Category Archives: Uncategorized

Authors: Amuncio, Jun Rey, Crisostomo, Kenneth, Gatinao, Hannah Michaela G, Palomo, Gerber Jay L, Paculanan, Kristian Jay C, Cedie E. Gabriel MIT

Abstract: The study presents the development and evaluation of a smart wearable system to monitor student appearance and activity participation through ID scanning at South East Asian Institute of Technology (SEAIT), Tupi, South Kotabato. Methods of traditional appearance in educational institutions are often disabled, error-prone and susceptible to manipulation. The project integrates human-computer interaction (HCI) principles into a smart wearable device that uses ID scanning to automate the attendance and recording of student participation. The system aims to improve accuracy, reduce administrative burden, and increase the user experience through user -friendly interfaces and real -time data processing. The purposeful test and performance assessment demonstrated that the system provides more efficiency and satisfaction than traditional methods, although some users expressed concern over the need for privacy and additional support. Overall, the system shows strong ability to increase institutional operations in the resource-limit environment.

Published by: vikaspatanker

Authors: Dhvani Marthak

 

Abstract: Social media was simply a tool for communication but has become an ubiquitous aspect of daily life in today's hyperconnected world. Its excess has taken an eyebrow from media observers, researchers, and therapists due to its unique capability to provide communication as well as content consumption. Social media such as Instagram, Facebook, Twitter, and TikTok provide liquid spaces that blend private information, entertainment, news reporting, and friendship, hence making them irreplaceable. Yet, the psychological price of such hyperconnectivity has turned too instant. The impact of a social media detox, or "social media detox," on participants aged between 16 and 50 years is analyzed in this study. The main objectives are to investigate the changes in emotion, behaviour, and psychology that occur during and after detox and whether these can be sustained in the longer term. The study is a mixed-methods approach, where qualitative interviewing of the response of respondents via 250 questions in a questionnaire produce richness and generalizability. Quantitative data were analyzed via SPSS, but thematic analysis of open questions yields subjective experience. Findings are a radical improvement in sleep quality, concentration, emotional control, and productivity on detox. Participants also manifested greater self-knowledge and social affiliation in the offline world. Though these findings reveal positive short-term improvements, the study further demonstrates high levels of variability concerning the duration over which such improvements are maintained after detox, along with some suggestion of return towards baseline levels of behavior. Besides, initial dependency level, age, and length of detox also appeared to play key mediating roles. Current research thus adds empirical evidence concerning the potential and limitations of social media detox, hence contributing to the literature surrounding digital well-being, mental health promotion, and potential for self-regulation.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Mr. Akshay M. Bodule, Dr. D.N. Chaudhari, Dr. A.P. Jadhao, Professor D.G. Ingale

 

 

Abstract: With the exponential rise in cloud storage usage and the growing demand for cross-platform accessibility, efficient file synchronization has become a critical requirement in modern computing environments. Traditional synchronization techniques, which often rely on full-file transfers and timestamp-based comparisons, are no longer sufficient—particularly in bandwidth-constrained or resource-limited scenarios such as mobile networks and edge computing systems. This paper presents a hybrid synchronization approach that integrates Two-Way Synchronization and Differential Synchronization to improve efficiency, reduce bandwidth consumption, and enhance data consistency. Two-Way Synchronization enables the detection and resolution of file changes from both local and cloud sources, supporting intelligent decision-making for conflict resolution, deletion propagation, and duplicate handling. Differential Synchronization enhances this process by transmitting only the modified segments of files, using techniques such as block-level comparison and rolling hashes, thereby significantly minimizing data transfer volume and synchronization time. The paper outlines the architecture, entities, processes, and data flows involved in each technique, along with corresponding algorithms and flowcharts. Finally, the paper identifies future challenges, focusing on component-level implementation, delta generation, metadata management, and cloud integration. The proposed solution offers a scalable and bandwidth-efficient synchronization framework suitable for real-time collaboration, offline-to-online transitions, and deployment in distributed and hybrid cloud environments.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.120

