J Anitha Ruth

@srmist.edu.in

Associate Professor, Science & Humanities
SRM INSTITUTE OF SIENCE AND TECHNOLOGY

J Anitha Ruth


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https://researchid.co/janitharuth

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science
31

Scopus Publications

115

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Optimized deep learning framework for periodontal disease severity prediction and treatment recommendation
    R. Kausalya, J. Anitha Ruth
    Biomedical Signal Processing and Control, 2026
  • Machine Learning-Based Analysis of Tweets Concerning Women's Safety
    Vijayalakshmi G. V. Mahesh, J. Anitha Ruth, R. Chandra Prabha
    Lecture Notes in Networks and Systems, 2026
  • EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation
    M. Saraniya, J. Anitha Ruth
    Discover Artificial Intelligence, 2025
    The outcome of In Vitro Fertilisation (IVF) success depends heavily on accurate embryo grading, which we have performed manually for many years. The authors develop EmbryoNet-VGG16, a system that functions as an automated embryo quality evaluation tool, combining Otsu segmentation with a modified Visual Geometry Group-16 (VGG16) Convolutional Neural Network (CNN) architecture. Training on 84 synthesised embryo pictures from a balanced dataset allowed our model to learn better generalisation. The healthcare imaging process begins with Otsu thresholding segmentation of embryo pictures and continues with the application of our 16-layer CNN model for embryonic quality assessment. The network contains specialised convolutional layers that identify important quality indicators through the analysis of border characteristics and structural integrity. Our EmbryoNet-VGG16 achieves superior classification results compared to traditional machine learning models, as indicated by an accuracy of 88.1%, with a precision of 0.90 and a recall of 0.86. This outperforms Random Forest and Decision Trees, as well as Logistic Regression models, which yield 83.41%, 82.31%, and 77.42%, respectively. EmbryoNet-VGG16 shows reliable good and poor embryo segregation by extracting quality features from manual expert assessments. The implementation of an automated system in IVF clinics would create standardised embryo assessment protocols that enhance treatment success rates while alleviating the time-consuming requirements of manual assessment processes.
  • Ebola optimization based spiking neural network for automatic hate speech recognition
    A. Meenakshi, J. Anitha Ruth
    International Journal of Information Technology Singapore, 2025
  • Clinical-ready CNN framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency
    G. Inbasakaran, J. Anitha Ruth
    Intelligence Based Medicine, 2025
    : Purpose This study develops a computationally efficient Convolutional Neural Network (CNN) for lung cancer classification in Computed Tomography (CT) images, addressing the critical need for accurate diagnostic tools deployable in resource-constrained clinical settings. Methods Using the IQ-OTH/NCCD dataset (1,190 CT images: normal, benign, and malignant classes from 110 patients), we implemented systematic architecture optimization with strategic data augmentation to address class imbalance and limited dataset challenges. Patient-level data splitting prevented leakage, ensuring valid performance metrics. The model was evaluated using 5-fold cross-validation and compared against established architectures using McNemar's test for statistical significance. Results The optimized CNN achieved 94% classification accuracy with only 4.2 million parameters and 18ms inference time. Performance significantly exceeded AlexNet (85%), VGG-16 (88%), ResNet-50 (90%), InceptionV3 (87%), and DenseNet (86%) with p<0.05. Malignant case detection showed excellent clinical metrics (precision: 0.96, recall: 0.95, F1-score: 0.95), critical for minimizing false negatives. Ablation studies revealed data augmentation contributed 6.6% accuracy improvement, with rotation and translation proving most effective. The model operates 4.3× faster than ResNet-50 while using 6× fewer parameters, enabling deployment on standard clinical workstations with 4-8GB GPU memory. Conclusions Carefully optimized CNN architectures can achieve superior diagnostic performance while meeting computational constraints of real-world medical settings. Our approach demonstrates that systematic optimization strategies effectively balance accuracy with clinical deployment feasibility, providing a practical framework for implementing AI-assisted lung cancer detection in resource-limited healthcare environments. The model's high sensitivity for malignant cases positions it as a valuable clinical decision support tool.
  • Deep learning-based embryo quality assessment: A dual-branch CNN model integrating morphological and spatial features
    M. Saraniya, J. Anitha Ruth
    Intelligence Based Medicine, 2025
