tank, Anand; Vanjara, Pratik Abstract: Here, we provide a framework that discovers sentiments from social media platforms, assesses, and transforms them into meaningful data. Social media is changing people's attitudes and habits, which in turn is influencing their choices. Attempting to keep an eye on social networking activity is a useful tool for tracking consumer attitude about products and firms and gauging loyalty from consumers. The framework can be the next natural area for branding based on the polarities on the internet and social media. We present a dynamic solution method for sentiment analysis using the classification of interpersonal data sources. To evaluate the caliber of social information services, we also introduce a brand-new quality model. We utilize public comments, posts through social media as an inspiring case study. Specifically, to pinpoint the comments and posts we concentrate on the spatiotemporal characteristics of the attitudes expressed by social media users. On datasets from the real world, experiments are carried out. Our suggested model’s performance is preliminary demonstrated by performance evaluation matrix 2024-01-01T00:00:00Z