AuthenShield: CAPTCHA-Free Bot Detection Through Passive User Interaction Analysis J. Faritha Banu, A. Advika, Srikrithi Santhanam Multidisciplinary Advancements in Human AI Augmentation, 2025 The importance of cybersecurity has increased significantly in the last decade, and there is a growing demand for safe modes of online identity verification. Traditional text-based and audio-visual CAPTCHAs are becoming vulnerable to AI-based hackers. They also hamper the user's browsing experience and pose accessibility challenges for disabled people. To mitigate these obstacles, this study presents a thorough analysis of passive verification methods employing behavioral biometrics to differentiate between humans and bots. A review of existing literature is conducted to evaluate the effectiveness of mechanisms such as keystroke dynamics, mouse movement tracking, and eye-blink recognition through facial detection. Keystroke analysis is carried out using a dataset of timing metrics from multiple users. Facial detection integrates real-time tracking of eye movements and blinking by computing the Eye Aspect Ratio (EAR). These data points are processed through machine learning models (such as SVM, XGBoost, and Random Forest Classifier) run using Python to train the classifiers and achieve an enhanced, multilayered bot-detection system. Honeypot traps are also incorporated to enhance bot resistance. A DDoS attack is simulated to evaluate the system's efficiency and resilience, and the results indicate that AuthenShield offers superior protection against automated attacks while continuing to maintain accessibility and consumer experience.
Machine Learning-Based Dynamic Context Real-Time Movie Recommendation System J. Faritha Banu, Utkarsh Kumar Singh, Raghav Singh, Abdul Muhaimin Khan Multidisciplinary Advancements in Human AI Augmentation, 2025 This chapter is an advanced film recommendation model that transforms the way users discover and interact with movies. Using avant-garde algorithms, it dynamically analyses user preferences and the visualization of stories and grades to offer highly personalized film suggestions. By participating in natural and interactive dialogues, the recommendations of the model's tailors are based on various criteria, including genres, actors, directors, and thematic elements. The objective is to simplify selecting films, improving user satisfaction by providing cured suggestions that align with individual tastes. When examining the key attributes obtained through the interactions and user feedback, this study evaluates the effectiveness of the different automatic learning models and natural language processing techniques in delivering precise recommendations. A comparative analysis of several algorithms is performed, including collaborative filtering, content based on content and approaches based on deep learning, to determine the optimal balance between precision, computational efficiency, and interpretability. The model of the model to adapt and learn from the user's behaviour guarantees continuous improvement in the quality of the recommendation, so it is a powerful tool for film enthusiasts. The results demonstrate that the AI model, after rigorous evidence against multiple models such as random forests, neuronal networks, and transformers-based architectures, achieves a 94%accuracy, establishing its effectiveness in delivering recommendations of high-quality user-centred movies.
Enhancing Intelligent Transportation Systems in Smart Cities Using VANETs With Deep Reinforcement Transfer Learning and Explainable AI S. S. Subashka Ramesh, J. Faritha Banu, V. R. Kavitha, T. Ramesh Transactions on Emerging Telecommunications Technologies, 2025 Urban automobile congestion is a persistent issue that reduces the quality of life, increases pollution, and causes financial inefficiencies. Existing traffic management strategies struggle to adapt to rapidly changing urban traffic conditions as they rely on static, rule‐based systems. Intelligent Transportation Systems (ITS) operate in highly dynamic environments with intricate temporal and spatial patterns influenced by factors such as weather, social events, and holidays. Accurately modeling these relationships, developing universal representations, and applying them to transportation challenges remain key obstacles. To optimize traffic flow, enhance road safety, and improve decision‐making transparency, this study introduces an advanced framework integrating Deep Reinforcement Transfer Learning (DRTL), Vehicular Ad Hoc Networks (VANETs), and Explainable AI (XAI). The goal is to develop an interpretable and adaptable ITS model capable of learning and applying knowledge across diverse traffic scenarios. The DRTL model facilitates rapid adaptation by leveraging pre‐trained RL techniques to accelerate learning in complex urban environments. XAI enhances model interpretability, ensuring transparency and reliability in ITS operations. The proposed approach is validated through simulations and real‐world traffic data, demonstrating significant improvements in incident detection, route optimization, and congestion forecasting. Compared to conventional machine learning models, the results show a 35% reduction in median congestion, a 40% improvement in real‐time route planning, and a 25% enhancement in accident response time. This research contributes to the development of intelligent, adaptive, and safer transportation networks for future smart cities by improving vehicle interactions, decision‐making accuracy, and system comprehension.
