Jackson Daniel

@kitcbe.com

Professor, Department of Biomedical Engineering
KIT - Kalaignarkarunanidhi Institute of Technology

Jackson Daniel


                        Download Compact PDF  
https://researchid.co/jackson2812
Dr. Jackson Daniel is a dedicated academician and accomplished researcher with over 26 years of distinguished experience in Biomedical Engineering, Instrumentation Engineering, and Non-Destructive Evaluation (NDE). He holds a Ph.D. from Anna University and an M.Tech in Biomedical Engineering from IIT Madras, and is a recognized Research Supervisor under Anna University. He has acted as Co - Investigator for BRNS-sponsored research project worth 24 Lakhs His research expertise centers on Magnetic Flux Leakage–based Non-Destructive Evaluation and Biomedical Instrumentation, with scholarly contributions published in leading IEEE and Elsevier journals.. He has also mentored DST- and TNSCST-funded student innovation projects, one of which received international recognition at DAAD, Germany. Beyond research, he has played a pivotal role in advancing Outcome-Based Education (OBE), curriculum development, academic governance, examination reforms, and NBA accreditation processes.

EDUCATION

1. DEEE in Electrical and Electronics Engineering, April 1995, Government Polytechnic College, Tuticorin
2. B.E in Instrumentation & Control Engineering., April 1998, Arulmigu Kalasalingam College of Engg., Krishnankoil., M.K.University
3. M.Tech. in Biomedical Engineering, May 2004, IITMadras, Chennai
4. Ph.D in Faculty of Information and Communication Engineering, February 2019, Anna University, Chennai