 

 

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Authors: Nishant Verma, Associate Professor Dr. Mehak

Abstract: This paper explores risk management practices in Small and Medium Enterprises (SMEs) through the interdisciplinary lenses of psychology, economics, and linguistics, emphasizing the role of lifelong learning in achieving sustainable futures. By examining how SMEs perceive, communicate, and economically strategize around risk, we uncover the cognitive, communicative, and systemic factors shaping risk resilience. This study draws on empirical data, theoretical frameworks, and case studies to advocate for integrative, adaptive, and continuous learning mechanisms to enhance SMEs’ sustainability and competitiveness in an increasingly volatile global market.Risk management is a critical component of business strategy, particularly for Small and Medium Enterprises (SMES) that often lack the resources of larger corporations. This paper investigates the risk management practices adopted by SMES, exploring their effectiveness, challenges, and the role of organizational culture, awareness, and external support. Using a mixed-methods approach, the study identifies common risks faced by SMES, evaluates current mitigation strategies, and proposes a framework for improved risk management. The findings highlight a need for enhanced awareness, training, and integration of risk management into business planning. This paper investigates the current landscape of risk management practices among SMES, with a focus on how they perceive, assess, and respond to various types of risks. Drawing on a mixed-methods research approach, including quantitative surveys and qualitative interviews with SME owners and managers, the study reveals that while most SMES recognize the existence of critical risks, few possess structured or formal risk management systems. Instead, risk responses are often reactive, ad hoc, and based on the intuition and personal experiences of the business owner rather than systematic analysis.

Published by: vikaspatanker

Authors: Md Shareef, P Sri Sindu, M Surya Teja, B Prasun Reddy

Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for effective predictive models to aid in early detection and intervention. This study presents a comprehensive approach to lung cancer prediction, leveraging advanced machine learning techniques and multimodal data integration. By incorporating diverse sources of information, including medical imaging scans, clinical records, and genetic markers, our proposed model aims to capture the complex interplay of factors influencing lung cancer risk. We employ a combination of feature engineering, feature selection, and ensemble learning methods to develop robust predictive models capable of accurately identifying individuals at elevated risk of developing lung cancer. Furthermore, we explore the interpretability of our models to gain insights into the underlying factors driving lung cancer susceptibility. Through extensive experimentation and validation on large-scale datasets, we demonstrate the efficacy of our approach in achieving superior predictive performance compared to existing methods. The proposed model holds significant promise for facilitating early detection, personalized risk assessment, and targeted interventions in lung cancer management, ultimately improving patient outcomes and reducing the burden of this devastating disease.

Published by: vikaspatanker

Authors: Naaz Parween, Ankita Gupta

 

Abstract: Understanding the candidate's personality in the modern world of the business world is like spotting the technical skills, just as critical as the latter. In fact, the personality an individual applies is the key to success in both personal and professional aspects. Hence, this study features a system using machine learning based on personality prediction from CVs in order to cut short the hiring time of the right employee to the required position by evaluation of personality contours of the candidate. More advanced yet with a combination of other techniques as for the classification model with the Big Five Personality Model along with the NLP technique, this method defines the traits such as Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism using keyword analysis only. To discover the machine learning algorithm of the highest quality, we tested various ones such as Logistics Regression, Naive Bayes, k-Nearest Neighbours (KNN), Support Vector Machines (SVM), and Random Forest. Consequently, it was evidenced after the study period that the Random Forest algorithm indeed showed the most precise result of 71%, thus surpassing other methods in the survey. At the time, the proposed system together with the business planning called "the recruitment tool" helps companies find the best candidate; therefore, the use of personality-based hiring becomes a major trend in them. The next step in the evolution process is that we will include a more extensive dataset and make the model more precise for tours.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Assistant Professor Mrs. G. Sangeetha Lakshmi, Mrs. S. Hemalatha

