Dr.S.Kanchana

@srmist.edu.in

Assistant Professor, Science & Humanities
Assistant Professor, Science & Humanities
Assistant Professor

Dr.S.Kanchana


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https://researchid.co/kanchans

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications
19

Scopus Publications

71

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • RESILIENCE GREY WOLF OPTIMIZATION-BASED CONVOLUTIONAL NEURAL NETWORK (RGWO-CNN) FOR CLASSIFICATION OF HEART DISEASE AND DIABETES (HDD)
    Journal of Theoretical and Applied Information Technology, 2025
  • FAORE PONY-INSPIRED CHAOTIC NEURAL ENCRYPTION FOR SECURE AND EFFICIENT MEDICAL IMAGE PROTECTION
    Journal of Theoretical and Applied Information Technology, 2025
  • AN IOT BASED EFFICIENT E-VOTING SYSTEM USING QR CODE AND EFFT-SWIFFT WITH BLOCKCHAIN FOR ENHANCED SECURITY AND TRANSPARENCY
    Journal of Theoretical and Applied Information Technology, 2025
  • Predicting autism spectrum disorder through sentiment analysis with attention mechanisms: a deep learning approach
    Murali Anand Mareeswaran, Kanchana Selvarajan
    Indonesian Journal of Electrical Engineering and Computer Science, 2025
    Autism spectrum disorder (ASD) is considered a spectrum disorder. The availability of technology to identify the characteristics of ASD will have major implications for clinicians. In this article, we present a new autism diagnosis method based on attention mechanisms for behavior modeling-based feature embedding along with aspect-based analysis for a better classification of ASD. The hybrid model comprises a convolutional neural network (CNN) architecture that integrates two bidirectional long short-term memory (BiLSTM) blocks, together with additional propagation techniques, for the purpose of classification the origins of Autism Tweet dataset; the proposed work takes Autism Tweet dataset and preprocesses them to employ n-gram to extract features of which the features of the ASD behavior are fed to generate the significant behavior for classification. The model takes into account both behavior-guided features across every aspect of the Class/ASD to provide higher accuracy using Adam optimizer. The experimental values inferred that the n-BiLSTM technique reaches maximum accuracy with 98%.
  • Optimized CNN-BiLSTM with Attention: A High Performance Model for Predicting Heart Disease Using Cleveland and Framingham Datasets
    Kayalvizhi K, Kanchana S, Silvia Priscila S, Padmavathy C, Banda SNV Ramana Murthy, Veeramani Thangavel
    Journal of Machine and Computing, 2024
    Worldwide, some 17.9 million survives are lost each year due to heart disease (HD), which is acknowledged by the World Health Organisation (WHO) as top cause of mortality. In order to simplify further action, HD prediction—a difficult problem—can give a computerised estimate of the HD level. Improving patient outcomes and allowing for timely medical interventions are both made possible by early detection and accurate calculation of HD. As a result, HD prediction has garnered a great deal of interest from healthcare facilities around the globe. There has been encouraging progress in the detection of cardiac illness thanks to recent developments in machine learning (ML). Transparency and explainability, in addition to generalisability and robustness, are crucial for ML models to be used in therapeutic settings. The efficient prediction and diagnosis of numerous diseases was greatly aided by systems based on Deep Learning (DL). By combining Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTMs), besides Attention Mechanisms (CNN-AM), this paper aims to build a strong HD prediction scheme. Minimal preparation is necessary for this procedure. To extract spatial features, CNN is used. To extract temporal characteristics, Bi-LSTM is used. Lastly, to filter out the outcomes of the more to ighted channel output classification, two channel to ights are allotted through the attention mechanism. The proposed model's parameters are fine-tuned using a new optimisation approach known as Newton-Raphson-based Optimiser (NRO), which ultimately leads to better classification accuracy. With accuracy of 95.3% on the Cleveland dataset and 98.1% on the Framingham dataset, respectively, the optimised CNN-BiLSTM-AM model demonstrated the best performance in the experimental findings.
  • Weight Optimization for Missing Data Prediction of Landslide Susceptibility Mapping in Remote Sensing Analysis
    Kanchana S, Jayakarthik R, Dineshbabu V, Saranya M, Srikanth Mylapalli, Rajesh Kumar T
    Journal of Machine and Computing, 2024
    To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.
  • A computational intelligent analysis of autism spectrum disorder using machine learning techniques
    Murali Anand Mareeswaran, Kanchana Selvarajan
    Iaes International Journal of Artificial Intelligence, 2024
