Usharani S

@ifet.ac.in

Associate Professor, Department of Computer Science and Engineering
IFET College of Engineering

Usharani S


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https://researchid.co/usharaniselvaraj

RESEARCH INTERESTS

Artificial intelligence, Machine Learning
54

Scopus Publications

557

Scholar Citations

14

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • Smart Contracts and Blockchain Architectures for Sustainable Resource Management Through Decentralized Traceability Transparency and Circular Value Creation
    P. Manju Bala, S. Usharani, A. Devi
    Studies in Big Data, 2026
  • Cognitive Analytics AI for Predictive Diagnostics and Neurological Forecasting in Brain Tumor Management
    S. Usharani, P. Manju Bala, A. Balachandar, A. Olukayode
    Composite Artificial Intelligence Fundamentals Challenges and Applications, 2026
    Brain tumors and neurological disorders present entirely intertwined problems concerning diagnosis and therapy-monitored activity, which at times would require a fast, sure response to improve the patient's state. This work constitutes cognitive AI-based analytics for deep learning as a mechanism for brain tumor diagnosis and its stages. It works by integrating information across different modalities—medical images, electronic health records, and molecular profiles—to simulate human thinking and find small changes and relevant clinical indicators. While CNNs were employed for image classification, transformers performed the time-series modeling and textual information that further predicated tumor types and survival time. Integration of knowledge graphs was also emphasized as it describes the correlations of symptoms of tumors with its types and dynamics thereby making it even more useful in a clinical setting. The in-depth methodology proposed intends to integrate predictive analytics and visual analytics with the use of AI algorithms to aid in predicting the dynamics of tumor growth and neurological impairment extent, as well as suggest the best possible treatment strategies for the patient. Collection for preliminary prognosis based on the operated system has been prepared and achieved much better than state-of-the-art where 87 percent accuracy has been achieved in tumor identification and 81 percent in survival prediction. The importance of multimodal integration of data with knowledge graphs creates a model to predict holistically and identify tumor features (EGFR mutations and their location) as biomarkers. The AI-driven framework includes an interactive visualization platform for clinicians supporting actionable insight decisions. These results suggest the superior performance of the proposed system over current practices and could thus transform personalized, data-driven patient care in neuro-oncology.
  • Deep Generative Models and Challenges in Synthesizing Histopathological Images for Breast Cancer Diagnosis
    P. Manju Bala, S. Usharani, A. Balachandar, Olusola Kunle Akinde
    Deep Learning Models Towards Health Informatics Management Foundations Challenges and Opportunities, 2026
    The diagnosis and prognosis of breast cancer highly rely on histopathological image analysis, which requires large-scale, high-quality annotated datasets. Collecting diverse and well-labeled histopathological images is difficult owing to privacy issues, data scarcity, and costly annotation. Deep generative models such as generative adversarial network (GAN) and variational autoencoders (VAEs) can create synthetic data, which complement histopathological datasets while maintaining patient privacy. This chapter scrutinizes the application of deep generative models for synthesizing high-fidelity histopathological images for breast cancer. Compared with applying various generative techniques, particularly conditional GANs (cGANs), StyleGAN, and CycleGAN, their contribution toward improving effectiveness in deep learning models for the classification, segmentation, and grading of breast cancer was also reviewed. Besides, the chapter examines how the synthetic data affect model generalization, domain adaptation, and bias mitigation. Among other major challenges about synthetic images are how to evaluate the quality of images, the availability of interpretation, and addressing ethical concerns. Advances made possible by synthetic data can enhance the capability of researchers and clinicians toward further robust AI-based histopathological evaluation leading toward early and accurate detection of breast cancer. In the chapter’s closing note, future endeavors relating to the possible combination of generative models with explainable artificial intelligence (XAI) and federated learning to ensure the premises of trustworthiness and scalability in medical applications are respectively sketched.