Abstract: Early and precise detection of oral cancer is critical for improving patient outcomes, yet conventional diagnostic methods often involve manual analysis, which can be slow and susceptible to human error. To overcome these limitations, this research introduces an automated detection system that combines deep learning for feature extraction with the Random Forest algorithm for classification. By analyzing medical images, the deep learning component identifies essential features such as texture, color inconsistencies, and irregular tissue structures. These features are then processed by the Random Forest classifier, which utilizes an ensemble of decision trees to enhance classification accuracy and minimize errors. Trained on a dedicated dataset of oral cancer images, the model effectively differentiates between malignant and benign tissues. Experimental findings reveal that this hybrid approach outperforms standard machine learning techniques, offering a faster and more dependable diagnostic tool to aid clinicians in early oral cancer detection and improve patient survival rates.

 

 

Published by: vikaspatanker

Authors: Assistant Professor Priya Tyagi, Assistant Professor Dr. A.P Srivastava, Sandeep Kumar Yadav, Sahil Gupta

Abstract: The Edu Tracker Association Platform is a comprehensive web-based application developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) that aims to streamline and enhance the management of educational activities, associations, and student performance tracking within academic institutions. The platform serves as a centralized hub for administrators, faculty, and students to interact, monitor, and manage academic and extracurricular engagements efficiently. By leveraging the full-stack capabilities of MERN, the system ensures a highly responsive user interface (React.js), robust server-side logic (Node.js and Express.js), and scalable data storage (MongoDB). Key features include student profile management, real-time performance tracking, association membership management, event scheduling, and detailed reporting tools. Role-based access control ensures secure data handling and personalized user experiences for students, faculty, and administrators. The Edu Tracker Association Platform enhances transparency, encourages student engagement in academic and non-academic activities, and simplifies the evaluation process. With its modular architecture and RESTful API integration, the platform is designed for scalability, future expansion, and integration with existing educational systems.

Published by: vikaspatanker

Authors: Sishank Singh Rawat, Satyam Dhar, Assistant Professor Dr. Prakash

Abstract: With rising expectations for instant, contactless, and personalized retail experiences, companies like Mars Inc., a global leader in the confectionery industry, are looking to modernize their vending operations. Traditional vending systems are constrained by static inventory models, manual restocking, and a lack of real-time adaptability—leading to stockouts, waste, and poor customer satisfaction. This paper introduces the Intelligent Vending Machine Optimization System, a smart retail solution designed to transform Mars Inc.'s global vending infrastructure. The system integrates IoT sensors, Azure-based Medallion architecture, machine learning, and edge computing to deliver predictive restocking, autonomous maintenance, and real-time customer insights. Voice and gesture-based interfaces improve accessibility, while Power BI dashboards offer centralized monitoring. This approach ensures scalable, energy-efficient, and intelligent vending operations, enabling Mars Inc. to lead the future of automated retail with data-driven precision.

 

 

Published by: vikaspatanker

Authors: Parth Rastogi

Abstract: The gaming industry has witnessed an absolute change because of the Free-to-Play (F2P) model, which not only provides everyone with the chance to enjoy games free of charge but also produces substantial revenue through in-game purchases. The potential threats to the player experience posed by intrusive monetization methods, in particular, loot boxes and pay-2-win mechanics, can result in decreased user engagement and long-run dissatisfaction. This survey studies the ways in which a game developer can achieve the balance between monetization optimization and a player experience – maintaining a high-quality player experience. A research that used a mixed-methods approach was carried out by the authors, including surveys, interviews, and sentiment analysis. The preliminary results support the idea that ethical, viable monetization schemes, like digital clothes in shop and game passes, are good methods for revenue generation and maintaining the player base. On the contrary, those that use exploitative measures will often encounter dislike and a high churn rate, as well. -seen in the gamers' reactions to this interaction. The strategies include the most suitable ones for fair, transparent, and sustainable monetization in F2P games.

 

 

Published by: vikaspatanker