    <p>Children between the ages of 12 and 24 months who have autism spectrum disorder (ASD) experience abnormalities in the brain that result in undesirable symptoms. Children with ASD struggle to comprehend what others are trying to say and or feel, and they experience extreme anxiety in social situations. Additionally, they have a hard time making friends and even living independently. The defective genes, which control the brain and govern how brain cells communicate with one another, are the primary cause of ASD because they alter brain function. Our primary goal is to assist therapists and parents of children with ASD in using current technologies, such as human intelligence and artificial intelligence, to treat ASD and assist those youngsters in obtaining better social interaction and societal integration. For the purpose of doing an early analysis of ASD, the data is divided into the following three categories: age, gender, and jaundice symptoms. The performance of machine learning algorithms can be influenced by a variety of factors, such as the size of the dataset and quality of the dataset, the choice of features, and the tuning of hyper-parameters. In this work, the support vector machine (SVM) yields 96% as the highest classification accuracy.</p>
  • E-Voting Based Blockchain Mechanism Using Feature Selection Based Machine Learning
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Predictive Maintenance Model Using Hybrid Procedure of Improved Quantum Cat Swarm Optimisation for Asset Management in Industry 4.0
    S. Kanchana, Dhivya Rajan, R. Mahaveerakannan, K. V. Daya Sagar, P. Subramanian, B. Rajakumar
    Lecture Notes in Networks and Systems, 2024
  • Machine learning approach for predicting heart and diabetes diseases using data-driven analysis
    Usha Sekar, Kanchana Selvarajan
    Iaes International Journal of Artificial Intelligence, 2023
    <p>Environmental changes and food habits affect people's health with numerous<br />diseases in today's life. Machine learning is a technique that plays a vital role<br />in predicting diseases from collected data. The health sector has plenty of<br />electronic medical data, which helps this technique to diagnose various<br />diseases quickly and accurately. There has been an improvement in accuracy<br />in medical data analysis as data continues to grow in the medical field. Doctors<br />may have a hard time predicting symptoms accurately. This proposed work<br />utilized Kaggle data to predict and diagnose heart and diabetic diseases. The<br />diseases heart and diabetes are the foremost cause of higher death rates for<br />people. The dataset contains target features for the diagnosis of heart disease.<br />This work finds the target variable for diabetic disease by comparing the<br />patient's blood sugars to normal levels. Blood pressure, body mass index<br />(BMI), and other factors diagnose these diseases and disorders. This work<br />justifies the filter method and principal component analysis for selecting and<br />extracting the feature. The main aim of this work is to highlight the<br />implementation of three ensemble techniques-Adaptive boost, Extreme<br />Gradient boosting, and Gradient boosting-as well as the emphasis placed on<br />the accuracy of the results.</p>
  • REVIVED ANT COLONY OPTIMIZATION-BASED ADABOOST ALGORITHM FOR HEART DISEASE AND DIABETES (HDD) PREDICTION
    Journal of Theoretical and Applied Information Technology, 2023
  • Prediction of Heart Disease and Diabetes (HDD) using Self-Adaptive Particle Swarm Optimization- Based Random Forest Algorithm(SAPSORF)
    Usha S, Kanchana S
    International Journal of Engineering Trends and Technology, 2023
  • Privacy Preservation of Medical Images Using Hybrid Chaotic Maps
    P. Suhasini, S. Kanchana
    Proceedings 4th IEEE 2023 International Conference on Computing Communication and Intelligent Systems Icccis 2023, 2023
  • Enhanced Fractional Order Lorenz System for Medical Image Encryption in Cloud-Based Healthcare Administration
    P. Suhasini, S. Kanchana
    International Journal of Computer Networks and Applications, 2022
  • Effective Analysis of Heart Disease Prediction using Machine Learning Techniques
    S. Usha, S. Kanchana
    Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022
  • Elevating the accuracy of missing data imputation using Bolzano classifier
    International Journal of Engineering and Technology, 2016
  • A monotonic sequence and subsequence approach in missing data statistical analysis
    Global Journal of Pure and Applied Mathematics, 2016
  • Boosting the accuracy of weak learner using semi supervised COGA techniques
    Arpn Journal of Engineering and Applied Sciences, 2016
  • Multiple imputation of missing data using efficient machine learning approach
    International Journal of Applied Engineering Research, 2015