  • Composite AI for Predictive Analysis of Autism Spectrum Disorder Using Facial Features
    S. Usharani, A. Ganesh, N. Muralidharan, G. Glorindal
    Composite Artificial Intelligence Fundamentals Challenges and Applications, 2026
    This chapter presents the composite artificial intelligence (AI) techniques in the face feature analysis for the autism spectrum disorder (ASD) prediction model. This research utilizes various AI techniques like facial feature recognition and deep learning to raise the precision of diagnosis owing to the inherent difficulty of diagnosing ASD. The chapter sets forth key phases like data acquisition, preprocessing, and model construction while concentrating on the identification of relevant face traits showing their link to ASD. Such advanced models consider predictive potential, focusing on Xception and VGG19. Convolutional Neural Networks (CNNs) recognize and categorize facial features. This is done in the chapter where different performance models are assessed to find out how effective they are in discriminating ASD cases from typically developing individuals in terms of accuracy, sensitivity, and specificity. In the end, it would be comprised of Composite AI and facial recognition which would be a very promising step towards the early identification of ASD without any invasive techniques as well as more effective clinical outcomes.
  • Advancements in Deep Learning for Medical Image Representation Techniques
    S. Usharani, P. Manju Bala, A. Balachandar, G. Glorindal
    Deep Learning Models Towards Health Informatics Management Foundations Challenges and Opportunities, 2026
    At present, deep learning-based automated feature extraction, classification, and diagnosis of medical images have made it different from traditional means. This section introduces a new hybrid technique—a combination of vision transformers (ViTs) and hierarchical vision networks (HVNs) for image representation in medicine. The proposed model improves efficiency and interpretability through an adaptive multi-scale attention mechanism, which elaborately distinguishes and highlights the areas diagnostically significant by avoiding the common methods. To address the problem of data scarcity, realistic data augmentation is achieved through a progressive generative adversarial network (P-GAN). This will allow better model generalization on several medical imaging datasets. The system works out for an end-to-end solution that demonstrates scaled-up real-life clinical practice, thereby limiting self-supervised contrastive learning for labeled data. When tested with different datasets such as histology, MRI, and CT, the proposed technique shows improved explainability, robustness, and classification accuracies compared to the state-of-the-art. The clinical decision-making process bolstered further by interpretability tools like class activation maps (CAMs) that give visual differences concerning model decisions will be indicated as a game-changing hybrid deep learning approach for personalized healthcare and accelerating diagnostic workflows.
  • Block Chain for Supply Chain Transparency and Product Traceability in a Circular Economy
    S. Usharani, P. Manju Bala, A. Devi, A. Balachandar, G. Glorindal
    Studies in Big Data, 2026
  • Integrating Imaging and Genomic Data with Composite AI to Enhance Breast Cancer Diagnosis and Early Detection
    P. Manju Bala, S. Usharani, A. Balachandar, Sunday Adeola Ajagbe, Matthew Olusegun Adigun
    Composite Artificial Intelligence Fundamentals Challenges and Applications, 2026
    Early detection of breast cancer will increase patient outcomes and survival, which will aid in improved patient care. However, integrating genetic data with medical imaging will enhance our understanding of the condition, resulting in a more accurate diagnosis and tailored therapy for the patient. Multiple artificial intelligence techniques coupled to create composite AI can enhance the interpretation of the aggregated data and pave the way for new, cutting-edge research directions in breast cancer. In this study, we present an integrated artificial intelligence system that improves breast cancer detection by combining data from genomics and imaging techniques. Our solution is a hybrid strategy that employs CNN models for image data and machine learning models (SVMs and Random Forests) for processing genomic data. We employ stacking, an additional ensemble learning technique, to merge the predictions of the CNN and genomic data models. As a result, our method produces a reliable and accurate diagnosis model by capturing unique imaging and genetic data. By capturing characteristics unique to picture data and genetic traits, this hybrid technique enables a more potent prediction model. Datasets that included genetic profiles with mammography pictures obtained from publicly accessible depositories such as TCGA and TCIA were used to evaluate the algorithms. Using the imaging data, the CNN model demonstrated an 88% accuracy rate in distinguishing between benign and malignant samples. An accuracy of 85% was obtained by analyzing the genomic data using SVM findings. With a classification accuracy of 92%, the composite model, which combined the output of the CNN and SVM models, showed a notable increase over the individual models’ outputs. The area under the curve increased by over 6% when cross-validation methods and AUC-ROC performance indicators were used, compared to the CNN model alone and the composite model. Performance assessment has shown that adding genetic and imaging data to composite AI greatly improves the precision and resilience of breast cancer detection. This illustrates the revolutionary potential of AI in the early diagnosis of breast cancer and opens the door for more sophisticated diagnostic and personalized treatment options.