RESEARCH, TEACHING, or OTHER INTERESTS

Biomedical Engineering, Numerical Analysis
13

Scopus Publications

161

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • MFO hyper parameter tuned machine learning algorithm for crack length severity classification
    Siva Kumar Mahalingam, Mathias Vijay Albert William, Abudhahir Ameerbasha, Jackson Daniel, Ramesh Subramanian
    Aip Conference Proceedings, 2024
  • Experimental investigation of the solar distiller using nano-black paint for different water depths
    V.S. Chandrika, J. Samson Isaac, Jackson Daniel, K. Kathiresan, CT. Muthiah, E Fantin Irudaya Raj, Ram Subbiah
    Materials Today Proceedings, 2022
  • RETRACTED: Recurrent Neural Networks for Feature Extraction from Dengue Fever
    Jackson Daniel, S. Irin Sherly, Veeralakshmi Ponnuramu, Devesh Pratap Singh, S.N. Netra, Wadi B. Alonazi, Khalid M.A. Almutairi, K.S.A. Priyan, Yared Abera
    Evidence Based Complementary and Alternative Medicine, 2022
    Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.
  • Convolutional Neural Network based Skin Lesion Classification and Identification
    U. AISHWARYA, I. JACKSON DANIEL, R. RAGHUL
    Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020
    Melanoma disease is the type of skin cancer in melanocytes, which are in the epidermis layer of the skin. The rate of msselanoma is in increasing order and found to be dangerous if not diagnosed at its beginning stage. To overcome this challenge, deep convolutional neural network techniques are used. The patient injured image is processing under different steps such as pre-processing using various filters followed by segmentation using the K-means clustering algorithm and Fuzzy C-means clustering algorithm. Finally, the detection and classification are executed with Convolutional Neural Network. Performance measures are evaluated for the proposed methodology with the accuracy of 98.43%, specificity of 98.77%, the sensitivity of 99.83%. The results drive that the proposed model of learning surpasses the existing algorithm and could be used to help medical practitioners to classify skin lesions.
  • Tsallis Entropy Segmentation and Shape Feature-based Classification of Defects in the Simulated Magnetic Flux Leakage Images of Steam Generator Tubes
    Jackson Daniel, A. Abudhahir, J. Janet Paulin
    International Journal of Pattern Recognition and Artificial Intelligence, 2020
    Early detection of water or steam leaks into sodium in the steam generator units of nuclear reactors is an important requirement from safety and economic considerations. Automated defect detection and classification algorithm for categorizing the defects in the steam generator tube (SGT) of nuclear power plants using magnetic flux leakage (MFL) technique has been developed. MFL detection is one of the most prevalent methods of pipeline inspection. Comsol 4.3a, a multiphysics modeling software has been used to obtain the simulated MFL defect images. Different thresholding methods are applied to segment the defect images. Performance metrics have been computed to identify the better segmentation technique. Shape-based feature sets such as area, perimeter, equivalent diameter, roundness, bounding box, circularity ratio and eccentricity for defect have been extracted as features for defect detection and classification. A feed forward neural network has been constructed and trained using a back-propagation algorithm. The shape features extracted from Tsallis entropy-based segmented MFL images have been used as inputs for training and recognizing shapes. The proposed method with Tsallis entropy segmentation and shape-based feature set has yielded the promising results with detection accuracy of 100% and average classification accuracy of 96.11%.
  • Characterization of defects on ferromagnetic tubes using magnetic flux leakage
    V. Suresh, A. Abudhahir, Jackson Daniel
    IEEE Transactions on Magnetics, 2019
    Detection of the defects and estimation of the defect’s parameters on the inspected specimen are complicated tasks in process industries. In this paper, a radial component of the magnetic flux leakage (MFL) is employed to detect and estimate the parameters of a circular defect on the outer surface of a ferromagnetic tube. The magnetizer arrangement with a Hall sensor is used to acquire the radial component of MFL, defects are estimated. The proposed estimation procedure is verified by preparing analytical, numerical, and experimental studies. The volumetric analyses were performed on three study cases. A good correlation is observed among the three studies. Estimation of the defect parameter in the proposed method yields good efficiency.
  • Detection and Classification of Discontinuities using Discrete Wavelet Transform and MFL Testing
    Materials Evaluation, 2018
  • Development of magnetic flux leakage measuring system for detection of defect in small diameter steam generator tube
    V Suresh, A Abudhahir, Jackson Daniel
    Measurement Journal of the International Measurement Confederation, 2017
  • Magnetic Flux Leakage (MFL) based defect characterization of steam generator tubes using artificial neural networks
    Jackson Daniel, A. Abudhahir, J. Janet Paulin
    Journal of Magnetics, 2017
    Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.
  • Characterization of defects in Magnetic Flux Leakage (MFL) images using wavelet transform and neural network
    Jackson Daniel, R. Mohanagayathriand, A. Abudhahir
    2014 International Conference on Electronics and Communication Systems Icecs 2014, 2014
    The colossal and cramped pipelines are used in piping industries like petroleum industries, power plants and other industries. The buried pipes may experience deterioration or breach of the material which leads to wrecking, percussion etc. So a distinct method is brought in to explore the defect in such regions, known as Magnetic Flux Leakage (MFL) technique which is one of the Non Destructive Testing (NDT) Method. According to this technique, the flux leakage variations at defect regions can be fetched using Hall sensors. The defect can be characterized by utilizing the raw data, which is obtained from COMSOL Multi-physics 4.3a simulation software. Preceding characterization, raw data has to be pre-processed to improve the fidelity of the information related to the discontinuities. In this work, different wavelet based de-noising techniques are performed to remove noise from the raw data. The performance measures are evaluated to select the appropriate wavelet de-noising technique for filtering the Magnetic Flux leakage (MFL) data. Then the features are extracted such as Mean, Standard deviation, Variance, Wavelet energy and Maximum flux value. This reduces the size of the data, which is used to train the neural network. The Feed Forward network is designed prior and trained to evaluate the defect length, width and depth. The corollaries in network training and testing are used to quantify the defect. The training phase plays an important role in characterization process. Percentage error for the different wavelets is computed while characterizing the defect in MFL images.
  • Comparison of segmentation techniques for detection of defects in non-destructive testing images
    R. Saranya, Jackson Daniel, A. Abudhahir, N. Chermakani
    2014 International Conference on Electronics and Communication Systems Icecs 2014, 2014
  • Certain studies on thresholding based defect detection algorithms for magnetic flux leakage images
    S. Janakiraman, J. Daniel, A. Abudhahir
    2013 IEEE International Conference on Emerging Trends in Computing Communication and Nanotechnology ICE Ccn 2013, 2013
  • An Empirical Approach for Objective Pain Measurement using Dermal and Cardiac Parameters
    K. Shankar, BharathiSubbiah V. Subbiah, Daniel Jackson
    Ifmbe Proceedings, 2009

