Authors: Dr. Saroj Agarwal, Sumit Sharma, Tanmay Kumawat, Vikas Bansal

 

Abstract: Traditional attendance monitoring systems rely heavily on manual processes or contact-based biometric solutions, which often lead to inefficiencies, proxy attendance, and lack of real-time behavioural insights [7]. Recent advancements in computer vision [5] have introduced facial recognition-based attendance systems; however, most existing solutions focus only on identity verification and fail to analyze participant engagement or emotional response during sessions [6]. This paper presents a comprehensive review and analysis of an integrated Facial Attendance and Sentiment Tracking System (FASTER), which combines real-time face detection [1], facial recognition using LBPH [2] and SVM classifiers [3], and expression-based sentiment monitoring [6] within a lightweight client-server architecture. Unlike previous systems that utilize either attendance automation or emotion detection independently, the proposed approach integrates both functionalities using OpenCV-based face detection [8], machine learning classifiers, and real-time data logging mechanisms. The system emphasizes low computational overhead, offline ca- pability, and user-friendly GUI-based interaction, making it suit- able for educational and organizational environments. Through comparative analysis with existing research, this study identifies key limitations in prior work and highlights the novelty of a unified attendance and sentiment-aware monitoring framework.

DOI: https://doi.org/10.5281/zenodo.19207818

 

Published by: vikaspatanker

Authors: Deepa M P

Abstract: In the digital era, data is considered a valuable asset for organizations and industries. Database Management Systems (DBMS) provide a systematic way to store, manage, and retrieve data efficiently. With the rapid growth of technology, DBMS has become essential in integrating operations across various sectors. From banking transactions to healthcare records and e-commerce platforms, databases play a crucial role in ensuring seamless functionality and decision-making. Database Management Systems (DBMS) have become a fundamental component in modern digital infrastructure, enabling efficient storage, retrieval, and management of data across diverse sectors. This paper explores the role of DBMS as a core technology integrating multiple domains such as banking, healthcare, education, e-commerce, and government systems. It highlights how databases ensure data consistency, security, and scalability while supporting real-time applications. The study also examines emerging trends such as cloud databases, AI integration, and distributed systems. The findings demonstrate that DBMS acts as a unifying backbone, driving digital transformation and improving operational efficiency across sectors.

DOI: https://doi.org/10.5281/zenodo.19207441

Published by: vikaspatanker

Authors: Aditi Gaur

Abstract: Workplace harassment and gender inequality continue to be persistent challenges in urban institutions despite increasing female participation in the workforce and the presence of legal safeguards. This paper examines the nature, forms, and impact of workplace harassment on women employees in urban public institutions. It also explores how structural inequalities, patriarchal norms, and organizational culture contribute to gender-based discrimination. Drawing on sociological theories and existing literature, the study highlights the gap between policy and practice, particularly in the implementation of laws such as the POSH Act. The paper concludes that while urban institutions offer better employment opportunities, they also reproduce gender inequalities through subtle and overt mechanisms. Policy recommendations are provided to promote safe and inclusive workplaces.

DOI: https://doi.org/10.5281/zenodo.19205342

 

Published by: vikaspatanker

Authors: Aryan Nandgaonkar, Prathmesh Kore, Mayur Godse, Om Dhamale, Shital Kawale

 

Abstract: EDUFLOW is an advanced, AI-powered educational management system designed to enhance the learning and teaching experience by integrating modern technologies with intelligent automation. The primary objective of the system is to simplify academic processes such as content creation, assessment generation, timetable management, and resource organization for both students and teachers. Traditional educational systems often face challenges such as time-consuming content preparation, lack of personalized learning support, and inefficient resource management. EDUFLOW addresses these issues by providing a centralized platform that leverages artificial intelligence to automate and optimize educational tasks. The system enables students to generate study materials, practice quizzes, and personalized timetables, helping them improve their learning efficiency and time management. At the same time, teachers can create quizzes, exams, and teaching schedules with minimal effort, reducing their workload and allowing them to focus more on effective teaching. One of the key features of EDUFLOW is its integration with AI models, which generate high-quality educational content such as multiple-choice questions, study notes, flashcards, and summaries based on user input. This significantly reduces manual effort and ensures the availability of diverse and up-to-date learning resources.

DOI: https://doi.org/10.5281/zenodo.19205066

 

Published by: vikaspatanker

Authors: Imrana. Z, Sanjay. S, Dr. K. Brindha

Abstract: Healthcare monitoring systems are evolving rapidly with the integration of artificial intelligence, wearable sensors, and cloud-based data analytics. Traditional healthcare monitoring approaches rely on periodic medical examinations which may fail to detect early health risks. This research proposes an AI-assisted predictive health monitoring framework capable of analysing physiological data collected from wearable devices. The system processes health indicators such as heart rate, sleep patterns, and physical activity to identify abnormal trends and provide early alerts. Machine learning algorithms are employed to analyse patterns and support preventive healthcare monitoring. Experimental evaluation indicates that predictive analytics improves early health risk detection compared to conventional monitoring approaches. The proposed system highlights the importance of integrating intelligent analytics with digital healthcare systems.

