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, Sangeetha Subramaniam 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
Study and Analysis of Cyberbullying Message Detection and Prevention Using Machine Learning Techniques S. Shanmugam, S. Gunasekaran, N. Anusha Securing the Digital Frontier Threats and Advanced Techniques in Security and Forensics, 2025 The proliferation of Internet usage has facilitated the practice of networking through social media platforms while also intensifying issues such as online abuse and cyberbullying. Cyberbullying has a disproportionately lousy effect, with victims’ health often deteriorating and some even resorting to suicide as a result. This chapter studies cyberbullying and classifying related messages to develop effective detection methods. We compare four machine learning approaches using Count Vectorizer and TF-IDF across two metrics. Recent advances in machine learning enable automatic cyberbullying detection, which can help mitigate the problem and assist victims. However, limitations exist in using machine learning for this task. While machine learning shows potential for cyberbullying detection, further research is required to improve accuracy and viability. This chapter discusses cyberbullying, proposes a classification framework, evaluates machine learning techniques, and suggests future research directions for developing more robust cyberbullying detection.
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.
Multimodal Deep Learning Framework for Pneumonia Detection Using Chest X-Rays and Clinical Data Anusha N, Sri Vaishnavi Banda, Alekhya Koyyada, Sathvika S, Viji Amutha Mary A, Harika Chiluveru 2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025 Pneumonia is an acute respiratory disease that warrants prompt and precise diagnosis, especially in high-risk populations including children, the elderly, and those with weakened immune systems. Although chest X-rays are frequently used in diagnosis, they often overlook important clinical information. In this work, we propose a deep learning model that integrates chest X-ray images and labeled clinical data, including age, gender, fever, SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, WBC count, and urinalysis. The model has a dual-branch structure where a CNN processes the images and a multilayer perceptron processes the clinical data, followed by feature fusion and a model fusion for the final classification. The model was trained on 624 cases and achieved 84% accuracy, with an F1 score of 0.89 and an AUC of 0.90. The Integrative Multimodal Deep Learning Model, based on the results, shows that the combination of clinical data with imaging is a breakthrough in precision and sensitivity of diagnosing pneumonia in comparison to single-modality approaches. In conclusion, this work illustrates the strong promise AI-powered multimodal frameworks hold for medical diagnosis in real-life scenarios, especially in underserved or precarious environments.
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, L K Joshila Grace IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024
Locker system: Development of intelligent surveillance using secure one time password and face recognition Arpn Journal of Engineering and Applied Sciences, 2017
Secure auditing of shared data in a private cloud using email notification Arpn Journal of Engineering and Applied Sciences, 2017
Android location based reminder including step counting distance and calorie measuring Research Journal of Pharmaceutical Biological and Chemical Sciences, 2016
Secured health records storage & retrieval system using keyword based key generation and Attribute Based Encryption (ABE) Research Journal of Pharmaceutical Biological and Chemical Sciences, 2016
Speech analysis for gender and emotion classification using classifier techinique Research Journal of Pharmaceutical Biological and Chemical Sciences, 2016
Private searching keyword frequency using homomorphic encryption Journal of Theoretical and Applied Information Technology, 2015
Evaluation of spatial query processing in spatial database International Journal of Applied Engineering Research, 2015
Secured data aggregation on multiple sensor applications International Journal of Applied Engineering Research, 2015
Dynamic data security considerations in Multi-Cloud storage International Journal of Applied Engineering Research, 2015