    Background The assessment of embryo quality on Day 3 plays a crucial role in enhancing in vitro fertilization (IVF) outcomes. The existing embryo grading method relies on human judgment, resulting in unreliable results across various assessments due to observer subjectivity and variability. Methods This research presents a dual-branch convolutional neural network (CNN) that combines spatial and morphological data features to perform an objective evaluation of embryo quality. The modified EfficientNet architecture within the first branch extracts deep spatial features from embryo images. The second branch analyzes morphological parameters obtained through bounding box analysis of symmetry scores and fragmentation percentages. The integrated features from both branches are processed by SoftMax-activated fully connected layers for quality grade classification. Results Experiments conducted on 220 embryo images from the Kaggle World Championship 2023 Embryo Classification competition demonstrate outstanding performance. The proposed system achieved 94.3% accuracy, outperforming specialised embryo evaluation techniques (88.5%-92.1%) and standard CNN structures, including VGG-16 (79.2%), ResNet-50 (80.8%), and MobileNetV2 (82.1%). The model achieved a precision of 0.849, a recall of 0.900, and an F1-score of 0.874. The segmentation methodology achieved 95.2% bounding box accuracy, ensuring trustworthy morphological feature extraction. Conclusions The dual-branch architecture provides a performance-efficiency equilibrium (8.3M parameters, 4.5 hours training time) suitable for clinical utilization. This method advances embryo assessment standards through objective evaluation techniques, minimising observer subjectivity while maximising IVF success rates through improved embryo screening.
  • Detection of Brain Tumor Using Machine Learning Model
    R. Uma, P. Ramkumar, C. Sivaprakash, J. Anitha Ruth, Sa.Viswavardinii
    Brain Informatics Technology, 2025
    Cancers pose a threat to human life when they arise in any part of the body, but they are more harmful when they arise in the brain. To save a life, it is best to diagnose and treat it early on. This research offers a thorough method for predicting brain tumors through the use of deep learning and transfer learning strategies, which are implemented in Python utilizing the TensorFlow, Keras libraries, and Flask framework. The process includes creating the model, augmenting the data, training, testing, and validating it. The dataset is made up of brain MRI pictures that have been enhanced with additional data to enhance model performance. The pre-trained image dataset serves as the foundation for feature extraction, and a bespoke dense layer is used to predict the tumor. The model achieves an impressive accuracy of roughly 92.94% after being trained and assessed over 15 epochs. The algorithm is trained to predict the tumor based on a single MRI scan from the image database.
  • Early prediction of cardiac arrest using data mining algorithms
    P. Ramkumar, R. Uma, D. Sivakumar, J. Anitha Ruth
    Artificial Intelligence Transformations for Healthcare Applications Medical Diagnosis Treatment and Patient Care, 2024
    Cardiac arrest is a potentially fatal loss of heart function that occurs suddenly and without warning. Predicting cardiac arrest early could increase the likelihood of survival and allow for prompt treatment. The discipline of computer science known as “datamining” focuses on the process of gleaning useful information from massive databases. Algorithms for data mining can be used to look for trends in records that can indicate a cardiac arrest. Patients at high risk of cardiac arrest due to their medical history, lifestyle choices, or other variables can be pinpointed, for instance, with the help of data mining algorithms. Prediction of cardiac arrest using data mining techniques is discussed in this research. The chapter talks about the many data mining methods that have been employed for this, and the studies that have evaluated their efficacy. The chapter also covers the difficulties of employing data mining for early prediction of cardiac arrest, as well as potential future research avenues.
  • COVID-19 contamination extraction from CT images using an adaptive network
    Poonguzhali Arunachalam, P. Ramkumar, R. Uma, J. Anitha Ruth
    Artificial Intelligence Transformations for Healthcare Applications Medical Diagnosis Treatment and Patient Care, 2024
