List of Book Publications

1. Abirami S, John T, Yuvaraj R and Jac Fredo AR (2021), “A comparative study on EEG features for neonatal seizure detection”, edited book chapter in Springer, titled as, Biomedical signal-based computer aided diagnosis for neurological disorders.

List of Journal Publications

1. Chetan R, Suja K, Soumya S, Murugappan M, Jac Fredo AR (2024), ‘Autism Spectrum Disorder Diagnosis using Fractals and Non-Fractals based Functional Connectivity Analysis and Machine Learning Methods’, Neural Computing and Applications (Springer publishers, IF: 6).

2. Pragya, Praveen Kumar Govarthan, Malay Nayak, Sudip Mukherjee,  Jac Fredo AR (2024), ‘Establishment of three gene prognostic markers in pancreatic ductal adenocarcinoma using machine learning approach’, Journal of Medical and Biological Engineering (Springer publishers, IF: 2).
3. Gokul M, Vaibhavi G, Aditi B, Shaik GAA, Dhanvi V, Amalin PA, Jac Fredo AR (2024), “Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features”, Multimedia Tools and Applications (Springer publishers, IF: 3.6)

4. Sriram Kumar P and Jac Fredo AR (2024) 'Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-based Feature Extraction with Machine Learning Approach', International Journal of Neural Systems  (World Scientific publishers, IF: 8)

5. Ankit Kumar M, Aseem S, Abhishesh Kumar M, Vishnu Priya, Nikitha Lakshmi Suseela M, Patharaj G, Jac Fredo AR, Shreyans Kumar J, Joseph S, Muthu MS (2024), 'Green analytical chemistry: Experimental and chemometric methods for the detection of therapeutics using liquid chromatography in wastewater samples', Analytical Chemistry Letters, vol. 14 (Taylor & Francis publishers, IF: 2.267)

6. Vaibhav J, Chetan TR, Sandeep SS, Murugappan M and Jac Fredo AR (2023) 'Age and Severity Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures', Arabian Journal for Science and Engineering (Springer publishers, IF: 2.9)

7. Sudharshan R, Kshitij S, Pragya P, Gowri MB, Jac Fredo AR  (2023) ‘Comparing Pertubagens from Differential Gene Expression Data Analysis of ASD using Random Forest and Statistical Test’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 682-685 (De Gruyter publishers)

8. Devesh J, Praveen Kumar G, Abirami S, Christy T, John T, Jac Fredo AR (2023) ‘A feasibility study on using EEG for Biometric Trait Authentication System’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 690-693 (De Gruyter publishers)

9. Swarubini PJ, Sriram Kumar P, Praveen Kumar G, Meghraj P, Parbani D, Shivabhijit S, Mythili A, Jac Fredo AR (2023) ‘ECG based Categorical emotion classification using time-domain features and Machine Learning’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 694-697 (De Gruyter publishers)

10. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘A comparative analysis of EDA decomposition methods for improved emotion recognition’, Journal of Mechanics in Medicine and Biology (World Scientific publishers, IF:0.8)

11. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘Electrodermal activity based analysis of emotion recognition using temporal-morphological features and machine learning algorithms’, Journal of Mechanics in Medicine and Biology  (World Scientific publishers, IF: 0.8)

12. Manoj K, Chandrakanta H, Sunil Kumar K, Eshita B, Kommu John VS, Hansashree P, Akhila SG, Shweta N, Thomas Kishore M, Yamini BK, Jac Fredo AR, Jitender S, Rose Dawn B (2023) ‘Altered cerebellar lobular volumes correlate with clinical deficits in siblings and children with ASD: evidence from toddlers’ Journal of Translational Medicine, 21 (1), 246 (BMC publishers, IF: 8.448)

13. Aditi B, Gokul M, Vaibhavi G, Abdul ASG, Dhanvi V, Amalin PA. Priya R, Anandh KR, Jac Fredo AR (2022) ‘Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms’, International Journal of Biomedical Engineering and Technology (Inderscience publishers)

14. Rohan Kotha, Priya Rani, Femi Robert, Christy Bobby Thomas, Suresh Kumar C, Jac Fredo AR (2022) ‘Damage monitoring in fibre-reinforced polymer composites using adaptive threshold methods and geometric features’, Journal of the Brazilian Society of Mechanical Sciences and Engineering (Springer publishers, IF: 2.361)

15. S Shaji, Jac Fredo AR, AK Ramaniharan, R Swaminathan (2022) ‘Study on the Effect of Extreme Learning Machine and its Variants in Differentiating Alzheimer Conditions from Selective Regions of Brain MR Images, Expert Systems with Applications, 118250 (Elsevier publishers, IF: 8.665)

16. Rakshit Mittal, Amalin Prince A, Jac Fredo AR (2022) ‘Time-sliced architecture for efficient accelerator to detrend high-definition electroencephalograms’, IEEE Transactions on Instrumentation and Measurement, vol. 71 (IEEE publishers, IF: 5.6).