RECENT SCHOLAR PUBLICATIONS

  • An Intelligent Deep Learning Framework For Early Detection Of Distributed Denial-Of-Service Attacks
    S Sutradhar, V Grewal, G Parmar, S Kanchana, M Shrivastava, P Kavitha
    INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 1721-1730 , 2026
    2026
  • RESILIENCE GREY WOLF OPTIMIZATION-BASED CONVOLUTIONAL NEURAL NETWORK (RGWO-CNN) FOR CLASSIFICATION OF HEART DISEASE AND DIABETES (HDD)
    S USHA, RA SHERIFF, S KANCHANA
    Journal of Theoretical and Applied Information Technology 103 (12) , 2025
    2025
  • FAORE pony-inspired chaotic neural encryption for secure and efficient medical image protection
    P Suhasini, S Kanchana
    Journal of Theoretical and Applied Information Technology 103 (10) , 2025
    2025
    Citations: 1
  • IMPACT OF BROKEN LINKS AND DEAD CODES ON OPEN-SOURCE REPOSITORIES: AN AI AUTO ENCODER APPROACH FOR SENSITIVE DATA PROTECTION
    M MUTHALAGU, DRR RATHINASABAPATHY, V SIKORSKA, ...
    Journal of Theoretical and Applied Information Technology 103 (10) , 2025
    2025
  • an Iot Based Efficient E-Voting System Using Qr Code and Efft-Swifft With Blockchain for Enhanced Security and Transparency
    T Prabakar, S Kanchana
    J Theor Appl Inf Technol 103 (7), 2917-2931 , 2025
    2025
    Citations: 2
  • Privacy Preservation of Medical Images Using Hybrid Chaotic Maps
    P Suhasini, S Kanchana
    2023 International Conference on Computing, Communication, and Intelligent … , 2023
    2023
    Citations: 2
  • Predictive maintenance model using hybrid procedure of improved Quantum Cat swarm optimisation for asset management in industry 4.0
    S Kanchana, D Rajan, R Mahaveerakannan, KVD Sagar, P Subramanian, ...
    International Conference on Data Analytics & Management, 499-515 , 2023
    2023
    Citations: 6
  • Revived ant colony optimization-based AdaBoost algorithm for heart disease and diabetes (HDD) prediction
    S Usha, S Kanchana
    Journal of Theoretical and Applied Information Technology 101 (4), 1552-1567 , 2023
    2023
    Citations: 12
  • Effective analysis of heart disease prediction using machine learning techniques
    S Usha, S Kanchana
    2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022
    2022
    Citations: 8
  • Enhanced fractional order lorenz system for medical image encryption in cloud-based healthcare administration
    P Suhasini, S Kanchana
    International Journal of Computer Networks and Applications (IJCNA) 9 , 2022
    2022
    Citations: 6
  • A Survey of Health Care Data in Cloud Environment
    Suhasini, Kanchana
    Indian Journal of Natural Sciences 12 (66), 32147-32155 , 2021
    2021
  • Exploration Of A State Of The Art On Cardiac Diseases Prediction Techniques
    S Usha, S Kanchana
    European Journal of Molecular & Clinical Medicine 7 (07), 2020 , 2021
    2021
  • An Exploiting Machine Learning Technique for Predicting Disease
    S Usha, S Kanchana
    Turkish Journal of Computer and Mathematics Education 12 (12), 1416-1423 , 2021
    2021
    Citations: 1
  • Predicting heart disease using feature selection techniques based on data driven approach
    S Usha, S Kanchana
    Webology 18 (4), 97-108 , 2021
    2021
    Citations: 5
  • Elevating the accuracy of missing data imputation using Bolzano classifier
    S Kanchana, AS Thanamani
    Int J Eng Technol (IJET) 8 (1), 138-45 , 2016
    2016
    Citations: 7
  • Experimental analysis of imputation of missing data using machine learning techniques
    S Kanchana, DAS Thanamani
    Int. J. Adv. Inf. Sci. Technol. 38 (38), 119-123 , 2015
    2015
    Citations: 5
  • Multiple imputation of missing data using efficient machine learning approach
    S Kanchana, DAS Thanamani
    International Journal of Applied Engineering Research 10 (1), 1473-1482 , 2015
    2015
    Citations: 5
  • Classification of efficient imputation method for analyzing missing values
    S Kanchana, AS Thanamani
    International Journal of Computer Trends and Technology 12 (4), 193-195 , 2014
    2014
    Citations: 10
  • BOOSTING THE ACCURACY OF WEAK LEARNER USING SEMI SUPERVISED CoGA TECHNIQUES
    S Kanchana, AS Thanamani
    VOL 11, 2006-2016 , 2006
    2006