Feature Selection and Dimensionality Reduction for Dengue Prediction Using Autoencoders and Boruta Algorithm Archana T, Faritha Banu J 2025 International Conference on Engineering Innovations and Technologies Icoeit 2025, 2025 Dengue is a worldwide health problem, predominating in the sub- tropical and tropical areas. It is spread by mosquitoes causing serious illness to human. Inadequate treatment combined with a delayed diagnosis may increase the chance of death while early diagnosis depends on reliable and effective predictive algorithms. Duplicate and irrelevant features often appear in high-dimensional clinical datasets, which elevates the computational complexity and reduces model performance. In order to select relevant features and reduce dimensionality in dengue prediction, this work suggests a hybrid strategy that combines Autoencoders and Boruta Algorithm. Autoencoders extract latent representations, removing duplication while Boruta algorithm uses a feature relevance ranking technique to find the most appropriate clinical features. Experimental data shows that this method improves classification accuracy and drastically lowers the amount of input features. Models like XGBoost and Deep Neural Networks further improve prediction performance by fine-tuning feature selection, which lowers computing cost and overfitting. By demonstrating the value of deep learning-driven feature selection in medical diagnosis, this study opens the door to dengue prediction models that are easier to understand and more effective.
Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction Vanitha M, Faritha Banu J 2025 International Conference on Automation and Computation Autocom 2025, 2025 Customer attrition has become the significant challenge for the bank, making large volume of customers to migrate to other banks, as the banks keeps providing multiple benefits to the incoming customers. The loss due to migration of the existing customer to the competitive bankers creates the banking churn, means of loss of customer relationship with the bank, and affects the development, business and the profitability of the corresponding bank. It is essential to predict the banking churn with a primary objective of retaining the customers, considered as a critical task. To achieve this objective, banking sectors employ the customer behavioral analysis to determine the rate of customer churn, resulting in incorrect diagnosis of the churn rate. To over this concern, this research manuscript proposes a novel federated Machine Learning (ML) framework for the prediction of the customer churn, directly contributes for the enhancement of the customer retention. This proposed novel framework offers significant advancements for the banking prediction on customer retention, thus provides an accurate tool for the analysis of customer relationships. The proposed prediction framework is analyzed in terms of accuracy, precision, recall, F1 score and the performance is compared with the state of the art prediction methodologies.
IoT Based Driver Drowsiness Detection Using Convolutional Neural Network Pranav Nair, Shibu Singh, Jahnavi Rai, J. Faritha Banu Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development Indiacom 2025, 2025 Drowsiness while driving is a core threat to global safety on roads, resulting in significant traffic accidents and fatalities. Efficient strategies to combat drowsy driving are an emerging need. The growing volume of vehicle traffic demands machine learning based technological advancements and scalable solutions to prevent road accidents. This paper discusses an IoTbased hybrid driver drowsiness detection system using machine learning algorithms. Cameras and sensors are used to capture the real time video of the driver's face and eyes, steering angle, vehicle speed etc. Additional sensors are used to monitor the heart rate, skin temperature etc. The YOLO model is used to extract features like eye blinking rate and yawning detection and to localize facial features like eyes, mouth, and head position with high precision. The identified regions from YOLO are passed to a CNN for further analysis and drowsiness detection. The proposed model is compared with the existing system and it outperforms it with the highest accuracy of 88.6%.
Weather Driven Predictive Scheduling for Intelligent Planner and Scheduler Rohith Khanna S., Dhanush Chandrasekar, Rushil Kumar, J. Faritha Banu Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development Indiacom 2025, 2025 The dynamic and volatile characteristics of weather greatly impact the scheduling decisions in personal planners and schedulers. It often causes inefficiencies and disruptions to our activities, including financial losses. Many researchers have identified that the machine learning algorithms and predictive modeling forecast climatic trends with significant accuracy. This research implements an integrated system that uses real-time API weather data sources and machine learning algorithms for intelligent planners and schedulers. The proposed system provides real-time, adaptable planner recommendations based on environment variables. This paper proposes the Support Vector Machine, XGBoost, Random Forest, Decision Tree, and Logistic Regression algorithm implementations for weather prediction and the potential impact of utilizing these algorithms in an intelligent scheduling assistant. Compared to all models, Random Forest shows the best performance by resulting in the highest accuracy of 0.99 and the lowest error of 0.19 in R² and RMSE metrics.