    The COVID-19 pandemic is one of the most significant threats to the general population's health in the 21st century. In this study, a novel meta-learning based FSS model is proposed. This model is realized as an adaptive relation network built on Deeplabv3+ for training the support sets and a convolutional network with swish activations functions for non-linear metric learning. The performance of this model that was trained using supervised and semi-supervised learning algorithms on two public datasets is significantly better. This model obtains a global accuracy of 0.8396 for ground glass opacity (GGO) and consolidation segmentation and 0.9996 for entire lung infection segmentations correspondingly. In addition, the model that was proposed generalizes well with data that has not yet been seen and has the potential to be expanded to the identification of other infections in image volumes that are rendered in three dimensions and four dimensions.
  • Machine learning and cryptographic solutions for data protection and network security
    Meenakshi, A., Uma, R., Visalakshi, P., Mahesh, Vijayalakshmi G. V. 1978-, Ruth, J. Anitha
    Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024
    "In the relentless battle against escalating cyber threats, data security faces a critical challenge - the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography.Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats."--
  • A smart anomaly detection method in cyber physical systems using machine learning
    P. Ramkumar, B. Shadaksharappa, R. Uma, J. Anitha Ruth, R. Valarmathi
    Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024
  • Secure data transmission in the wsn sector utilizing a heuristic multi-level clustering mechanism with dynamic trust computation
    Uma R., P. Ramkumar, J. Anitha Ruth, R. Valarmathi, C. Vinola
    Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024
  • Preface
    Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024
  • Innovative machine learning applications for cryptography
    Innovative Machine Learning Applications for Cryptography, 2024
  • Preface
    Innovative Machine Learning Applications for Cryptography, 2024
  • Prediction of Embryo Selection Using Efficient Otsu Segmentation for in- Vitro Fertilization Techinques
    M. Saraniya, J. Anitha Ruth
    Communications in Computer and Information Science, 2024
  • Two Stage Machine Learning Framework to Identify Periodontitis and Dental Caries
    R. Kausalya, J. Anitha Ruth
    4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024
  • Optimized Heuristic Technique for Task Scheduling in Secure Cloud Storage Environment
    S. Maheshwari, J. Anitha Ruth
    Proceedings of the 2024 13th International Conference on System Modeling and Advancement in Research Trends Smart 2024, 2024
  • Implementation of Ensemble Predictive Models for Parkinson’s Disease Detection
    J. Anitha Ruth, Vijayalakshmi G. V. Mahesh, R. Uma
    Lecture Notes in Networks and Systems, 2024
  • Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection
    J. Anitha Ruth, R. Uma, A. Meenakshi, P. Ramkumar
    Neural Processing Letters, 2022
  • Automatic classification of white blood cells using deep features based convolutional neural network
    A. Meenakshi, J. Anitha Ruth, V. R. Kanagavalli, R. Uma
    Multimedia Tools and Applications, 2022
  • Investigation of deep fake images using pre-trained CNN frameworks
    Anitha Ruth J., Uma R., Vijayalakshmi G. V. Mahesh, P. Ramkumar
    Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks, 2022
  • Genome-Wide Autism Prediction
    R. Uma, P. Ramkumar, J. Anith Ruth, R. Valarmathi
    Aip Conference Proceedings, 2022
  • A Hierarchical Machine Learning Frame Work to Classify Breast Tissue for Identification of Cancer
    J. Anitha Ruth, Vijayalakshmi G. V. Mahesh, R. Uma, P. Ramkumar
    Lecture Notes in Electrical Engineering, 2022
  • Prediction of Lung Cancer using Data Mining Techniques
    R Uma, P. Ramkumar, J Anith Ruth, R Valarmathi, M.Sheela Devi
    3rd International Conference on Smart Electronics and Communication Icosec 2022 Proceedings, 2022
  • Detection of Disease of Tomato Plant Based on Convolution Neural Network
    P. Ramkumar, Anitha Ruth J, Uma R, Valarmathi R, Venkatesh D
    Proceedings of the 2021 4th International Conference on Computing and Communications Technologies Iccct 2021, 2021
  • Apple leaf disease identification based on optimized deep neural network
    Anitha Ruth J., Uma R., Meenakshi A.
    Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments, 2020
  • RWH: Assessing Optimum Cistern Size Based on K-Means and Crowd Computing Approach
    A. Pavithra, M. Pushpa, J. Anitha Ruth
    Proceedings International Conference on Smart Electronics and Communication Icosec 2020, 2020
  • Cloud computing-based resource provisioning using k-means clustering and GWO prioritization
    A. Meenakshi, H. Sirmathi, J. Anitha Ruth
    Soft Computing, 2019
  • Secure data storage and intrusion detection in the cloud using MANN and dual encryption through various attacks
    J. Anitha Ruth, H. Sirmathi, A. Meenakshi
    Iet Information Security, 2019
  • Quality assured optimal resource provisioning and scheduling technique based on Improved Hierarchical Agglomerative Clustering Algorithm (IHAC)
    Meenakshi A., Sirmathi H., Anitha Ruth J.
    International Journal of Engineering and Technology, 2016

