Category Archives: Uncategorized

Authors: Ms. K.Madhumitha, Abdul Kareem S, Divakaran M, Gowtham G M

Abstract: Real-Time Voice-Enabled IoT Irrigation for Smart Agriculture introduces an advanced automated irrigation system aimed at improving water management and agricultural efficiency. The proposed framework combines IoT-based environmental sensors with real-time data processing and a voice-interaction interface to support intelligent farm operations. Sensors deployed in the field measure soil moisture, ambient temperature, and humidity, transmitting the collected data to a cloud platform for continuous monitoring and analysis. The system automatically activates or deactivates irrigation based on threshold values and real-time conditions, ensuring precise water distribution. Furthermore, a voice-enabled feature allows farmers to access system updates and manage irrigation through simple spoken commands using smartphones or smart devices. This reduces the need for manual supervision and promotes efficient resource utilization. The solution is particularly beneficial for remote agricultural areas where timely intervention is critical. Experimental validation indicates enhanced water conservation, reduced operational effort, and improved crop growth compared to conventional irrigation practices. Overall, the proposed system offers a scalable, economical, and user-friendly approach to achieving sustainable and data-driven smart farming.

DOI: https://doi.org/10.5281/zenodo.18998450

 

Published by: vikaspatanker

Authors: Deepak Tomar

Abstract: The rapid expansion of cloud computing, artificial intelligence applications, and hyperscale digital services has significantly increased the energy demand of modern data centers, raising concerns about sustainability and operational efficiency. Energy storage systems have emerged as a promising solution for stabilizing power supply, integrating renewable energy sources, and improving overall energy utilization in data center infrastructures. However, conventional energy management strategies often lack the intelligence required to dynamically optimize energy storage and distribution under varying workloads and fluctuating energy availability. This study explores the concept of intelligent energy storage management for sustainable data centers by integrating advanced analytics, machine learning techniques, and real-time monitoring systems to optimize energy storage operations. The proposed framework enables predictive energy demand forecasting, intelligent charging and discharging of storage systems, and efficient integration of renewable energy sources such as solar and wind power. Through intelligent decision-making mechanisms, the system aims to reduce energy waste, lower operational costs, and minimize carbon emissions while maintaining high reliability and performance of data center operations. The findings highlight the potential of intelligent energy storage management systems to significantly enhance energy efficiency and support the transition toward greener and more sustainable data center infrastructures.

DOI: https://doi.org/10.5281/zenodo.18997564

 

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar

Abstract: Insider threats remain one of the most complex and costly cybersecurity challenges faced by modern organizations, as malicious or negligent actions originate from trusted users who possess legitimate access to critical systems and sensitive information. Traditional rule-based detection mechanisms often fail to identify subtle behavioral deviations that precede insider incidents, resulting in delayed response and elevated organizational risk. This study proposes a behavioral analytics framework powered by machine learning techniques to detect insider threats through dynamic modeling of user activity patterns. By leveraging multi-source organizational logs, including authentication records, file access events, communication metadata, and network activity traces, the framework constructs individualized behavioral baselines and identifies anomalous deviations indicative of potential threat activity. Both supervised and unsupervised learning models are evaluated using a benchmark insider threat dataset, with careful attention to data imbalance mitigation and model interpretability. Experimental results demonstrate that ensemble learning methods and temporal modeling approaches significantly enhance detection accuracy while maintaining acceptable false positive rates. The findings underscore the importance of integrating behavioral machine learning models into Security Operations Centers to enable proactive, scalable, and context-aware insider threat mitigation strategies.

DOI: https://doi.org/10.5281/zenodo.18996897

 

Published by: vikaspatanker

Authors: Prof.Mohite.B, Vaishnavi Mishra, Pranali kardale, Soniya Kerkar, Shruti Pakhare

Abstract: Traditional attendance systems in schools and industries require manual marking, which is time- consuming and prone to errors. This paper presents an *AI-based face recognition attendance system* that automatically detects and recognizes a person’s face using a camera and records attendance in a database. The system uses artificial intelligence and machine learning algorithms to identify individuals in real time. This approach improves accuracy, saves time, and eliminates proxy attendance. The system can be used in educational institutions, offices, and organizations for efficient attendance management.

 

 

Published by: vikaspatanker

Authors: Siddhi Pawar, Anuradha Raut, Tanuja Suryawanshi, Shravani Wadghare, Pradnya Satpute

Abstract: Gesture recognition has become an important research area in Human–Computer Interaction (HCI). It enables users to control digital systems using natural hand movements instead of traditional input devices such as keyboards or touch screens. This paper presents FlowBeat, a gesture‑controlled music interaction system that allows users to control music playback using simple hand gestures captured through a webcam. The system uses computer vision techniques with OpenCV and MediaPipe to detect hand landmarks and classify gestures in real time. The recognized gestures are mapped to commands such as play, pause, next track, and previous track. The proposed system provides a low‑cost, touchless, and intuitive interface for music control. The paper discusses existing gesture recognition techniques, system architecture, algorithm design, and advantages of the proposed solution. The motivation behind developing the FlowBeat system is to create a more natural and convenient way for users to interact with multimedia applications. Traditional music control methods often require physical contact with devices, which may not always be practical in certain situations. Gesture-based interaction allows users to control music playback without touching the device, thereby improving accessibility and user comfort. The proposed system focuses on providing an efficient and user-friendly gesture recognition framework that can operate using commonly available hardware such as a standard webcam. By combining computer vision techniques with real-time gesture detection, the system aims to deliver smooth interaction and reliable performance. The study also highlights the potential of gesture-based interfaces in future multimedia systems and interactive technologies.

