Quantum- based optimization of learning pathways in remote education platforms using analytics Jany S. L. Shabu, J. Refonaa, N. Anusha, Sheryl Oliver A., Sonia Jenifer Rayen, et al. Revolutionizing Education with Remote Experimentation and Learning Analytics, 2025 The use of quantum-based optimization methods for integrating with remote education applications can be considered as one of the novel ways of improving the learning environment and achievements. As the situation and the concept of remote education remain rather new and constantly developing, it is crucial to imply flexible learning paths. The advancement of quantum computing and analytics as within the past decade provide possible solutions for dealing with intricate challenges in optimizing education products and services. This introduction discusses related literature to inform the development of quantum-based approaches to enhancing remote learning. Finally, through experiments, the work also shows the potential of using quantum-based optimization in improving the learning remoting system to offer better learning solutions.
A swarm intelligence optimization for lung cancer detection from RNA-seq gene expression data using convolutional neural networks Swarm Optimization for Biomedical Applications, 2025
CONVOLUTIONAL NEURAL NETWORK-BASED DRIVER DROWSINESS DETECTION SYSTEM Arpn Journal of Engineering and Applied Sciences, 2025 Driver drowsiness and fatigue form the two main causes for road accidents around the globe, affecting more people who fall in the age group 18-45. In this paper, a proposed Driver Safety System (DSS) is aimed at detecting real-time drivers' signs of fatigue. The system captures video at all times of the face of the driver and develops each frame into grayscale images using the HAAR CASCADE algorithm, which is a very reliable object detection tool. MobileNet takes these images to deep recognition and tracking of closed eyes per frame. Inside the decision block, a counter logs the period over which the eyes close that puts a flag on the drowsy driver, raises an alert, and then restarts the counter. For higher accuracy, the DSS integrates other Convolutional Neural Networks (CNN) models that are implemented, such as ResNet50, MobileNetV2, and VGG16. Using pre-trained layers, it enhances the system to more accurately classify distracted behavior. Optimized as a High Precision Low Power (HPLP) prototype, it functions at its best under consistent lighting with a homogeneous background, so there are no reflections or interference from the background. Testing has shown that this approach, based on CNN, significantly outperforms existing techniques with better accuracy. The DSS would process video in real-time and identify fatigue much in advance, thus proving its meaningful contribution to safer driving through timely alert mechanisms.
Crypto Tracking Web Application N. Anusha, Akepogu Vivek, Ananya Gullapally, Ponugoti Ram Teja, Regadamilli S R S Rahul 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024
Groundwater Chemistry Of Umred Taluka, Nagpur District, Maharashtra Alpashi L Sadawarti, Shubham P Masurkar, N. Anusha, J. Refonaa, Ramesh Cheripelli 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024
Enhanced Web based Multi-Platform E-voting Solution Anusha Nallapareddy, T Swapna, Sundaramurthy Shanmugam, Jany Shabu, J Refonaa Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
Enforcement of CNN Model in Drone Detection System A. Viji Amutha Mary, N Anusha, Mercy Paul Selvan, R. Rajalakshmi, S. Jancy, et al. IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024
