MIAN QAISAR Saeed

Ongoing Research Projects

Event-Driven systems, Signal processing, Machile learning, Deep learning, Features extraction, Dimension reduction, Sampling theory, Bioinformatics, Smart Grid, Battery management systems, Positron emission tomography scanner, logic design, Embedded systems, Time-Frequency analysis, multiresolution analysis, multi rate processing.

Ongoing Research Projects

• Proficient Monitoring System for Li-Ion Batteries State of Health Estimation for Smart Grid Energy Storage (2020-2023) Contribution: Principle Investigator
• Load Recognition by Interpreting the Smart Meter Data: A Perspective for Energy Management in Smart Grid (2020-2023) Contribution: Principle Investigator
• Modeling and Event-Driven Processing Based Elucidation of the Power Quality Disturbances in smart grids (2020-2023) Contribution: Principle Investigator
• Hybridization of Decomposition with Optimization and Ensemble Learning Techniques for Scheziopherinia Detection by Processing the Multivariate EEG Signals  (2022-2023) Contribution: Principle Investigator
• Automated Detection and Classification of Leukemia Using Deep Learning (2022-2024) Contribution: Co Investigator

Research Activities

• Plos One [Éditeur académique]
• https://journals.plos.org/plosone/static/editorial-board, rechercher “Saeed Mian Qaisar” 
• Journal of Healthcare Engineering [Éditeur académique]
• https://www.hindawi.com/journals/jhe/editors/#editorial-board 
• Frontiers in Signal Processing, Biomedical Signal Processing [Éditeur académique]
• https://loop.frontiersin.org/people/1371195/overview 
• Signal Processing Letters, IEEE [Reviewer] 
• Sensors, IEEE [Reviewer]
• Circuits and Systems, IEEE [Reviewer]
• Methods, Elsevier [Reviewer]
• Computer Methods and Programs in Biomedicine, Elsevier [Reviewer]
• Computers in Biology and Medicine, Elsevier [Reviewer]
• Biocybernetics and Biomedical Engineering, Elsevier [Reviewer]
• Journal of Ambient Intelligence and Humanized Computing, Springer [Reviewer]
• Physical and Engineering Sciences in Medicine, Springer [Reviewer]
• Biomedical Signal Processing and Control, Elsevier [Reviewer]
• Applied Energy, Elsevier [Reviewer]
• Journal of Energy Storage, Elsevier [Reviewer]
• Sensors, MDPI [Reviewer]
• Energies, MDPI [Reviewer]
• Electronics, MDPI [Reviewer]
• International Conference on Event Based Control Communication and Signal Processing (EBCCSP) (2015 à aujourd'hui) [Comité technique]
• IEEE International Conference on Signal and Image Processing (ICSIP) (2017-Present) [Comité technique]
• International Conference on Electrical and Computing Technologies and Applications (ICECTA2019)  [Comité technique]
• BioInformatics and Bioengineering (BIBE2020) [Comité technique]
• International Conference on Physics, Mathematics and Statistics (ICPMS2020) [Comité technique]
• The 2nd International Scientific-Practical Conference on "Modern Information Measurement and Control Systems: Problems and Perspectives 2020 (MIMCS’2020)" [Comité technique]
• Learning and Technology Conference (L&T) (2017 à aujourd'hui) [Comité technique]
• International Conference on Graphics and Image Processing (ICGIP) (2020-Present) [Comité technique]
• International Conference on Electrical and Computing Technologies and Applications (ICECTA2019)  [Comité technique]
• International Conference on Electrical and Computing Technologies and Applications (ICECTA2019)  [Comité technique]
• The international conference on Information and Knowledge Systems (ICIKS2023) [Comité technique]