  • AI-Driven Integration and Workflow Optimization in Modern Healthcare Facilities
    S. Usharani, Manju Bala P., A. Devi, K. Rajkumar, D. Saravanan
    Evaluation and Assessment of AI Driven Systems in Hospitals, 2025
    This chapter explores AI services for administrative and clinical workflows, emphasizing measurable gains in patient experience, efficiency, and diagnostic accuracy. The project applied predictive analytics for bed occupancy and inventory, NLP for clinical documentation, AI for medical imaging, and automation for routine tasks. A structured framework guided data collection, model building, process mapping, deployment, and feedback. Cybersecurity, interoperability, and ethics ensured responsible use. Case studies showed X-ray accuracy improved from 88.5% to 94.2%, pneumonia sensitivity from 86.1% to 91.8%, and specificity from 89.4% to 92.6%. NLP entity extraction F1 scores rose from 0.83 to 0.89, and AUC-ROC from 0.91 to 0.96. Patient wait times dropped 42% (48→28 mins), no-shows fell 60% (15 to 6%), and admin task time declined 40% (35 to 21 mins). Inventory refill shrank 38% (9 to 5.5 hrs), and ICU bed forecasts had a 2.1 unit MAE. These results confirm that AI, applied through ethical frameworks, drives measurable hospital improvements.
  • Innovative Approach to Duplicate Image Detection: Leveraging Graph Neural Networks and Spectral Clustering for Optimal Performance
    Usharani Selvaraj, Dhanalakshmi Kaliratnam
    IETE Journal of Research, 2025
    Visual information in smart applications consumes extensive storage due to its high density and frequent replication, leading to increased retrieval complexity and latency. Existing duplicate image detection methods still rely on manual inspection and face copyright concerns, creating the need for a more efficient automated solution. To address these issues, this study proposes an Optimised Locally Inspired High-Level Graph Convolutional Neural Network (OLI-HGCNN) model for accurate image duplication detection. Four benchmark datasets-D-Rep, CoMoFoD, CASIA CMFD, and COVERAGE are employed to collect input images, containing both original and duplicate samples. A density-based feature extraction module is first applied to generate mask images that assist in identifying authentic samples. Subsequently, a Locally Scaled Spectral Clustering model is used to detect identical images based on the generated masks. During the detection stage, a High-Level Graph Convolutional Neural Network extracts global dependencies among images, where high-level node representations capture more informative and discriminative features. The Parametric Rectified Linear Unit (PReLU) activation function enhances computational flexibility. The proposed framework finally classifies images as non-duplicate or duplicate. Experimental results demonstrate superior performance with a deduplication rate of 0.38 bytes and a complexity rate of 0.13 bits.
  • Securing Patient Data in Healthcare with Quantum Cryptography in the Quantum Era
    P. Manju Bala, S. Usharani, A. Balachandar
    Quantum Computing the Future of Information Processing, 2025
    The rapid digitization of healthcare has greatly improved the efficiency and accessibility of medical services, but it has also brought significant vulnerabilities in data security. As quantum computing progresses, it threatens to render traditional encryption methods obsolete, potentially exposing complex patient data to unprecedented cyber extortions. Quantum cryptography, leveraging the philosophies of quantum mechanism, finds a robust solution to these challenges by creating fundamentally secure communication channels. This chapter delves into the implementation and impact of quantum cryptography on securing patient data in the healthcare sector, focusing on the tangible outcomes of such integration. We start by examining the current landscape of healthcare data security, emphasizing the weaknesses of conventional encryption in the aspect of quantum developments. The core technology discussed is quantum key distribution (QKD), which employs quantum entanglement and superposition to create secure encryption keys. These keys are virtually immune to eavesdropping, as any interception attempt disturbs the quantum state and becomes detectable. Our analysis details the practical steps for implementing QKD in healthcare systems. This includes the deployment of quantum cryptographic hardware, integration with existing IT infrastructure, and the establishment of secure quantum communication channels. We discuss the requirements for ensuring compatibility and the necessary adjustments to current encryption protocols to accommodate QKD. The chapter highlights the expected outcomes of implementing quantum cryptography in healthcare. Enhanced data security is the foremost benefit, with patient data becoming highly resistant to both current and future cyber threats. We quantify the improvements in data integrity, confidentiality, and availability, illustrating how quantum cryptography mitigates threats related with information cracks and unlawful access.