17. Femi R, Amalin Prince A,  Jac Fredo  AR (2022) ‘Investigation of using CNT and Cu/CNT Wires for Replacing Cu for Power Electronics and Electrical Applications', ECS Journal of Solid State Science and Technology, Vol. 11, No. 2 (IOP Science publishers, IF:2.2)

18. Femi Robert, Amalin Prince A, JacFredo AR (2021), ‘Influence of wire electrical discharge machine cutting parameters on the magnetization characteristics of electrical steel laminations’ Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2021.10.141 (Elsevier publishers, IF: 1.24)

19. Amalin Prince, Rakshit Mittal, Saif Nalband, Femi Robert, Jac Fredo AR (2021), ‘Modified-MaMeMi Filter Bank for Efficient Extraction of Brainwaves from Electroencephalograms’, Biomedical Signal Processing & Control, https://doi.org/10.1016/j.bspc.2021.102927 (Elsevier publishers, IF: 5.1)

20. Rakshit M, Amalin PA, Saif N, Femi R, & Jac Fredo AR (2020), ‘Low-power hardware accelerator for detrending measured biopotential data’, IEEE Transactions on Instrumentation and Measurement. (IEEE publishers, IF: 5.6)

21. Jac Fredo AR, John T, Prasanth T, Vineetha K, Georg L & Justin D (2020), ‘Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation’, Journal of Neuroscience Methods, https://doi.org/10.1016/j.jneumeth.2020.108884 (Elsevier publishers, IF: 2.785)

22. Maya AR, Afrooz J, Jac Fredo AR, Inna F, Barbara B & Ralph-Axel M (2020), ‘Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity’, Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05193-y (Springer publishers, IF: 4.774)

23. Jac Fredo AR, Abilash RS, Femi R, Sri Madhava Raja N, & Suresh Kumar C (2020) ‘Automated damage detection and characterization of polymer composite images using Tsallis-particle swarm optimisation based multi-level threshold and multifractals’ Polymer Composites, https://doi.org/10.1002/pc.25611 (Wiley publishers, IF: 2.268)

24. Suresh Kumar C, Arumugam V, Kenned JJ, Karthikeyan R, & Jac Fredo AR (2019) ‘Experimental investigation on the effect of glass fiber orientation on impact damage resistance under cyclic indentation loading using AE monitoring’ Nondestructive Testing and Evaluation, https://doi.org/10.1080/10589759.2019.1684491 (Taylor and Francis publishers, IF: 1.735)

25. Femi R, Sharma A, Katare H, & Jac Fredo AR (2019) ‘Investigation of graphene as a material for electrical contacts in the application of microrelays using finite element modeling’ Materials Research Express, vol. 6, no. 9, pp. 1-14 (IOP Science publishers, IF: 1.449)

26. Jac Fredo AR, Abilash RS, Femi R, Sri Madhava Raja N, & Suresh Kumar C (2019), ‘Characterization of global and local damages in composite images using geometrical and Fourier-Hu moment based shape descriptors’, Journal of Testing and Evaluation, https://doi.org/10.1520/JTE20180701 (ASTM International publishers, IF: 0.711)

27. Jac Fredo AR, Abilash RS, Femi R, Mythili A, & Suresh Kumar C (2018), ‘Classification of damages in composite images using Zernike moments and support vector machines’, Composites B: Engineering, vol. 168, pp. 77-86. (Elsevier publishers, IF: 13.1)

28. Jac Fredo AR, Abilash RS, & Suresh Kumar C (2017), ‘Segmentation and analysis of damages in composite images using multi-level threshold methods and geometrical features’, Measurements, vol. 100, pp. 270-278. (Elsevier publishers, IF: 5.6)

29. Jac Fredo AR, Josena TR, Rajkumar E, & Mythili A (2017), ‘Classification of normal and knee joint disorder vibroarthrographic signals using multi fractals and support vector machines’, Biomedical Engineering: Applications, Basis and Communications, vol. 29, no. 3, pp. 1-9. (World Scientific publishers)

30. Karthick PA, Navaneethakrishna M, Punitha N, Jac Fredo AR, & Ramakrishnan S (2016), ‘Analysis of muscle fatigue conditions using time-frequency images and GLCM features’, Current Directions in Biomedical Engineering, vol. 2, pp. 1-4. (De Gruyter publishers)

31. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Automated segmentation and analysis of corpus callosum in autistic MR images using fuzzy c-means based level set’, Journal of Medical and Biological Engineering, vol. 35, no. 3, pp. 331-337. (Springer publishers, IF: 1.211) 

32. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Segmentation and analysis of corpus callosum in autistic MR brain images using reaction diffusion level sets’, Journal of Medical Imaging and Health Informatics, vol. 5, pp. 1-5. (American Scientific publishers, IF: 0.549)

33. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Subcortical region segmentation using fuzzy based augmented Lagrangian multiphase level set method in autistic MR brain images’, Biomedical Sciences Instrumentation, vol. 51, pp. 323-331.  (Instrumentation Society of America)

34. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2014), ‘Segmentation and analysis of brain sub-cortical regions using regularized multi-phase level set in autistic MR Images’, International Journal of Imaging Systems and Technology, vol. 24, no. 3, pp. 256-262. (Wiley publishers, IF: 1.423)

35. Jac Fredo AR, Kavitha G & Ramakrishnan S (2014), ‘Segmentation and morphometric analysis of sub-cortical regions in autistic MR brain images using fuzzy Gaussian model based distance regularized multi-phase level set’, International Journal of Biomedical Engineering and Technology, vol.15, no.3, pp. 211-223. (Inderscience publishers)

36. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2014), ‘Analysis of cortical and subcortical regions in autistic MR images using level set method and structure tensors’, Biomedical Sciences Instrumentation, vol. 50, pp. 140-149. (Instrumentation Society of America)

List of Conference Publications

1. Vaibhav Jain, Sandeep Singh Sengar, Jac Fredo AR (2023) ‘Age-specific diagnostic classification of autism spectrum disorder using deep learning approaches’, Studies in Health Technology and Informatics, 309, 267-271.
2. Vaibhavi Gupta, Gokul Manoj, Aditi Bhattacharya, Sandeep Singh Sengar, Rakesh Mishra, Bhoomika R. Kar, Chhitij Srivastava, Jac Fredo AR (2023) ‘A framework to diagnose autism spectrum disorder using morphological connectivity of sMRI and XGBoost’ Studies in Health Technology and Informatics, 309, 33-37.
3. Praveen Kumar G, Sriram Kumar P, Nagarajan G, Jac Fredo AR (2023) ‘Investigating Windowing Techniques in Emotion Classification with ECG and Machine Learning’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
4. Tikaram, Manasi BI, Chetan R, Jac Fredo AR (2023) ‘Advancing ASD Diagnostic Classification with features of Continuous Wavelet Transform of fMRI and machine learning algorithms’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
5. Nivedhitha S, Akshaya MV, Praveen Kumar G, Ramesh M, Anandh KR, Jac Fredo AR (2023) ‘Deep learning based Diagnosis of ASD using Pretrained Convolutional Neural Network’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
6. Sriram Kumar P, Jac Fredo AR (2023) ‘Investigating the Effects of Two-Class Categorical Emotion Classification through Electrodermal activity and Machine Learning’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
7. Pragya, Praveen Kumar G, Malay Nayak, Sudip Mukherjee, Jac Fredo AR (2023) 'Implantable immunoprotective hydrogels containing engineered cells secreting ATP2A3 for the treatment of PDAC', International Conference on Advanced Materials for Better Tomorrow, Varanasi, India
8. Sanju Kumari, Jac Fredo AR, Shreyans Kumar Jain (2023) 'Extraction and Purification of New Compounds from Murraya paniculata to investigate their cytotoxic potential', International Conference on seperation and purification technologies, IIT Patna, India.
9. Sriram Kumar P, Jac Fredo AR (2023) ‘Enhancing Emotion Recognition: Machine Learning with Phasic Spectrogram Texture Features’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.

10. Praveen Kumar G, Sriram Kumar P, Nagarajan G, Jac Fredo AR (2023) ‘Deep Learning Framework for Categorical Emotional States Assessment using Electrodermal Activity Signals’, Studies in Health Technology and Informatics, 305, 40-43.

11. Lokesh NL, Sriram Kumar P, Praveen Kumar G, Priya R, Nagarajan G, Jac Fredo AR  (2023) ‘Automated Emotion Recognition System using Blood Volume Pulse and XGBoost Learning’, Studies in Health Technology and Informatics, 305, 52-55.

12. Chetan R, Suja K, Soumya S, Jac Fredo AR  (2023) ‘Diagnostic Classification of ASD using Fractal Functional Connectivity of fMRI and Logistic Regression’, Studies in Health Technology and Informatics, 305, 60-63.

