Jac Fredo Agastinose Ronickom

According to our database, Jac Fredo Agastinose Ronickom authored at least 19 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning.

Sriram Kumar P, Jac Fredo Agastinose Ronickom

Int. J. Neural Syst., May, 2024

2023

Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach.

Gokul Manoj, Sandeep Singh Sengar, Jac Fredo Agastinose Ronickom

CoRR, 2023

Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System.

Jahanvi Jeswani, Praveen Kumar Govarthan, Abirami Selvaraj, A. Amalin Prince, John Thomas, Mohanavelu Kalathe, Venkat Sreeraj V. Se Subramaniam, Jac Fredo Agastinose Ronickom

Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Differential Gene Expression Data Analysis of ASD Using Random Forest.

Praveen Kumar Pragyaa Govarthan, Kshitij Sinha, Sudip Mukherjee, Jac Fredo Agastinose Ronickom

Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning.

Sriram Kumar P, Praveen Kumar Govarthan, Nagarajan Ganapathy, Jac Fredo Agastinose Ronickom

Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.

Abdul Aleem Shaik Gadda, Dhanvi Vedantham, John Thomas, Yuvaraj Rajamanickam, Ramshekhar N. Menon, Jac Fredo Agastinose Ronickom

Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Multi-Class Seizure Type Classification Using Features Extracted from the EEG.

Abirami Selvaraj, Swarubini PJ, John Thomas, Yuvaraj Rajamanickam, Ramshekhar N. Menon, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression.

Ravi Ratnaik, Chetan Rakshe, Manoj Kumar, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

Diagnostic Classification of ASD Using Fractal Functional Connectivity of fMRI and Logistic Regression.

Chetan Rakshe, Suja Kunneth, Soumya Sundaram, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

Automated Emotion Recognition System Using Blood Volume Pulse and XGBoost Learning.

Lokesh Naidu Lebaka, Praveen Kumar Sriram Kumar Pa Govarthan, Priya Rani, Nagarajan Ganapathy, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

Identifying the Optimal Location of Facial EMG for Emotion Detection Using Logistic Regression.

Praveen Kumar Sriram Kumar Pa Govarthan, Swarubini PJ, Mythili Aasaithambi, Nagarajan Ganapathy, Karthik Pa, Deepesh Kumar, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals.

Praveen Kumar Govarthan, Nagarajan Sriram Kumar Pa Ganapathy, Jac Fredo Agastinose Ronickom

Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

2022

Time-Sliced Architecture for Efficient Accelerator to Detrend High-Definition Electroencephalograms.

Rakshit Mittal, A. Amalin Prince, Agastinose Ronickom Jac Fredo

IEEE Trans. Instrum. Meas., 2022

Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images.

Sreelakshmi Shaji, Jac Fredo Agastinose Ronickom, Anandh Kilpattu Ramaniharan, Ramakrishnan Swaminathan

Expert Syst. Appl., 2022

Automated Diagnosis of Autism Spectrum Disorder Condition Using Shape Based Features Extracted from Brainstem.

Vaibhav Jain, Abirami Selvaraj, Rakshit Mittal, Priya Rani, Anandh Kilpattu Ramaniharan, Jac Fredo Agastinose Ronickom

Proceedings of the Challenges of Trustable AI and Added-Value on Health, 2022

2021

Low-Power Hardware Accelerator for Detrending Measured Biopotential Data.

Rakshit Mittal, A. Amalin Prince, Saif Nalband, Femi Robert, Agastinose Ronickom Jac Fredo

IEEE Trans. Instrum. Meas., 2021

Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

Maya A. Reiter, Afrooz Jahedi, Agastinose Ronickom Jac Fredo, Inna Fishman, Barbara A. Bailey, Ralph-Axel Müller

Neural Comput. Appl., 2021

Modified-MaMeMi filter bank for efficient extraction of brainwaves from electroencephalograms.

Rakshit Mittal, A. Amalin Prince, Saif Nalband, Femi Robert, Agastinose Ronickom Jac Fredo

Biomed. Signal Process. Control., 2021

2014

Segmentation and analysis of brain subcortical regions using regularized multiphase level set in autistic MR images.

Agastinose Ronickom Jac Fredo, G. Kavitha, S. Ramakrishnan

Int. J. Imaging Syst. Technol., 2014