  • Towards Reliable AI-Based Assessment: Modular Scoring and Hallucination Control
    K. Rajkumar, Devikanniga Devarajan, Kavitha S J, S. Usharani
    2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2025, 2025
  • Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications
    S. Usharani, P. Manju Bala, T. Ananth kumar, G. Glorindal Selvam
    Targeted Chemotherapy with Personalized Immunotherapy an AI Approach, 2025
  • Dynamic Multi-Agent Reinforcement Learning Based Load Balancing on Software Defined Networking
    S. Wilson Prakash, S. Usharani, R. Rajesh, Rajkumar K
    2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025
  • Image Classification in Medical Diagnosis for Diabetic Retinopathy-Deep Learning
    Manju Bala. P, Usharani S, Sangeetha S, Prithisha S
    International Conference on Advanced Computing Technologies Icoact 2025, 2025
  • Deep Learning-Based Ultrasound Analysis Using Explainable Artificial Intelligence (XAI) Methods for Breast Cancer
    P. Manju Bala, S. Usharani, A. Balachandar, R. Rajmohan
    Mathematical Methods in the Digital Age, 2025
  • Enhanced Detection of Multiple Sclerosis Using Recurrent Slice-wise Attention Network (RSANet) on Brain MRI Scans
    Lakshmi T, Manju Bala P, Suriyapriya S, Usharani S
    2025 International Conference on Emerging Technologies in Engineering Applications Icetea 2025 Proceedings, 2025
  • MediBloom AI-Powered Public Health Assistant Revolutionizing Disease Awareness
    S. Usharani, P. Manju Bala, R. Rajesh, S. Ruddhra, V. Kiruthigaa, K. Rajkumar
    2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2025, 2025
  • YOLOX Driven Smart Surveillance for Real Time Intelligent Object Detection and Anomaly Monitoring
    S. Usharani, R. Rajesh, D.Annal Priyadarshini, R. Vijayabharathi, P.Manju Bala, R. Muthukumaran
    2025 5th International Conference on Intelligent Technologies Conit 2025, 2025
  • A Privacy-Preserving Phishing Detection System Using Reinforcement Learning and Graph-Based Hybrid Models
    S. Usharani, P. Manju Bala, A. Balachandar, Ruddhra, K. Ramya, P. Nithisri
    International Conference on Computing Intelligence and Application Ciacon 2025, 2025
  • Optimizing Crop Yields with Multilayer Perceptrons: A Data-Driven Approach
    N. Nivetha, S. Usharani
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation
    P. Manju Bala, S. Priyadharshini, S. Usharani, A. Balachandar
    2024 3rd International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2024, 2024
  • Enhanced Image Processing for Automobile Accident Detection Using Deep Learning
    S. Usharani, J. Dhanushkumar, P. Manju Bala, A. Balachandar
    2024 3rd International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2024, 2024
  • An image storage duplication detection method using recurrent learning for smart application services
    S. Usharani, K. Dhanalakshmi
    Journal of Supercomputing, 2023
  • A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features
    S. Usharani, K. Dhanalakshmi
    2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2023, 2023
  • Monitoring the Student's Entry and Exit Time in the Classroom
    S. Usharani, K. Vijayaragavan, A. Balachandar, P. Manju Bala
    2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
  • Energy Efficiency in Wireless Sensor Network using Red Fox Optimization Algorithm
    A. Balachandar, S. Usharani, P. Manju Bala, P.V. Akshaya
    Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems Icears 2023, 2023
  • Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray
    P. Manju Bala, S. Usharani, R. Rajmohan, T. Ananth Kumar, A. Balachandar
    Eai Springer Innovations in Communication and Computing, 2023
  • Investigation of energy optimization for spectrum sensing in distributed cooperative IoT network using deep learning techniques
    M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani, P. Manju Bala