DOI: https://doi.org/10.5281/zenodo.19203957

Published by: vikaspatanker

Authors: Ms. Shristi Singh

Abstract: The rapid growth of digital technologies has significantly transformed modern marketing practices. Businesses increasingly rely on digital platforms such as social media, websites, and data analytics tools to engage customers and deliver personalized experiences. However, this dependence on consumer data has raised serious concerns regarding data privacy and protection. In response, regulatory frameworks such as the General Data Protection Regulation (GDPR) and India’s Digital Personal Data Protection (DPDP) Act have been introduced to ensure ethical and transparent data practices. These regulations have compelled organizations to modify their digital marketing strategies by emphasizing consent, transparency, and data security. This study examines the impact of data privacy regulations on digital marketing strategies using secondary data collected from research articles, industry reports, and official publications (2020–2025). The findings indicate that while compliance increases operational costs and restricts data usage, it also enhances consumer trust and encourages ethical marketing practices. The study concludes that privacy-focused marketing is not only a legal necessity but also a strategic advantage for long-term business sustainability.

DOI: https://doi.org/10.5281/zenodo.19203389

Published by: vikaspatanker

Authors: Ayesha Sayyad, Afrin Sayyad, Pragati Khude, Jyoti Bhuruk, Mrs.P.P.Maindargi

Abstract: Predictive maintenance has become an important application of Artificial Intelligence in modern industries. Traditional maintenance techniques often lead to unexpected machine failures and increased operational costs. This research proposes an AI-based smart digital twin system that monitors machine performance and predicts possible failures before they occur. The digital twin model replicates the physical machine in a virtual environment using sensor data and machine learning algorithms. The system analyzes temperature, vibration, and operational parameters to detect abnormal patterns. Experimental results show that the proposed model can effectively identify potential faults and reduce downtime. This approach improves maintenance efficiency, increases equipment life, and reduces operational costs.

Published by: vikaspatanker

Authors: Srikavyalakshmi S, Sivakanni

 

Abstract: The Indian mobile application market has grown significantly in recent years, with digital platforms becoming an essential tool for businesses to connect with their target audience. In this fast-moving environment, marketing campaigns play a critical role in determining whether an app gains visibility, attracts users, and retains them over time. This study examines the effectiveness of marketing campaigns for Yuukke, a women-focused digital networking and community platform developed by Betamonks Technology Factory Pvt. Ltd., Chennai. Since Yuukke currently relies on informal and unstructured marketing with no defined strategy, understanding which channels and approaches actually work for their specific audience has become a pressing business need. Through descriptive research, this study analyses consumer behavior, channel preferences, and the impact of marketing frequency on app usage among women entrepreneurs, professionals, and startup aspirants in India.

DOI: https://doi.org/10.5281/zenodo.19185189

 

Published by: vikaspatanker

Authors: Mr.C.Radhakrishnan, Nijuram

 

Abstract: Cognitive Sleep Modulation through Generative AI and Real-Time Multi-Sensor Fusion introduces an intelligent, adaptive framework designed to improve sleep quality using advanced artificial intelligence techniques. The system gathers multi-modal physiological data—including electroencephalography (EEG), heart rate variability (HRV), respiratory signals, and body movement—from wearable and IoT-enabled devices. A real-time sensor fusion mechanism integrates these heterogeneous data streams and applies deep learning models to accurately classify sleep stages and detect disruptions. Based on the identified physiological state, generative AI algorithms produce personalized audio guidance, calming soundscapes, and cognitive relaxation prompts tailored to individual neural patterns. The framework dynamically adjusts environmental conditions such as lighting, sound, and temperature to facilitate smooth transitions across sleep cycles. Reinforcement learning strategies continuously optimize interventions by learning from long-term sleep efficiency metrics and user feedback. Experimental evaluations indicate reduced sleep onset latency, prolonged deep sleep phases, and improved sleep consistency. This intelligent, non-invasive solution demonstrates strong potential for personalized sleep enhancement and contributes to advancements in digital healthcare, cognitive science, and AI-driven wellness systems.

DOI: https://doi.org/10.5281/zenodo.19184531

 

Published by: vikaspatanker

Authors: P. Sunil, G. Swapna

Abstract: Quantum machine learning has emerged as a promising approach to enhance computational efficiency by leveraging the principles of quantum computing. However, the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, such as noise, limited qubit availability, and circuit depth constraints, restrict the implementation of fully quantum models. To address these challenges, this study focuses on the design, implementation, and performance evaluation of hybrid quantum-classical machine learning (HQML) models. The proposed approach integrates parameterized quantum circuits with classical optimization techniques to enable efficient learning within NISQ environments. The study employs standard benchmark datasets, including Iris, Breast Cancer, and MNIST, to evaluate the performance of the hybrid model. The results indicate that the HQML model achieves competitive accuracy on small and medium-sized datasets while maintaining balanced precision, recall, and F1-score. However, performance declines for complex datasets due to hardware limitations and noise effects. Additionally, the hybrid model demonstrates a lower number of parameters compared to classical deep learning models but requires higher training time due to iterative quantum-classical optimization. The findings highlight that hybrid quantum-classical models provide a practical and scalable approach for utilizing quantum computing in the current technological landscape. Although challenges related to noise, scalability, and computational overhead persist, advancements in quantum hardware and algorithm design are expected to improve performance. This study contributes to the growing field of quantum machine learning by providing a systematic framework for evaluating hybrid models on NISQ devices and identifying key areas for future research.

Published by: vikaspatanker