Dr NITHYASHRI J

@srmist.edu.in

Assistant Professor, Engineering & Technology
SRM Institute of Science and Technology



                 

https://researchid.co/nithyashri

, received her Doctoral degree from Sathyabama university, Chennai. Published more than 25 research articles in various International/ National Journals and Conferences. She has more than 21 years of teaching experience in various Engineering Colleges. Her research area includes Computer Vision, Image Processing and Pattern Recognition.

EDUCATION

Ph.D., (2008–2016) , Department of Computer Science & Engineering,
Sathyabama University, Chennai, India.
M.E., (2003 – 2005), Department of Computer Science & Engineering,
Sathyabama University, Chennai, India.
FIRST CLASS
BE., (1996–2000), Department of Computer Science & Engineering,
VRS College of Engineering & Technology,University of Madras, Chennai, India.
FIRST CLASS

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Human-Computer Interaction

7

Scopus Publications

127

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Automated Diagnosis of Cancer Disease with Human Tissues using Haralick Texture Features and Deep Learning Techniques
    S. Balakrishnan, N.S. Simonthomas, J. Nithyashri, B. Suchitra, G. Umamaheswari, and D. Pradeep
    Iskender AKKURT
    The increasing use of automated cancer diagnosis based on histopathological images is significant because it is likely to increase the accuracy of diagnosis and decrease the workload on pathologists. This research introduces a hybrid methodology that integrates Haralick texture features with deep learning strategies to improve the automated identification of cancer in human tissue specimens. Haralick texture features, obtained from the Gray-Level Co-Occurrence Matrix (GLCM), offer essential information regarding the spatial relationships and textural characteristics present in tissue samples, which frequently signal the presence of cancerous alterations. The integration of these interpretable texture features with convolutional neural networks (CNNs) makes our approach use the strengths of both traditional texture analysis and deep learning's ability to learn complex patterns. This will process raw image data with the Haralick features leading to a powerful model that, hopefully, makes better classification along with interpretability. These features, handcrafted and capturing features like contrast, correlation, energy, and homogeneity, provide differences in the texture of the tissue that classify between normal cells and abnormal ones. Experimental results were presented in distinguishing cancerous and non-cancerous tissues with high accuracy. The diagnostic efficiency was also enhanced while at the same time providing a reliable and scalable tool that may assist pathologists during clinical decision-making, which consequently leads to efficient cancer diagnosis and patient care.

  • Intelligent Classification of Liver Diseases using Ensemble Machine Learning Techniques
    Nithyashri, Harsh Goel, and Manvendra Singh Hada
    IEEE
    Liver disease is a more challenging health crisis which affects millions of people worldwide. Early detection and treatments are essential for improving patient outcomes, but diagnosis at early stage is more challenging. Machine learning algorithms were significantly used to improve the accuracy and efficiency of liver disease diagnosis. This study developed a machine learning model to predict the stage of liver disease using a variety of clinical features. The LR Hyperparameter tuned model is used to improve the accuracy to 83% on a test set, much higher than traditional diagnostic methods. This suggests that the model could be used to develop a non-invasive, cost-effective, and highly accurate tool for diagnosing and monitoring liver disease patients. Additionally, the model could identify high-risk patients for developing liver disease complications, such as cirrhosis and liver failure. This information could inform personalized treatment plans to prevent the development of complications. Overall, the machine learning model has the potential to transform the early detection and management of liver disease.

  • IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model
    J. Nithyashri, Ravi Kumar Poluru, S. Balakrishnan, M. Ashok Kumar, P. Prabu, and S. Nandhini
    Elsevier BV

  • A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning
    Senthil G. A, R. Prabha, J. Nithyashri, Suganthi. P, I. Thamarai, and Sridevi S
    IEEE
    Heart Disease or Cardiovascular Disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age and sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include K-Nearest Neighbour (KNN) and decision tree classifier which is compared to yield the desired and efficient output.

  • Comparison analysis of IoT based industrial automation and improvement of different processes - Review
    V. Kamatchi Sundari, J. Nithyashri, S. Kuzhaloli, Jayasudha Subburaj, P. Vijayakumar, and P. Subha Hency Jose
    Elsevier BV

  • A Gaussian mixture model for classifying the human age using DWT and Sammon map
    J. Nithyashri and G. Kulanthaivel
    Science Publications
    The appearance of a human face rigorously changes with respect to age that makes Age Classification as a more challenging task. The algorithms such as, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Radial Basis Function (RBF), motivated many Face Researchers to focus their attention in classifying the human age into various age groups. The Classification rate produced by these existing algorithms is not significant indeed. In this study, Gaussian Mixture Models (GMM) is used for classifying the facial images into different age groups. A combination of Discrete Wavelet Transformation (DWT) and Sammon Map are used to extract the facial features. The performance of this approach is tested using Album-2 of MORPH database. A maximum classification rate of 99.52% is achieved in stage-1, whereas 99.46% is achieved in stage-2 using GMM. Also the accuracy achieved using Gaussian Mixture Model, is comparatively greater than K-NN.