    Hybrid Intelligent Approaches for Smart Energy Practical Applications, 2022
  • Smart energy management techniques in industries 5.0
    S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan, M. Pavithra
    Hybrid Intelligent Approaches for Smart Energy Practical Applications, 2022
  • Blockchain-Based IoT Architecture for Software-Defined Networking
    P. Manju Bala, S. Usharani, T. Ananth Kumar, R. Rajmohan, M. Pavithra
    Eai Springer Innovations in Communication and Computing, 2022
  • Demystifying digital transformation technologies in healthcare
    Demystifying Graph Data Science Graph Algorithms Analytics Methods Platforms Databases and Use Cases, 2022
  • Blockchain Technology Use Cases in Healthcare Management: State-of-the-Art Framework and Performance Evaluation
    S. Usharani, P. Manju Bala, R. Rajmohan, T. Ananth Kumar, M. Pavithra
    Eai Springer Innovations in Communication and Computing, 2022
  • Semantic knowledge graph technologies in data science
    Demystifying Graph Data Science Graph Algorithms Analytics Methods Platforms Databases and Use Cases, 2022
  • Integrated Implementation of Hybrid Deep Learning Models and IoT Sensors for Analyzing Solider Health and Emergency Monitoring
    S. Usharani, R. Rajmohan, P. Manju Bala, D. Saravanan, P. Agalya, D. Raghu Raman
    1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2022, 2022
  • Tariff Stroll for Monetary Trading Using Deep Learning
    N. Sangeetha, S. Jayalakshmi, D. Raghu Raman, D. Saravanan, S. Usharani
    2021 International Conference on System Computation Automation and Networking Icscan 2021, 2021
  • Enhancing Online Store in Aggregator Model for SME in Multi Categories using Django Channels
    R. Inba, D. Raghu Raman, S. Jayalakshmi, D. Saravanan, S. Usharani
    2021 International Conference on System Computation Automation and Networking Icscan 2021, 2021
  • Mobile Application for Doctor Appointment Scheduling
    S. Usharani, S. Prithivi, S. Sharmila, P. Manju Bala, T. Ananth Kumar, R. Rajmohan
    2021 International Conference on System Computation Automation and Networking Icscan 2021, 2021
  • Deep learning technique based visually impaired people using YOLO V3 framework mechanism
    A. Balachandar, E. Santhosh, A. Suriyakrishnan, N. Vigensh, S. Usharani, P. Manju Bala
    2021 3rd International Conference on Signal Processing and Communication Icpsc 2021, 2021
  • Dynamic analysis on crypto-ransomware by using machine learning: Gandcrab ransomware
    S Usharani, P Manju Bala, M Martina Jose Mary
    Journal of Physics Conference Series, 2021
  • Detect the replication attack on wireless sensor network by using intrusion detection system
    P. Manju Bala, S. Usharani, V. Abarna
    Journal of Physics Conference Series, 2021
  • Secure violent detection in android application with trust analysis in google play
    D. Saravanan, J. Feroskhan, R. Parthiban, S. Usharani
    Journal of Physics Conference Series, 2021
  • Mathematical Models for Computer Vision in Cardiovascular Image Segmentation
    S. Usharani, K. Dhanalakshmi, Manju Bala, M. Pavithra, R. Rajmohan
    Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications, 2021
  • Pregnancy Women-Smart Care Intelligent Systems: Patient Condition Screening, Visualization and Monitoring with Multimedia Technology
    S. Usharani, P. Manju Bala, R. Rajmohan, T. Ananth Kumar, S. Arunmozhi Selvi
    Intelligent Interactive Multimedia Systems for E Healthcare Applications, 2021
  • Integrated Mathematical Modelling and Analysis of Paddy Crop Pest Detection Framework Using Convolutional Classifiers
    R. Rajmohan, M. Pavithra, P. Praveen Kumar, S. Usharani, P. Manjubala, N. Padmapriya
    Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications, 2021
  • Voice Based Form Filling System for Visually Challenged People