RECENT SCHOLAR PUBLICATIONS

  • Optimized deep learning framework for periodontal disease severity prediction and treatment recommendation
    R Kausalya, JA Ruth
    Biomedical Signal Processing and Control 119, 109768 , 2026
    2026
  • Detection of Brain Tumor Using Machine Learning Model
    R Uma, P Ramkumar, C Sivaprakash, JA Ruth, S Viswavardinii
    Brain Informatics Technology, 493-508 , 2025
    2025
  • Clinical-Ready CNN Framework for Lung Cancer Classification: Systematic Optimization for Healthcare Deployment with Enhanced Computational Efficiency
    G Inbasakaran, JA Ruth
    Intelligence-Based Medicine, 100292 , 2025
    2025
    Citations: 5
  • EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation
    M Saraniya, JA Ruth
    Discover Artificial Intelligence 5 (1), 194 , 2025
    2025
    Citations: 3
  • Ebola optimization based spiking neural network for automatic hate speech recognition
    A Meenakshi, JA Ruth
    International Journal of Information Technology 17 (3), 1631-1639 , 2025
    2025
    Citations: 2
  • Machine Learning-Based Analysis of Tweets Concerning Women's Safety
    VGV Mahesh, J Anitha Ruth, R Chandra Prabha
    Doctoral Symposium on Computational Intelligence, 523-535 , 2025
    2025
  • Deep learning-based embryo quality assessment: a dual-branch CNN model integrating morphological and spatial features
    M Saraniya, JA Ruth
    Intelligence-Based Medicine 12, 100273 , 2025
    2025
    Citations: 5
  • Optimized heuristic technique for task scheduling in secure cloud storage environment
    S Maheshwari, JA Ruth
    2024 13th International Conference on System Modeling & Advancement in … , 2024
    2024
    Citations: 1
  • Two Stage Machine Learning Framework to Identify Periodontitis and Dental Caries
    R Kausalya, JA Ruth
    2024 4th International Conference on Mobile Networks and Wireless … , 2024
    2024
    Citations: 1
  • Machine Learning and Cryptographic Solutions for Data Protection and Network Security
    JA Ruth, VGV Mahesh, P Visalakshi, R Uma, A Meenakshi
    IGI Global , 2024
    2024
    Citations: 3
  • Implementation of Ensemble Predictive Models for Parkinson’s Disease Detection
    JA Ruth, VGV Mahesh, R Uma
    International Conference on Advances in Information Communication Technology … , 2024
    2024
  • Innovative machine learning applications for cryptography
    JA Ruth, GV Vijayalakshmi, P Visalakshi, R Uma, A Meenakshi
    IGI Global , 2024
    2024
    Citations: 5
  • Early Prediction of Cardiac Arrest Using Data Mining Algorithms
    P Ramkumar, R Uma, D Sivakumar, JA Ruth
    Artificial Intelligence Transformations for Healthcare Applications: Medical … , 2024
    2024
    Citations: 1
  • COVID-19 Contamination Extraction From CT Images Using an Adaptive Network
    P Arunachalam, P Ramkumar, R Uma, JA Ruth
    Artificial Intelligence Transformations for Healthcare Applications: Medical … , 2024
    2024
  • A Smart Anomaly Detection Method in Cyber Physical Systems Using Machine Learning
    P Ramkumar, B Shadaksharappa, R Uma, JA Ruth, R Valarmathi
    Machine Learning and Cryptographic Solutions for Data Protection and Network … , 2024
    2024
    Citations: 1
  • Secure Data Transmission in the WSN Sector Utilizing a Heuristic Multi-Level Clustering Mechanism With Dynamic Trust Computation
    R Uma, P Ramkumar, JA Ruth, R Valarmathi, C Vinola
    Machine Learning and Cryptographic Solutions for Data Protection and Network … , 2024
    2024
  • Prediction Analysis of Natural Disasters Using Machine Learning
    P Ramkumar, R Uma, D Satishkumar, JA Ruth, S Harikrishna
    Predicting Natural Disasters With AI and Machine Learning, 147-157 , 2024
    2024
  • Prediction of Embryo Selection Using Efficient Otsu Segmentation for in-Vitro Fertilization Techinques
    M Saraniya, JA Ruth
    International Conference on Deep Sciences for Computing and Communications … , 2023
    2023
  • Meta-heuristic based deep learning model for leaf diseases detection
    JA Ruth, R Uma, A Meenakshi, P Ramkumar
    Neural Processing Letters 54 (6), 5693-5709 , 2022
    2022
    Citations: 26
  • Automatic classification of white blood cells using deep features based convolutional neural network
    A Meenakshi, JA Ruth, VR Kanagavalli, R Uma
    Multimedia tools and applications 81 (21), 30121-30142 , 2022
    2022
    Citations: 24