  • Classification of human age based on Neural Network using FG-NET Aging database and Wavelets
    J. Nithyashri and G. Kulanthaivel
    IEEE
    Face Aging has been an vital area of research for the past few decades. As the age increases, there are some visible changes in the face, making age classification simpler. Based on the facial growth, we can classify the human age into various kinds. Though there are various algorithms existed so far, a more sophisticated method is attempted for classifying facial age. Age Prototypes, Statistical models and Distance based technique have been widely used for classification of human face. The system can be improved by using the Wavelet Transformation (WT) for extracting the face features and Artificial Neural Network to classify the age group. The facial images are pre-processed and then the face features are extracted using Wavelet Transformation. The distance between each of features are evaluated using Euclidean distance and these values were given as input to Adaptive Resonance Network (ART). The Neural Network is trained using FG-NET (Face and Gesture Recognition Research Network) aging database. The human age is classified into four categories as Child (0-12 years), Adolescence (13-18 years), Adult (19-59 years) and Senior Adult (60 years and above) which is discussed in the paper.

RECENT SCHOLAR PUBLICATIONS

  • Automated Diagnosis of Cancer Disease with Human Tissues using Haralick Texture Features and Deep Learning Techniques
    DP S.Balakrishnan,N.S.Simonthomas,J.Nithyashri ,B.Suchitra,G.Umamaheswari
    International Journal of Computational and Experimental Science and 2025

  • VisionAid: Enhancing Accessibility for the visually impaired with YOLO and gTTS
    NJ Bhagyalakshmi .R
    International Conference on Visual Analytics and Data Visualization (ICVADV 2025

  • IOT Enabled Solar Step Lights for Outdoor
    KSK T.R.Saravanan, J.Nithyashri,P.Kanmani
    IN Patent 439924-001 2025

  • Advanced Mathematical Applications of Cryptographic protocols in Distributed Ledger Technologies and Digital Currency systems
    SSJN Senthil G.A, R.Prabha, R.Avudainayaki
    International Conference on Optimization Techniques in the Field of Engineering 2025

  • An intelligent diagnosis of Anemia using TensorFlow: Potentially Effective in AI and Quantum Network - based Medical applications
    IM J. Nithyashri, Pasala Sree Ramya
    AI and Quantum Network Applications in Business and Medicine, 173-188 2024

  • Advanced Mathematical Application of Cryptographic Protocols in Distributed Ledger Technologies and Digital Currency Systems
    S G A, R Prabha, R Avudainayaki, S Sundaram, J Nithyashri
     2024

  • An Intelligent Classification of Liver Diseases Using Ensemble Machine Learning Techniques
    MSH Dr.Nithyashri J, Harsh Goel
    2nd International Conference on Intelligent Cyber Physical System and IoT 2024

  • An intelligent Blood Bank Management and Blood Monitoring System Using Machine Learning
    DJN Yellagada Pradeep, Jatin Singhania
    African Journal of Biological Science 6 (10), 960 - 966 2024

  • A Novel Meta Analysis and classification of Herbal Medicinal Plant Raw Materials for food consumption prediction using Hybrid Deep Learning Techniques based on Augumented
    A G. A. Senthi, R. Prabha, S. Sridevi,J. Nithyashri
    5th World Conference on Artificial Intelligence : Advances and Applications a 2024

  • Hybrid Deep Learning Techniques Based on Augmented Reality in Computer
    GA Senthil, R Prabha, S Sridevi, J Nithyashri, A Suganya
    Proceedings of World Conference on Artificial Intelligence: Advances and 2024

  • IoT based washing machine for agricultural crops
    DPK Dr. T. R. Saravanan, Dr. J. Nithyashri, Dr. Antony sophia
     2023

  • IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model
    SN J. Nithyashri, Ravi kumar Poluru,S. Balakrishnan, M. Ashok kumar, P. Prabu
    Measurment:Sensors 29 2023