    S. Usharani, P. Manju Bala, R. Balamurugan
    2020 International Conference on System Computation Automation and Networking Icscan 2020, 2020
  • IDS based fake content detection on Social Network Using Bloom Filtering
    P. Manju Bala, S. Usharani, M. Aswin
    2020 International Conference on System Computation Automation and Networking Icscan 2020, 2020
  • Detection of ransomware in static analysis by using Gradient Tree Boosting Algorithm
    Martina Jose Mary M., Usharani S., Manju Bala P., S. G. Sandhya
    2020 International Conference on System Computation Automation and Networking Icscan 2020, 2020
  • Classification of hyper spectral remote sensing data to categorize crop condition, stage and variety
    International Journal of Scientific and Technology Research, 2020
  • De-duplication techniques in centralised billing systems
    International Journal of Scientific and Technology Research, 2020
  • De-duplication techniques: A study
    International Journal of Recent Technology and Engineering, 2019
  • Handover priority to the data at knob level in vanet
    International Journal of Recent Technology and Engineering, 2019
  • Elegant app to endorse indian merchandise intended for monetary maturity
    International Journal of Recent Technology and Engineering, 2019
  • Lossy node elimination based on link stability algorithm in wireless sensor network
    International Journal of Recent Technology and Engineering, 2019
  • Social data analysis for predicting next event
    M Deiva Ragavi, S. Usharani
    2014 International Conference on Information Communication and Embedded Systems Icices 2014, 2015

RECENT SCHOLAR PUBLICATIONS

  • Block Chain for Supply Chain Transparency and Product Traceability in a Circular Economy
    S Usharani, PM Bala, A Devi, A Balachandar, G Glorindal
    Blockchain Innovations for a Sustainable Circular Economy, 279-298 , 2026
    2026
  • Smart Contracts and Blockchain Architectures for Sustainable Resource Management Through Decentralized Traceability Transparency and Circular Value Creation
    PM Bala, S Usharani, A Devi
    Blockchain Innovations for a Sustainable Circular Economy, 299-323 , 2026
    2026
  • Sustainable Blockchain and WSN Architecture for the Metaverse
    P Manju Bala, S Usharani, A Balachandar, A Olukayode
    Blockchain Enabled Metaverse for Smart Wireless Sensor Networks, 243-271 , 2026
    2026
  • MetaBlockSphere Self Evolving Framework (MBSF) for Scalable and Interoperable Blockchain Metaverse Systems
    S Usharani, PM Bala, A Devi, A Balachandar, G Glorindal
    Blockchain Enabled Metaverse for Smart Wireless Sensor Networks, 273 , 2026
    2026
    Citations: 1
  • Deep Generative Models and Challenges in Synthesizing Histopathological Images for Breast Cancer Diagnosis
    PM Bala, S Usharani, A Balachandar, OK Akinde
    Deep Learning Models towards Health Informatics Management, 84-100 , 2026
    2026
    Citations: 1
  • Advancements in Deep Learning for Medical Image Representation Techniques
    S Usharani, PM Bala, A Balachandar, G Glorindal
    Deep Learning Models towards Health Informatics Management, 28-45 , 2026
    2026
    Citations: 1
  • AI-Driven Integration and Workflow Optimization in Modern Healthcare Facilities
    S Usharani, A Devi, K Rajkumar, D Saravanan
    Evaluation and Assessment of AI-Driven Systems in Hospitals, 235-278 , 2026
    2026
    Citations: 1
  • Innovative Approach to Duplicate Image Detection: Leveraging Graph Neural Networks and Spectral Clustering for Optimal Performance
    U Selvaraj, D Kaliratnam
    IETE Journal of Research, 1-12 , 2025
    2025
  • Towards Reliable AI-Based Assessment: Modular Scoring and Hallucination Control
    K Rajkumar, D Devarajan, S Usharani
    2025 Fourth International Conference on Smart Technologies and Systems for … , 2025
    2025