13. Ravi R, Chetan R, Manoj K, Jac Fredo AR  (2023) ‘Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression’, Studies in Health Technology and Informatics, 305, 64-67

14. Abirami S, Swarubini PJ, John T, Yuvraj R, Ramshekhar NM, Jac Fredo AR (2023) ‘Multi-class Seizure type Classification using Features Extracted from the EEG’, Studies in Health Technology and Informatics, 305, 68-71.

15. Vinay Kumar B, Sriram Kumar P, Praveen Kumar G, Swarubini PJ, Mythili A, Nagarajan G, Karthick PA, Deepesh K, Jac Fredo AR (2023) ‘ Identifying the Optimal Location of Facial EMG for Emotion Detection using Logistic Regression’, Studies in Health Technology and Informatics, 305, 81-84.

16. Jahanvi J, Praveen Kumar G, Abirami S, Amalin P, John T, Mohanavelu K, Venkat S, Jac Fredo AR ‘Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System’, Studies in Health Technology and Informatics, 302, 257-261.

17. Shaik Gadda AA, Dhanvi V, John T, Yuvaraj R, Ramshekhar NM, Jac Fredo AR (2023) ‘Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.’ Studies in Health Technology and Informatics, 302, 232-236.

18. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning’, Studies in Health Technology and Informatics, 302, 73-77.

19. Pragya, Praveen Kumar G, Kshitij S, Sudip M, Jac Fredo AR (2023) ‘Differential Gene Expression Data Analysis of ASD Using Random Forest’, Studies in Health Technology and Informatics, 302:1047-1051.

20. Chetan Tanaji Rakshe and Jac Fredo AR (2022) ‘Investigation of brain networks in autism using fractal, non-fractal and Pearson correlation methods’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

21. Sriram Kumar P and Jac Fredo AR (2022) ‘Classification of emotional states using electrodermal activity and random forest’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

22. Abirami S, John Thomas, Rajamanickam Yuvaraj, Jac Fredo AR (2022) ‘Characterization of seizure subtypes using time-frequency features from scalp EEG signals’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

23. Aditi Bhattacharya, Gokul Manoj, Vaibhavi Gupta, Shaik Gadda Abdul Aleem, Amalin Prince A, Priya Rani, Jac Fredo AR (2022) ‘Evaluation of Zernike moments of corpus callosum for discrimination of autism using Random Forest’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

24. Praveen kumar G, Nagarajan Ganapathy and Jac Fredo AR (2022) ‘Deep-learning framework for ECG based categorical emotional states assessment’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

25. Aalan Natarajan, Deepesh Kumar and Jac Fredo AR (2022) ‘Vision-guided autonomous robotic system for pick and place tasks in healthcare settings’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

26. Vaibhav Jain, Abirami Selvaraj, Rakshit Mittal, Priya Rani, Anandh Kilpattu Ramaniharan, Jac Fredo AR (2022) ‘Automated diagnosis of autism spectrum disorder condition using shape-based features extracted from brainstem’, Challenges of Trustable AI and Added-Value on Health,European Federation for Medical Informatics (EFMI), 32nd Medical Informatics Europe Conference (MIE2022), Nice, France, May 27-30, 53-57.

27. Arijit Gupta, Amalin Prince A, Jac Fredo AR, Femi Robert, Transformer-based models for supervised monocular depth estimation, International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 2022, pp. 1-5.

28. Edwin M, Saif N, Jac Fredo AR, & Amalin Prince A 2020, ‘Analysis and classification of vibroarthrographic signals using tuneable ‘Q’wavelet transform’, 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp.65-70.

29. Jac Fredo AR, Georg L. & Justin D, 2019, ‘Classification of typical developing and autism spectrum disorder using connectivity matrix and support vector machines’, IEEE International conference on Engineering in Medicine and Biology, Berlin, Germany.

30. Jac Fredo AR, Maya R, Afrooz J & Ralph AM, 2018, ‘Classification of autism and control on resting state fMRI using conditional random forest’, IEEE International conference on Engineering in Medicine and Biology, Honolulu, Hawaii.

31. Jac Fredo AR,  Kavitha G & Ramakrishnan S 2015 ‘Analysis of corpus callosum and its sub-anatomical regions in autistic MR brain images using structure tensors’, Second International Conference on Biomedical Systems, Signals and Images, IIT Madras, 2015. 