  • An Intelligent System for Plant Disease Diagnosis and Analysis Based on Deep Learning and Augmented Reality
    DJN Dr.G.A.Senthil, Dr.R.Prabha
    4th International Conference on Data Intelligence and Cognitive Informatics 2023

  • Temperature Based Touch Less Attendance System
    DNK Dr. T. R. Saravanan, Dr. J. Nithyashri, Dr. A. Prabhu chakaravarthy
    Intellectual Property Rights, India 17 2023

  • Intelligent assistant to predict and control the home appliances in user environment through brain computer interface using hybrid deep learning model
    MDSA Dr. V. Mohan Raj, Dr. J. Nithyashri, Dr. V. Priyanka Brahmaiah, Dr. A ...
    Journal of Complementary Medicine Research 14 (2), 150-156 2023

  • Crypto Currency Price Prediction Using Deep Learning
    DSS Dr. M. Manickam, Dr. J. Nithyashri, Aryan Kumar
    RB Journal of Libraray and Information Science 13 (4), 32-42 2023

  • A novel analysis and detection of autism spectrum disorder in artificial intelligence using hybrid machine learning
    R Prabha, J Nithyashri, I Thamarai, S Sridevi
    2023 International Conference on Innovative Data Communication Technologies 2023

  • A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning
    SS Senthil G.A R. Prabha, J. Nithyashri, Suganthi.P, I.Thamarai
    IEEE International Conference on Innovative Data Communication Technologies 2023

  • IoT Integrated Printer
    ETBSJH Dr. V. Vijeye Kaveri, Dr. J. Nithyashri, Dr. A. Devipriya
    IN Patent 367362-001 2022

  • Surveillance and Patrolling of Women Safety Using Visual Trigger Automation
    S Vinodhkumar, J Nithyashri, D Brindha, S Balakrishnan
    Mathematical Statistician and Engineering Applications 71 (4), 1440-1446 2022

MOST CITED SCHOLAR PUBLICATIONS

  • Classification of human age based on Neural Network using FG-NET Aging database and Wavelets
    J Nithyashri, G Kulanthaivel
    2012 Fourth international conference on advanced computing (ICoAC), 1-5 2012
    Citations: 53

  • IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model
    SN J. Nithyashri, Ravi kumar Poluru,S. Balakrishnan, M. Ashok kumar, P. Prabu
    Measurment:Sensors 29 2023
    Citations: 31

  • Comparison analysis of IoT based industrial automation and improvement of different processes-review
    PSHJ V Kamatchi sundari, J Nithyashri,S kuzhaloli,Jayasudha Subburaj, P ...
    Materials Today:Proceedings 2021
    Citations: 18

  • A novel analysis and detection of autism spectrum disorder in artificial intelligence using hybrid machine learning
    R Prabha, J Nithyashri, I Thamarai, S Sridevi
    2023 International Conference on Innovative Data Communication Technologies 2023
    Citations: 9

  • An Intelligent System for Plant Disease Diagnosis and Analysis Based on Deep Learning and Augmented Reality
    DJN Dr.G.A.Senthil, Dr.R.Prabha
    4th International Conference on Data Intelligence and Cognitive Informatics 2023
    Citations: 5

  • Facial Age Classification Using Discrete Wavelet Transform and K-Nearest Neighbour Algorithm
    JNG kulanthaivel
    Journal of Computer Science Engineering and Information Technology Research 2014
    Citations: 3

  • Classifying the human age using Discrete Wavelet Transform, KNN and MORPH database
    DGK J.Nithyashri
    Journal of Computer Applications 6 (4), 102-106 2013
    Citations: 2

  • System Software
    J Nithyashri
    TataMcgraw Hill Publications 2008
    Citations: 2

  • The present invention relates to automatically detect the early stage of breast cancer using mobile phone
    DKG Dr. B. Dhanalakshmi, Dr. Sudha Rajesh, Dr. A. Jesudoss, Dr. J ...
    IN Patent 11/2,021 2021
    Citations: 1

  • A Study On Online Spam Review Detection Methods by Machine Learning Approach
    DSJR Dr.Sudha Rajesh, Dr.M.Mercy Theresa, Dr.J.Nithyashri
    Turkish Journal of Computer and Mathematics Education 12 (9), 1292-1304 2021
    Citations: 1

  • Intelligent Classification of Liver Images Using Back Propagation Neural Network
    SFS J.Nithyasshri
    International Journal of Engineering Science and Computing 9 (3) 2019
    Citations: 1

  • cervical cancer detection using support vector machine
    B J.Nithyashri
    International Journal of Emerging Trends in Science & Technology 4 (3) 2017
    Citations: 1