MOST CITED SCHOLAR PUBLICATIONS

  • Meta-heuristic based deep learning model for leaf diseases detection
    JA Ruth, R Uma, A Meenakshi, P Ramkumar
    Neural Processing Letters 54 (6), 5693-5709 , 2022
    2022
    Citations: 26
  • Automatic classification of white blood cells using deep features based convolutional neural network
    A Meenakshi, JA Ruth, VR Kanagavalli, R Uma
    Multimedia tools and applications 81 (21), 30121-30142 , 2022
    2022
    Citations: 24
  • Secure data storage and intrusion detection in the cloud using MANN and dual encryption through various attacks
    J Anitha Ruth, H Sirmathi, A Meenakshi
    IET Information Security 13 (4), 321-329 , 2019
    2019
    Citations: 17
  • Cloud computing-based resource provisioning using k -means clustering and GWO prioritization: A. Meenakshi et al.
    A Meenakshi, H Sirmathi, J Anitha Ruth
    Soft Computing 23 (21), 10781-10791 , 2019
    2019
    Citations: 15
  • Clinical-Ready CNN Framework for Lung Cancer Classification: Systematic Optimization for Healthcare Deployment with Enhanced Computational Efficiency
    G Inbasakaran, JA Ruth
    Intelligence-Based Medicine, 100292 , 2025
    2025
    Citations: 5
  • Deep learning-based embryo quality assessment: a dual-branch CNN model integrating morphological and spatial features
    M Saraniya, JA Ruth
    Intelligence-Based Medicine 12, 100273 , 2025
    2025
    Citations: 5
  • Innovative machine learning applications for cryptography
    JA Ruth, GV Vijayalakshmi, P Visalakshi, R Uma, A Meenakshi
    IGI Global , 2024
    2024
    Citations: 5
  • Steganography based secure data storage and intrusion detection for cloud computing using signcryption and artificial neural network
    JA Ruth, H Sirmathi, A Meenakshi
    Research Journal of Applied Sciences, Engineering and Technology 13 (5), 354-364 , 2016
    2016
    Citations: 5
  • EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation
    M Saraniya, JA Ruth
    Discover Artificial Intelligence 5 (1), 194 , 2025
    2025
    Citations: 3
  • Machine Learning and Cryptographic Solutions for Data Protection and Network Security
    JA Ruth, VGV Mahesh, P Visalakshi, R Uma, A Meenakshi
    IGI Global , 2024
    2024
    Citations: 3
  • Ebola optimization based spiking neural network for automatic hate speech recognition
    A Meenakshi, JA Ruth
    International Journal of Information Technology 17 (3), 1631-1639 , 2025
    2025
    Citations: 2
  • Optimized heuristic technique for task scheduling in secure cloud storage environment
    S Maheshwari, JA Ruth
    2024 13th International Conference on System Modeling & Advancement in … , 2024
    2024
    Citations: 1
  • Two Stage Machine Learning Framework to Identify Periodontitis and Dental Caries
    R Kausalya, JA Ruth
    2024 4th International Conference on Mobile Networks and Wireless … , 2024
    2024
    Citations: 1
  • Early Prediction of Cardiac Arrest Using Data Mining Algorithms
    P Ramkumar, R Uma, D Sivakumar, JA Ruth
    Artificial Intelligence Transformations for Healthcare Applications: Medical … , 2024
    2024
    Citations: 1
  • A Smart Anomaly Detection Method in Cyber Physical Systems Using Machine Learning
    P Ramkumar, B Shadaksharappa, R Uma, JA Ruth, R Valarmathi
    Machine Learning and Cryptographic Solutions for Data Protection and Network … , 2024
    2024
    Citations: 1
  • A Hierarchical Machine Learning Frame Work to Classify Breast Tissue for Identification of Cancer
    JA Ruth, VGV Mahesh, R Uma, P Ramkumar
    Proceedings of the 11th International Conference on Computer Engineering and … , 2021
    2021
    Citations: 1
  • Optimized deep learning framework for periodontal disease severity prediction and treatment recommendation
    R Kausalya, JA Ruth
    Biomedical Signal Processing and Control 119, 109768 , 2026
    2026
  • Detection of Brain Tumor Using Machine Learning Model
    R Uma, P Ramkumar, C Sivaprakash, JA Ruth, S Viswavardinii
    Brain Informatics Technology, 493-508 , 2025
    2025
  • Machine Learning-Based Analysis of Tweets Concerning Women's Safety
    VGV Mahesh, J Anitha Ruth, R Chandra Prabha
    Doctoral Symposium on Computational Intelligence, 523-535 , 2025
    2025
  • Implementation of Ensemble Predictive Models for Parkinson’s Disease Detection
    JA Ruth, VGV Mahesh, R Uma
    International Conference on Advances in Information Communication Technology … , 2024
    2024