32. Jac Fredo AR, Kavitha G & Ramakrishnan S 2014, ‘Segmentation of sub-cortical regions of autistic MR brain images using combination of fuzzy c-means and Gaussian distribution model based augmented Lagrangian multi-phase level sets’, Nineteenth International Conference on Mechanics in Medicine and Biology, Bologna, Italy, pp. 431-434, ISBN: 978-88-901675-1-5.

33. Jac Fredo AR,  Kavitha G & Ramakrishnan S 2014, ‘Analysis of sub-cortical regions in cognitive processing using fuzzy c-means clustering and geometrical measure in autistic MR images’, 40th North East Bio-Engineering Conference, Boston, United States of America, pp. 1-2, doi: 10.1109/NEBEC.2014.6972791. 

34. Jac Fredo AR, Kavitha G & Ramakrishnan S 2014, ‘Segmentation and analysis of subcortical regions of autistic MR brain images using Gaussian distribution model based reaction diffusion multi-phase level sets and geometric feature’, Frontiers in Neuroinformatics, doi: 10.3389/conf.fninf.2014.18.00090. Leiden, The Netherlands

35. Jac Fredo AR, Kavitha G & Ramakrishnan S 2014, ‘Characterization of autistic MR brain images using fuzzy c-means based reaction diffusion multi-phase level sets and structure tensor’, National Conference on Present Scenario and Future Trends in Biomedical Engineering and Healthcare Technologies, IIT Varanasi, India. 

36. Jac Fredo AR, Ramakrishnan S, Jaginth C, & Srinivasan S 2010, ‘Virtual Audiometer‘, International Conference on Instrumentation, Instrumentation Society of India, Pune, India.

37. Jac Fredo AR 2007, ‘SCADA-Automation in thermal power plant‘, Conference on Efactory Technologies and Challenges, IRTT Erode, India.

Grants and Funding:
 

Current Team

Collaborators
 

Aluminees 

IIT (BHU)

VIT University:

Post-doctoral Positions

Postdoc candidates (having fellowship from CSIR, DST, DBT etc.)  or wish to apply may contact me by sending detailed CV with research interests.(Candidates with Engineering backgrounds are preferred)

Ph.D. Positions

Ph.D. positions are available in my lab for students who have qualified CSIR/UGC/DBT JRF/ DST inspire fellowship. Interested candidate have to follow guidelines of IIT-BHU PhD admission (Candidates with Engineering backgrounds or proficiency in coding platforms like Matlab, Python or Rstudio are preferred)

Summer or Winter internships

Summer or Winter internships are available in my lab for Masters and Undergraduate students. Candidates can sent their CV and project proposal.

1. We have EDA, ECG, EMG and EEG sensors which can record the physiological signals from the subjects during different experiments. 
2. We have two high computing workstations which helps us to process the computationally complex dimensional data.
3. Abdul Aleem, a Fourth-year student awarded MITACS Globalink Research Internship to pursue research under Dr. Albert Vette, Neuromuscular Control & Biomechanics Laboratory, University of Alberta, Canada, from May 2023 to July 2023.
4. Lokesh Lebaka, a Fourth-year student awarded ANU-FRT (Future Research Talent) program Internship to pursue research under Dr. Gaetan Burgio in Gene Editing Lab, Australian National University, Australia, from May 2023 to July 2023.
5. Successfully conducted seven-dayshop program on "AI for Biomedical Applications" on 22/05/2023-28/05/2023 in the ABLT IIT (BHU), Vananasi (Served as the co-convener).
6. Successfully conducted one day training program on "Introduction to Computational Neuroscience-2022" on 09/09/2022 in the seminar hall of school of Biomedical Engineering with Guest Speakers Dr. Manoj Kumar (NIMHANS, Bengaluru), Dr. Yuvraj Rajamanickom (NTU, Singapore) and Dr. Nagarajan (IIT Hyderabad).

7. 

8. Mr. Gokul Manoj, Fourth year IDD student awarded DAAD-Combined Study and Practice Stays for Engineers from Developing Countries (KOSPIE) to pursue research under Prof. Kerstin Konrad, RWTH Aachen University, Aachen, Germany from September 2022 to March 2023.
9. NAS server available in our lab, which permits to access the medical data available in our lab from anywhere around the campus. 
10. UPS available in our lab, which can support the NAS and workstation to get continues power supply. 

1. DPGC Convener, September, 2023-August, 2024

2. School Web Convener, September, 2023-August, 2024

3. DUGC Convener, September, 2022-August, 2023

4. Time table Convener, June, 2022-August, 2023

5. Examination Convener, June, 2022-August, 2023

1. Awarded Distinguished Alumina Award 14/05/2022 by Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, India.