 

 

Published by: vikaspatanker

Authors: Mythul Krishna, Shifa Sherin. S, Dr.R.Karthik

Abstract: Augmented Intelligence is a changing paradigm that focuses on human-AI collaboration to augment decision-making, productivity, and problem-solving. Unlike traditional AI, which seeks to automate processes, augmented intelligence concentrates on supplementing human intelligence with machine learning, data analytics, and automation. This collaboration is revolutionizing several sectors, such as healthcare, finance, and education, by enhancing accuracy, efficiency, and innovation. Nonetheless, issues of data privacy, bias in algorithms, and ethics need to be resolved in order to guarantee the deployment of responsible AI. Transparency and human intervention are necessary in developing trust and maximizing AI- based solutions. With the growth of technology, increased intelligence through augmentation is projected to reshape workspaces and social interactions and provide new fronts for collaboration between humans and AI. This paper delves into its uses, challenges, and implications in the future in the digital era.

DOI: https://doi.org/10.5281/zenodo.18980249

 

Published by: vikaspatanker

Authors: Nathivadhani N, Akaliya S, Dr R.Karthik

Abstract: The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.

DOI: https://doi.org/10.5281/zenodo.18979750

 

Published by: vikaspatanker

Authors: R. Saniya Paul, Livistone S P, Dr. S. Sheeja

Abstract: Drug discovery is inherently complex and expensive with many requisites in terms of precision with respect to interactions' molecular modelling, biological activity prediction and chemical compounds optimisation. Classical computing methods have contributed substantially to the progress of computational drug discovery via molecular simulations, machine learning models and high-throughput virtual screening. However, challenges emerge from the exponentiality of molecular configuration space and the low efficiency of classical algorithms. Quantum computing as a new computing paradigm that offers a novel approach to computation based on various phenomena such as superposition and entanglement and therefore offers a way to overcome the previous limitations. This work presents a comparison of classical and quantum computing approaches in drug discovery with emphasis on their strengths and weaknesses, current progress and future prospects in different aspects of pharmaceutical research.

DOI: https://doi.org/10.5281/zenodo.18979549

 

Published by: vikaspatanker

Authors: Pragadeeshwaran R, Mohanapriya D, Dr.S.Sheeja

Abstract: Conventional animal health management practices involve extensive manual observation and documentation, resulting in late disease detection and ineffective veterinary care, especially in rural areas. To fill this pressing need, this paper proposes a comprehensive AI-assisted web application for proactive animal health monitoring. The proposed system employs a strong three-tier architecture, combining a React.js front end, a Node.js API gateway, and Supabase for secure and real-time data management. The system is segmented into role-based portals for Farmers, Veterinarians, and Administrators, supporting bilingual functionality (English and Tamil) for broad grassroots reach. The key innovation here is the combination of two Artificial Intelligence components: a Convolutional Neural Network (CNN) for the quick diagnosis of dermatological and visible diseases from user-submitted images and a Natural Language Processing (NLP) engine that combines unrefined farmer observations into formatted clinical reports. By leveraging the digital recording of longitudinal vitality parameters such as temperature and food intake, along with AI-driven diagnoses, the proposed system enables precise remote veterinary diagnosis. This system greatly minimizes the time gap between disease manifestation and treatment, thus enhancing animal well-being, preventing economic losses for farmers, and optimizing the workflow of veterinary experts.

DOI: https://doi.org/10.5281/zenodo.18979262

Published by: vikaspatanker

Authors: Ms. Shristi Singh

Abstract: The increasing penetration of the internet and the widespread use of smartphones have transformed the way consumers obtain information and purchase products. In the modern digital environment, marketing activities have shifted from traditional methods to digital platforms such as social media, search engines, websites, and e-commerce portals. These platforms allow companies to communicate with customers quickly and effectively while promoting their products and services. With the expansion of digital connectivity in India, the influence of digital marketing is no longer limited to urban regions. Rural areas are also experiencing rapid digital adoption. Improved internet infrastructure and affordable smartphones have enabled rural consumers to access online information, compare products, and engage in digital transactions. The present study explores the satisfaction level of rural consumers toward digital marketing practices. The research relies on secondary data sources such as academic journals, books, research reports, and online publications related to rural marketing and digital consumer behaviour. The findings indicate that digital marketing enhances rural consumers’ access to product information, increases product availability, and offers convenient purchasing options. However, challenges such as limited digital literacy, weak network connectivity, and concerns about online security still influence consumer satisfaction in rural areas.

DOI: https://doi.org/10.5281/zenodo.18979257

 

Published by: vikaspatanker