RECENT SCHOLAR PUBLICATIONS

  • TONO-GUN: Design and Development of a Miniature Non-Contact Air-Puff Tonometer for Intraocular Pressure Measurement
    I Jackson Daniel, Sanjai Prasath, Kesavan, Mouriya
    International Journal of Drug Delivery Technology 16 (37s), 191-196 , 2026
    2026
  • Non-Invasive Bone Healing Using Low-Intensity Pulsed Ultrasound (LIPUS)
    I Jackson Daniel, Suriya, Vigneshwaran, Vishmiya
    Non-Invasive Bone Healing Using Low-Intensity Pulsed Ultrasound (LIPUS) 16 … , 2026
    2026
  • An IoT-Enabled Wearable System for Continuous Monitoring of Menstrual Health and Anemia Risk in Women
    I Jackson Daniel, K. Isha Prabha , R. Srinithi, R. Gopiha Shri
    International Journal of Drug Delivery Technology 16 (37s), 96-101 , 2026
    2026
  • ULTRASOUND IMAGE DE-NOISING AND FEATURE ENHANCEMENT FOR FETAL GROWTH MONITORING
    M DEEPARANI, DM KRISHNA, M SHANMUGATHAI, SA BHOSALE, ...
    Journal of Environmental Protection and Ecology 26 (8), 3338-3347 , 2025
    2025
  • MFO hyper parameter tuned machine learning algorithm for crack length severity classification
    SK Mahalingam, MVA William, A Ameerbasha, J Daniel, R Subramanian
    AIP Conference Proceedings 3192 (1), 020001 , 2024
    2024
  • Recurrent Neural Networks for Feature Extraction from Dengue Fever
    D Jackson, SI Sherly, V Ponnuramu, DP Singh, SN Netra, WB Alonazi, ...
    Evidence-Based Complementary and Alternative Medicine 2022 , 2022
    2022
  • Research Article Recurrent Neural Networks for Feature Extraction from Dengue Fever
    J Daniel, SI Sherly, V Ponnuramu, DP Singh, SN Netra, WB Alonazi, ...
    2022
  • Experimental investigation of the solar distiller using nano-black paint for different water depths
    VS Chandrika, JS Isaac, J Daniel, K Kathiresan, CT Muthiah, EFI Raj, ...
    Materials Today: Proceedings 56, 1406-1410 , 2022
    2022
    Citations: 11
  • Convolutional neural network based skin lesion classification and identification
    U Aishwarya, IJ Daniel, R Raghul
    2020 International Conference on Inventive Computation Technologies (ICICT … , 2020
    2020
    Citations: 12
  • Tsallis entropy segmentation and shape feature-based classification of defects in the simulated magnetic flux leakage images of steam generator tubes
    J Daniel, A Abudhahir, JJ Paulin
    International Journal of Pattern Recognition and Artificial Intelligence 34 … , 2020
    2020
    Citations: 6
  • Characterization of defects on ferromagnetic tubes using magnetic flux leakage
    V Suresh, A Abudhahir, J Daniel
    IEEE Transactions on Magnetics 55 (5), 1-10 , 2019
    2019
    Citations: 14
  • Magnetic flux leakage (MFL) based defect characterization of steam generator tubes using artificial neural networks
    J Daniel, A Abudhahir, JJ Paulin
    J. Magn 22 (1), 34-42 , 2017
    2017
    Citations: 13
  • Development of magnetic flux leakage measuring system for detection of defect in small diameter steam generator tube
    V Suresh, A Abudhahir, J Daniel
    Measurement 95, 273-279 , 2017
    2017
    Citations: 51
  • Defect Detection in Ferromagnetic Material Using Magnetic Flux Leakage (MFL) Image Processing
    AASSS Jackson Dniel I, Prabhu A
    International Journal of Applied Engineering Research 10 (19), 14951-14956 , 2015
    2015
  • Krawtchouk Moment-Based Characterization of Defects in Magnetic Flux Leakage (MFL) Image
    JPJAA Jackson Daniel I
    International Journal of Applied Engineering Research 10 (1), 15593-15597 , 2015
    2015
  • Comparison of segmentation techniques for detection of defects in non-destructive testing images
    R Saranya, J Daniel, A Abudhahir, N Chermakani
    2014 International Conference on Electronics and Communication Systems … , 2014
    2014
    Citations: 22
  • Characterization of defects in magnetic flux leakage (MFL) images using wavelet transform and neural network
    J Daniel, R Mohanagayathriand, A Abudhahir
    2014 International Conference on Electronics and Communication Systems … , 2014
    2014
    Citations: 17
  • Certain studies on thresholding based defect detection algorithms for Magnetic Flux Leakage images
    S Janakiraman, J Daniel, A Abudhahir
    2013 IEEE International Conference ON Emerging Trends in Computing … , 2013
    2013
    Citations: 2
  • Characterization of Defects in Magnetic Flux Leakage Images
    S Sangeetha, Jackson Daniel I, Abudhahir A, Gokul R
    International Journal of Computer Applications, 16-20 , 2013
    2013
  • An empirical approach for objective pain measurement using dermal and cardiac parameters
    K Shankar, BSV Subbiah, D Jackson
    13th International Conference on Biomedical Engineering: ICBME 2008 3–6 … , 2009
    2009
    Citations: 13