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Revived ant colony optimization-based AdaBoost algorithm for heart disease and diabetes (HDD) prediction
    S Usha, S Kanchana
    Journal of Theoretical and Applied Information Technology 101 (4), 1552-1567 , 2023
    2023
    Citations: 12
  • Classification of efficient imputation method for analyzing missing values
    S Kanchana, AS Thanamani
    International Journal of Computer Trends and Technology 12 (4), 193-195 , 2014
    2014
    Citations: 10
  • Effective analysis of heart disease prediction using machine learning techniques
    S Usha, S Kanchana
    2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022
    2022
    Citations: 8
  • Elevating the accuracy of missing data imputation using Bolzano classifier
    S Kanchana, AS Thanamani
    Int J Eng Technol (IJET) 8 (1), 138-45 , 2016
    2016
    Citations: 7
  • Predictive maintenance model using hybrid procedure of improved Quantum Cat swarm optimisation for asset management in industry 4.0
    S Kanchana, D Rajan, R Mahaveerakannan, KVD Sagar, P Subramanian, ...
    International Conference on Data Analytics & Management, 499-515 , 2023
    2023
    Citations: 6
  • Enhanced fractional order lorenz system for medical image encryption in cloud-based healthcare administration
    P Suhasini, S Kanchana
    International Journal of Computer Networks and Applications (IJCNA) 9 , 2022
    2022
    Citations: 6
  • Predicting heart disease using feature selection techniques based on data driven approach
    S Usha, S Kanchana
    Webology 18 (4), 97-108 , 2021
    2021
    Citations: 5
  • Experimental analysis of imputation of missing data using machine learning techniques
    S Kanchana, DAS Thanamani
    Int. J. Adv. Inf. Sci. Technol. 38 (38), 119-123 , 2015
    2015
    Citations: 5
  • Multiple imputation of missing data using efficient machine learning approach
    S Kanchana, DAS Thanamani
    International Journal of Applied Engineering Research 10 (1), 1473-1482 , 2015
    2015
    Citations: 5
  • an Iot Based Efficient E-Voting System Using Qr Code and Efft-Swifft With Blockchain for Enhanced Security and Transparency
    T Prabakar, S Kanchana
    J Theor Appl Inf Technol 103 (7), 2917-2931 , 2025
    2025
    Citations: 2
  • Privacy Preservation of Medical Images Using Hybrid Chaotic Maps
    P Suhasini, S Kanchana
    2023 International Conference on Computing, Communication, and Intelligent … , 2023
    2023
    Citations: 2
  • FAORE pony-inspired chaotic neural encryption for secure and efficient medical image protection
    P Suhasini, S Kanchana
    Journal of Theoretical and Applied Information Technology 103 (10) , 2025
    2025
    Citations: 1
  • An Exploiting Machine Learning Technique for Predicting Disease
    S Usha, S Kanchana
    Turkish Journal of Computer and Mathematics Education 12 (12), 1416-1423 , 2021
    2021
    Citations: 1
  • BOOSTING THE ACCURACY OF WEAK LEARNER USING SEMI SUPERVISED CoGA TECHNIQUES
    S Kanchana, AS Thanamani
    VOL 11, 2006-2016 , 2006
    2006
    Citations: 1
  • An Intelligent Deep Learning Framework For Early Detection Of Distributed Denial-Of-Service Attacks
    S Sutradhar, V Grewal, G Parmar, S Kanchana, M Shrivastava, P Kavitha
    INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 1721-1730 , 2026
    2026
  • RESILIENCE GREY WOLF OPTIMIZATION-BASED CONVOLUTIONAL NEURAL NETWORK (RGWO-CNN) FOR CLASSIFICATION OF HEART DISEASE AND DIABETES (HDD)
    S USHA, RA SHERIFF, S KANCHANA
    Journal of Theoretical and Applied Information Technology 103 (12) , 2025
    2025
  • IMPACT OF BROKEN LINKS AND DEAD CODES ON OPEN-SOURCE REPOSITORIES: AN AI AUTO ENCODER APPROACH FOR SENSITIVE DATA PROTECTION
    M MUTHALAGU, DRR RATHINASABAPATHY, V SIKORSKA, ...
    Journal of Theoretical and Applied Information Technology 103 (10) , 2025
    2025
  • A Survey of Health Care Data in Cloud Environment
    Suhasini, Kanchana
    Indian Journal of Natural Sciences 12 (66), 32147-32155 , 2021
    2021
  • Exploration Of A State Of The Art On Cardiac Diseases Prediction Techniques
    S Usha, S Kanchana
    European Journal of Molecular & Clinical Medicine 7 (07), 2020 , 2021
    2021