  • MediBloom AI-Powered Public Health Assistant Revolutionizing Disease Awareness
    S Usharani, PM Bala, R Rajesh, S Ruddhra, V Kiruthigaa, K Rajkumar
    2025 Fourth International Conference on Smart Technologies and Systems for … , 2025
    2025
  • Advanced Privacy Measures for Data Sharing in Federated Learning Networks
    P Manju Bala, S Usharani, A Balachandar, A Olukayode
    Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex … , 2025
    2025
  • Enhanced Privacy Preserving Deep Learning Using Blockchain and Federated Learning
    S Usharani, P Manju Bala, A Balachandar, G Glorindal
    Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex … , 2025
    2025
    Citations: 1
  • Automated 3D U‐Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI‐Driven Cancer Research Applications
    S Usharani, PM Bala, TA kumar, GG Selvam
    Targeted Chemotherapy with Personalized Immunotherapy: An AI Approach, 279-307 , 2025
    2025
    Citations: 1
  • A Privacy-Preserving Phishing Detection System Using Reinforcement Learning and Graph-Based Hybrid Models
    S Usharani, PM Bala, A Balachandar, K Ramya, P Nithisri
    2025 International Conference on Computing, Intelligence, and Application … , 2025
    2025
  • YOLOX Driven Smart Surveillance for Real Time Intelligent Object Detection and Anomaly Monitoring
    S Usharani, R Rajesh, DA Priyadarshini, R Vijayabharathi, PM Bala, ...
    2025 5th International Conference on Intelligent Technologies (CONIT), 1-6 , 2025
    2025
    Citations: 2
  • Enhanced Detection of Multiple Sclerosis Using Recurrent Slice-wise Attention Network (RSANet) on Brain MRI Scans
    T Lakshmi, S Suriyapriya, S Usharani
    2025 International Conference on Emerging Technologies in Engineering … , 2025
    2025
    Citations: 1
  • Dynamic Multi-Agent Reinforcement Learning Based Load Balancing on Software Defined Networking
    SW Prakash, S Usharani, R Rajesh
    2025 International Conference on Networks and Cryptology (NETCRYPT), 359-364 , 2025
    2025
  • Capitalizing on Bagging, Boosting, and Voting Classifiers to Optimize Placement Outcomes: Ensemble Techniques in Educational Data Prediction
    DKC Sekhar, S Usharani, R Priya, DKS Raju, DD Saravanan
    2025
  • 12 Deep Learning-Based Ultrasound Analysis Using Explainable Artificial Intelligence (XAI) Methods for Breast Cancer
    PM Bala, S Usharani, A Balachandar, R Rajmohan
    Math Optimization for Artificial Intelligence: Heuristic and Metaheuristic … , 2025
    2025
  • Securing patient data in healthcare with quantum cryptography in the quantum era
    PM Bala, S Usharani, A Balachandar
    Quantum Computing, 268-282 , 2025
    2025
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Prognosis of chronic kidney disease (CKD) using hybrid filter wrapper embedded feature selection method
    R Parthiban, S Usharani, D Saravanan, D Jayakumar, DU Palani, ...
    European Journal of Molecular & Clinical Medicine 7 (9), 2511-2530 , 2021
    2021
    Citations: 51
  • Dynamic analysis on crypto-ransomware by using machine learning: Gandcrab ransomware
    S Usharani, P Manju Bala, M Martina Jose Mary
    Journal of Physics: Conference Series 1717 (1), 012024 , 2021
    2021
    Citations: 35
  • Blockchain-based IoT architecture for software-defined networking
    P Manju Bala, S Usharani, T Ananth Kumar, R Rajmohan, M Pavithra
    Blockchain, artificial intelligence, and the internet of things … , 2021
    2021
    Citations: 25
  • A study on application of various artificial intelligence techniques on internet of things
    DR Raman, D Saravanan, R Parthiban, DU Palani, DDS David, ...
    European Journal of Molecular & Clinical Medicine 7 (9), 2531-2557 , 2021
    2021
    Citations: 25
  • An energy-efficient trust based secure data scheme in wireless sensor networks
    DU Palani, D Raghuraman, DD StalinDavid, R Parthiban, S Usharani, ...