MOST CITED SCHOLAR PUBLICATIONS

  • Development of magnetic flux leakage measuring system for detection of defect in small diameter steam generator tube
    V Suresh, A Abudhahir, J Daniel
    Measurement 95, 273-279 , 2017
    2017
    Citations: 51
  • Comparison of segmentation techniques for detection of defects in non-destructive testing images
    R Saranya, J Daniel, A Abudhahir, N Chermakani
    2014 International Conference on Electronics and Communication Systems … , 2014
    2014
    Citations: 22
  • Characterization of defects in magnetic flux leakage (MFL) images using wavelet transform and neural network
    J Daniel, R Mohanagayathriand, A Abudhahir
    2014 International Conference on Electronics and Communication Systems … , 2014
    2014
    Citations: 17
  • Characterization of defects on ferromagnetic tubes using magnetic flux leakage
    V Suresh, A Abudhahir, J Daniel
    IEEE Transactions on Magnetics 55 (5), 1-10 , 2019
    2019
    Citations: 14
  • Magnetic flux leakage (MFL) based defect characterization of steam generator tubes using artificial neural networks
    J Daniel, A Abudhahir, JJ Paulin
    J. Magn 22 (1), 34-42 , 2017
    2017
    Citations: 13
  • An empirical approach for objective pain measurement using dermal and cardiac parameters
    K Shankar, BSV Subbiah, D Jackson
    13th International Conference on Biomedical Engineering: ICBME 2008 3–6 … , 2009
    2009
    Citations: 13
  • Convolutional neural network based skin lesion classification and identification
    U Aishwarya, IJ Daniel, R Raghul
    2020 International Conference on Inventive Computation Technologies (ICICT … , 2020
    2020
    Citations: 12
  • Experimental investigation of the solar distiller using nano-black paint for different water depths
    VS Chandrika, JS Isaac, J Daniel, K Kathiresan, CT Muthiah, EFI Raj, ...
    Materials Today: Proceedings 56, 1406-1410 , 2022
    2022
    Citations: 11
  • Tsallis entropy segmentation and shape feature-based classification of defects in the simulated magnetic flux leakage images of steam generator tubes
    J Daniel, A Abudhahir, JJ Paulin
    International Journal of Pattern Recognition and Artificial Intelligence 34 … , 2020
    2020
    Citations: 6
  • Certain studies on thresholding based defect detection algorithms for Magnetic Flux Leakage images
    S Janakiraman, J Daniel, A Abudhahir
    2013 IEEE International Conference ON Emerging Trends in Computing … , 2013
    2013
    Citations: 2
  • TONO-GUN: Design and Development of a Miniature Non-Contact Air-Puff Tonometer for Intraocular Pressure Measurement
    I Jackson Daniel, Sanjai Prasath, Kesavan, Mouriya
    International Journal of Drug Delivery Technology 16 (37s), 191-196 , 2026
    2026
  • Non-Invasive Bone Healing Using Low-Intensity Pulsed Ultrasound (LIPUS)
    I Jackson Daniel, Suriya, Vigneshwaran, Vishmiya
    Non-Invasive Bone Healing Using Low-Intensity Pulsed Ultrasound (LIPUS) 16 … , 2026
    2026
  • An IoT-Enabled Wearable System for Continuous Monitoring of Menstrual Health and Anemia Risk in Women
    I Jackson Daniel, K. Isha Prabha , R. Srinithi, R. Gopiha Shri
    International Journal of Drug Delivery Technology 16 (37s), 96-101 , 2026
    2026
  • ULTRASOUND IMAGE DE-NOISING AND FEATURE ENHANCEMENT FOR FETAL GROWTH MONITORING
    M DEEPARANI, DM KRISHNA, M SHANMUGATHAI, SA BHOSALE, ...
    Journal of Environmental Protection and Ecology 26 (8), 3338-3347 , 2025
    2025
  • MFO hyper parameter tuned machine learning algorithm for crack length severity classification
    SK Mahalingam, MVA William, A Ameerbasha, J Daniel, R Subramanian
    AIP Conference Proceedings 3192 (1), 020001 , 2024
    2024
  • Recurrent Neural Networks for Feature Extraction from Dengue Fever
    D Jackson, SI Sherly, V Ponnuramu, DP Singh, SN Netra, WB Alonazi, ...
    Evidence-Based Complementary and Alternative Medicine 2022 , 2022
    2022
  • Research Article Recurrent Neural Networks for Feature Extraction from Dengue Fever
    J Daniel, SI Sherly, V Ponnuramu, DP Singh, SN Netra, WB Alonazi, ...
    2022
  • Defect Detection in Ferromagnetic Material Using Magnetic Flux Leakage (MFL) Image Processing
    AASSS Jackson Dniel I, Prabhu A
    International Journal of Applied Engineering Research 10 (19), 14951-14956 , 2015
    2015
  • Krawtchouk Moment-Based Characterization of Defects in Magnetic Flux Leakage (MFL) Image
    JPJAA Jackson Daniel I
    International Journal of Applied Engineering Research 10 (1), 15593-15597 , 2015
    2015
  • Characterization of Defects in Magnetic Flux Leakage Images
    S Sangeetha, Jackson Daniel I, Abudhahir A, Gokul R
    International Journal of Computer Applications, 16-20 , 2013
    2013