REGION-BASED FULLY DEEP CONVOLUTIONAL NEURAL NETWORKS ENHANCED WITH CARNIVOROUS PLANT ALGORITHM FOR PLANT DISEASE DETECTION AND CLASSIFICATION Journal of Theoretical and Applied Information Technology, 2024
Alzheimer's Disease Detection using Deep Learning Algorithm J Faritha Banu, R Kingsly Stephen, N Aditya, L C Dhanush Raaghav Proceedings of the 18th Indiacom 2024 11th International Conference on Computing for Sustainable Global Development Indiacom 2024, 2024
Federated GAN based framework for Alzheimer disease classification using finite impulse response filter techniques RK Stephen, JF Banu Neural Computing and Applications 38 (9), 300 , 2026 2026
Dengue Fever Prediction Empowered by Radial Basis Function Networks, Dynamic Mode Decomposition, and Learning-Based Foraging Algorithm ATF Banu J Journal of Computer Science 22 (4), 1298-1312 , 2026 2026
Scylla: A Novel Water Quality Prediction Model Using Machine Learning Algorithm JF Banu, RK Sridhar, R Kumar, D Chandrasekar AI-Driven Sustainable and Secure Smart Infrastructure Systems, 1-30 , 2026 2026
Machine Learning-Based Dynamic Context Real-Time Movie Recommendation System JF Banu, UK Singh, R Singh, AM Khan Multidisciplinary Advancements in Human-AI Augmentation, 173-198 , 2026 2026
AuthenShield: CAPTCHA-Free Bot Detection Through Passive User Interaction Analysis JF Banu, A Advika, S Santhanam Multidisciplinary Advancements in Human-AI Augmentation, 113-140 , 2026 2026
AuthenShield – CAPTCHA-free Bot Detection Through Passive User Interaction Analysis SS J. Faritha Banu, A Advika igi global 10.4018/979-8-3373-1987-2 book chapter , 2025 2025
Machine Learning Based Dynamic Context Real-Time Movie Recommendation System AK J.Faritha Banu, Utkarsh Kumar Singh, Raghav Singh igi global 10.4018/979-8-3373-1987-2 book chapter , 2025 2025
Enhancing Intelligent Transportation Systems in Smart Cities Using VANETs With Deep Reinforcement Transfer Learning and Explainable AI SSS Ramesh, JF Banu, VR Kavitha, T Ramesh Transactions on Emerging Telecommunications Technologies, 1-20 , 2025 2025 Citations: 8
Weather Driven Predictive Scheduling for Intelligent Planner and Scheduler R Khanna, D Chandrasekar, R Kumar, JF Banu 2025 12th International Conference on Computing for Sustainable Global … , 2025 2025
IoT Based Driver Drowsiness Detection Using Convolutional Neural Network P Nair, S Singh, J Rai, JF Banu 2025 12th International Conference on Computing for Sustainable Global … , 2025 2025
Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction V M, F Banu J 2025 International Conference on Automation and Computation (AUTOCOM), 143-148 , 2025 2025 Citations: 1
Improved Bidirectional-Long Short-Term Memory for Customer Churn Prediction in the Telecom Industry V M, F Banu J 2024 1st International Conference on Sustainability and Technological … , 2024 2024
Improved Bidirectional-Long Short-Term Memory for Customer Churn Prediction in the Telecom Industry 2024 1st International Conference on Sustainability and Technological … , 2024 2024
Diagnosing Parkinson's Disease with KNN Classifier Utilizing Speech Feature Extraction S Santhanam, A Advika, S Santhanam, JF Banu 2024 International Conference on Electronic Systems and Intelligent … , 2024 2024
Enhancing Underwater Object Detection Using Advanced Deep Learning De-Noising Techniques A Umamageswari, S Deepa, FBJ Hussain, P Shanmugam Traitement du Signal 41 (5), 2593 , 2024 2024 Citations: 8
Emerging Trends in Engineering and Technology (Volume - 8) M Bajpai, DAVS Reddy, DVL Devi, faritha banu 10.62778/int.book.454 , 2024 2024
Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction E Saraswathi, JF Banu Neural Computing and Applications 36 (23), 14395-14407 , 2024 2024 Citations: 7
Region-based fully deep convolutional neural networks enhanced with carnivores plant algorithm for plant disease detection and classification E Saraswathi, JF BANU Journal of Theoretical and Applied Information Technology 102 (9) , 2024 2024 Citations: 2
A novel probabilistic intermittent neural network (PINN) and artificial jelly fish optimization (AJFO)-based plant leaf disease detection system E Saraswathi, J Faritha Banu Journal of Plant Diseases and Protection 131 (2), 587-600 , 2024 2024 Citations: 6