    European Journal of Molecular & Clinical Medicine 7 (9) , 2021
    2021
    Citations: 25
  • Medical wireless sensor network coverage and clinical application of MRI liver disease diagnosis
    DDS David, R Parthiban, D Jayakumar, S Usharani, D RaghuRaman, ...
    European Journal of Molecular & Clinical Medicine 7 (9), 2559-2571 , 2021
    2021
    Citations: 24
  • Secure violent detection in Android application with trust analysis in Google Play
    D Saravanan, J Feroskhan, R Parthiban, S Usharani
    Journal of Physics: Conference Series 1717 (1), 012055 , 2021
    2021
    Citations: 23
  • Industrialized service innovation platform based on 5g network and machine learning
    S Usharani, D Jayakumar, DU Palani, D Raghuraman, R Parthiban, ...
    European Journal of Molecular & Clinical Medicine 7 (11), 5684-5703 , 2020
    2020
    Citations: 22
  • Certain investigation on monitoring the load of short distance orienteering sports on campus based on embedded system acceleration sensor
    D Jayakumar, DU Palani, D Raghuraman, DD StalinDavid, D Saravanan, ...
    European Journal of Molecular & Clinical Medicine 7 (9) , 2021
    2021
    Citations: 20
  • An Energy Efficient Traffic-Less Channel Scheduling Based Data Transmission In Wireless Networks
    D Saravanan, DDS David, S Usharani, D Raghuraman, D Jayakumar, ...
    European Journal of Molecular & Clinical Medicine 7 (11), 5704-5722 , 2020
    2020
    Citations: 19
  • Lossy node elimination based on link stability algorithm in wireless sensor network
    U Palani, D Saravanan, R Parthiban, S Usharani
    International Journal of Recent Technology and Engineering (IJRTE) 7 (6S5) , 2019
    2019
    Citations: 19
  • Detection of ransomware in static analysis by using Gradient Tree Boosting Algorithm
    S Usharani, SG Sandhya
    2020 International Conference on System, Computation, Automation and … , 2020
    2020
    Citations: 18
  • Handover priority to the data at knob level in vanet
    SG Sandhya, D Saravanan, U Palani, S Usharani
    International Journal of Recent Technology and Engineering (IJRTE) 7 (6S5) , 2019
    2019
    Citations: 18
  • Deep learning technique based visually impaired people using YOLO V3 framework mechanism
    A Balachandar, E Santhosh, A Suriyakrishnan, N Vigensh, S Usharani, ...
    2021 3rd International Conference on Signal Processing and Communication … , 2021
    2021
    Citations: 15
  • Security improvement of dropper elimination scheme for IoT based wireless networks
    M Sudha, D Saravanan, S Usharani
    International Journal of Engineering Trends and Technology (IJETT) 45 (3) , 2017
    2017
    Citations: 13
  • Mobile application for doctor appointment scheduling
    S Usharani, S Prithivi, S Sharmila, PM Bala, TA Kumar, R Rajmohan
    2021 International Conference on System, Computation, Automation and … , 2021
    2021
    Citations: 12
  • Pregnancy women—Smart care intelligent systems: Patient condition screening, visualization and monitoring with multimedia technology
    S Usharani, PM Bala, R Rajmohan, TA Kumar, SA Selvi
    Intelligent Interactive Multimedia Systems for e-Healthcare Applications … , 2021
    2021
    Citations: 11
  • A systematic approach to agricultural drones using a machine learning model
    S Arunmozhiselvi, TA Kumar, PM Bala, S Usharani, G Glorindal
    Machine Learning Approaches and Applications in Applied Intelligence for … , 2022
    2022
    Citations: 10
  • Furtive video recorder using intelligent vehicle with the help of android mobile
    D Saravanan, R Parthiban, S Usharani, KS Kumar
    Int. J. Pure Appl. Math 119, 975-981 , 2018
    2018
    Citations: 9
  • Smart energy management techniques in industries 5.0
    S Usharani, P Manju Bala, T Ananth Kumar, R Rajmohan, M Pavithra
    Hybrid Intelligent Approaches for Smart Energy: Practical Applications, 225-252 , 2022
    2022
    Citations: 8