Alzheimer's Disease Detection using Deep Learning Algorithm JF Banu, RK Stephen, N Aditya, LCD Raaghav 2024 11th International Conference on Computing for Sustainable Global … , 2024 2024 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Iot Based Cloud Intergrated Smart Classroom For Smart and a Sustainable Campus SM Dr Faritha Banu J, Gladiss Merlin N.R, Revathi R Procedia Computer Science 172, 77–81 , 2020 2020 Citations: 142
Artificial Intelligence Based Customer Churn Prediction Model for Business Markets EOM J. Faritha Banu ,S. Neelakandan,B.T Geetha,V. Selvalakshmi,A. Umadevi Computational Intelligence and Neuroscience 2022 , 2022 2022 Citations: 66
Enhancement in manufacturing systems using Grey-Fuzzy and LK-SVM approach TP Latchoumi, G Kalusuraman, JF Banu, TL Yookesh, TP Ezhilarasi, ... 2021 IEEE International Conference on Intelligent Systems, Smart and Green … , 2021 2021 Citations: 59
Ontology Based Image Retrieval by Utilizing Model Annotations and Content JF Banu, P Muneeshwari, K Raja, S Suresh, TP Latchoumi, S Deepan 2022 12th International Conference on Cloud Computing, Data Science … , 2022 2022 Citations: 43
Artificial intelligence with attention based BiLSTM for energy storage system in hybrid renewable energy sources VN J. Faritha Banu, Rupali Atul Mahajan , U. Sakthi , Vinay Kumar Nassa , D ... Sustainable Energy Technologies and Assessments 52 , 2022 2022 Citations: 35
Modeling of hyperparameter tuned hybrid cnn and lstm for prediction model AMB J. Faritha Banu, S. B. Rajeshwari, J. S. Kallimani, S. Vasanthi Intelligent Automation & Soft Computing 33 (3), 1393–1405 , 2022 2022 Citations: 17
Asian Research Consortium JF Banu, VG Sekar Asian Journal of Research in Social Sciences and Humanities 6 (12), 717-730 , 2016 2016 Citations: 13
Energy Aware Seagull Optimization-Based Unequal Clustering Technique in WSN Communication AKSPDVB D. Anuradha , R. Srinivasan , T. Ch. Anil Kumar , J. Faritha Banu Intelligent Automation & Soft Computing 32 (No.3), pp.1325-1341 , 2021 2021 Citations: 12
Exploring Machine Learning Algorithms for the Prediction of Dengue: A Comprehensive Review A Thirugnanam, FB Jahir Hussain Revue d'Intelligence Artificielle 37 (5), 1281-1290 , 2023 2023 Citations: 10
Utilizing Deep Convolutional Neural Networks for Multi-Classification of Plant Diseases from Image Data S Elumalai, FBJ Hussain Traitement du Signal 40 (4), 1479-1490 , 2023 2023 Citations: 10
Enhancing Intelligent Transportation Systems in Smart Cities Using VANETs With Deep Reinforcement Transfer Learning and Explainable AI SSS Ramesh, JF Banu, VR Kavitha, T Ramesh Transactions on Emerging Telecommunications Technologies, 1-20 , 2025 2025 Citations: 8
Enhancing Underwater Object Detection Using Advanced Deep Learning De-Noising Techniques A Umamageswari, S Deepa, FBJ Hussain, P Shanmugam Traitement du Signal 41 (5), 2593 , 2024 2024 Citations: 8
Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction E Saraswathi, JF Banu Neural Computing and Applications 36 (23), 14395-14407 , 2024 2024 Citations: 7
Computer Fundamentals and Programming in C (RMK) A Goel, GN Mittal, Ajay, Faritha Banu J, R Radhika Pearson Education India , 2016 2016 Citations: 7
A novel probabilistic intermittent neural network (PINN) and artificial jelly fish optimization (AJFO)-based plant leaf disease detection system E Saraswathi, J Faritha Banu Journal of Plant Diseases and Protection 131 (2), 587-600 , 2024 2024 Citations: 6
Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images. VR Kavitha, FB Jahir Hussain, P Chillakuru, P Shanmugam Traitement du Signal 41 (1) , 2024 2024 Citations: 5
Alzheimer's Disease Detection using Deep Learning Algorithm JF Banu, RK Stephen, N Aditya, LCD Raaghav 2024 11th International Conference on Computing for Sustainable Global … , 2024 2024 Citations: 4
Novel Framework for Dengue Classification and Early Recovery using Machine Learning Algorithms JF Banu, G Hariprasad, T Archana, P Srivatsan 2024 11th International Conference on Computing for Sustainable Global … , 2024 2024 Citations: 4
Study of QoS management techniques for VoiceApplications V Faritha Banu, J, Ramachandran International Journal of Computer Science and Electronics Engineering, ISSN … , 2013 2013 Citations: 4