Selected publications

• Hassan, F., Hussain, S. F., & Qaisar, S.M. (2023). Fusion of Multivariate EEG Signals for Schizophrenia Detection using CNN and Machine Learning Techniques. Information Fusion. https://doi.org/10.1016/j.inffus.2022.12.019  17.56 ISI & SCOPUS [Q1]
• Haider, U., Waqas, M., Hanif, M., Alasmary, H., & Qaisar, S. M. (2023). Network load prediction and anomaly detection using ensemble learning in 5G cellular networks. Computer Communications. https://doi.org/10.1016/j.comcom.2022.10.017  5.05 ISI & SCOPUS [Q1]
• Qaisar, S.M., Khan, S.I., Srinivasan, K. and Krichen, M., (2022). Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. Journal of King Saud University- Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2022.05.009  8.84 ISI & SCOPUS [Q1]
• Ali S.U., Waqar A., Aamir M., Qaisar S.M., Iqbal J., (2022). Model Predictive Control of Consensus-based Energy Management System for DC Microgrid. PLOS ONE. https://doi.org/10.1371/journal.pone.0278110  3.75 ISI & SCOPUS [Q1]
• Haroon, F., Aamir, M., Waqar, A., Qaisar, S.M., Ali, S. U., & Almaktoom, A. T. (2023). A Composite Exponential Reaching Law Based SMC with Rotating Sliding Surface Selection Mechanism for Two Level Three Phase VSI in Vehicle to Load Applications. Energies, 16(1), 346.  https://doi.org/10.3390/en16010346  3.25 ISI & SCOPUS [Q1]
• Khan, H., Nizami, I. F., Qaisar, S. M., Waqar, A., Krichen, M., & Almaktoom, A. T. (2022). Analyzing optimal battery sizing in microgrids based on the feature selection and machine learning approaches. Energies, 15(21), 7865. https://doi.org/10.3390/en15217865  3.25 ISI & SCOPUS [Q1]
• Abbas, A., Qaisar, S.M., Waqar, A., Ullah, N. and Al-Ahmadi, A.A., (2022). Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon. Energies. https://doi.org/10.3390/en15103701  3.25 ISI & SCOPUS [Q1]
• Zidi, S., Mihoub, A., Qaisar, S.M., Krichen, M. and Al-Haija, Q.A., (2022). Theft Detection Dataset for Benchmarking and Machine Learning based Classification in a Smart Grid Environment. Journal of King Saud University- Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2022.05.007    8.84 ISI & SCOPUS [Q1]
• Qaisar, S.M., Khan, S.I., Dallet, D., Tadeusiewicz, R. and Pławiak, P., (2022). Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare. Biocybernetics and Biomedical Engineering. https://doi.org/10.1016/j.bbe.2022.05.006     5.69 ISI & SCOPUS [Q1]
• Hassan F., Hussain F., Qaisar S.M., (2022). Epileptic Seizure Detection using a Hybrid 1D CNN-Machine Learning approach from EEG data. Journal of Healthcare Engineering. https://doi.org/10.1155/2022/9579422  3.82 ISI & SCOPUS [Q2]
• Salankar, N. and Qaisar, S.M., (2022). EEG based Stress Classification by using Difference Plots of Variational Modes and Machine Learning. Journal of Ambient Intelligence and Humanized Computing, 1-15. https://doi.org/10.1007/s12652-022-03856-3   3.66 ISI & SCOPUS [Q1]
• Salankar, N., Qaisar, S. M., Pławiak, P., Tadeusiewicz, R., & Hammad, M. (2022). EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning. Biocybernetics and Biomedical Engineering. https://doi.org/10.1016/j.bbe.2021.12.009  5.69 ISI & SCOPUS [Q1]
• Hussain, S. F., & Qaisar, S. M. (2022). Epileptic seizure classification using level-crossing EEG sampling and ensemble of sub-problems classifier. Expert Systems with Applications, 191, 116356. https://doi.org/10.1016/j.eswa.2021.116356      8.67 ISI & SCOPUS [Q1]
• Khan, S. I., Qaisar, S. M., & Pachori, R. B. (2022). Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features. Biomedical Signal Processing and Control, 73, 103445. https://doi.org/10.1016/j.bspc.2021.103445  5.08 ISI & SCOPUS [Q1]
• Narayanasamy, S. K., Srinivasan, K., Qaisar, S. M, & Chang, C. Y. (2021). Ontology-enabled Emotional Sentiment Analysis on COVID-19 Pandemic related Twitter Streams. Frontiers in Public Health.  https://www.frontiersin.org/articles/10.3389/fpubh.2021.798905/full   6.46 ISI & SCOPUS [Q1]
• Qaisar, S. M. (2021). Signal-piloted processing and machine learning based efficient power quality disturbances recognition. Plos one, 16(5), e0252104. https://doi.org/10.1371/journal.pone.0252104  3.24 ISI & SCOPUS [Q1]
• Qaisar, S. M., & Hussain, S. F. (2021). An effective arrhythmia classification via ECG signal subsampling and mutual information based subbands statistical features selection. Journal of Ambient Intelligence and Humanized Computing, 1-15. https://doi.org/10.1007/s12652-021-03275-w  3.66 ISI & SCOPUS [Q1]
• Qaisar, S. M., & Fawad Hussain, S. (2021), Effective Epileptic Seizure Detection by Using Level-Crossing EEG Sampling Sub-Bands Statistical Features Selection and Machine Learning for Mobile Healthcare, Computer Methods and Programs in Biomedicine, 106034. https://doi.org/10.1016/j.cmpb.2021.106034 7.03 ISI & SCOPUS [Q1]
• Qaisar, S. M., Mihoub, A., Krichen, M., &Nisar, H. (2021). Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification. Sensors, 21(4), 1511.https://doi.org/10.3390/s21041511 3.84 ISI & SCOPUS [Q1]
• Subasi, A., & Qaisar, S. M. (2021). The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface. Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/1970769  3.82 ISI & SCOPUS [Q2]
• Mukhtar, H., Qaisar, S. M., & Zaguia, A. (2021). Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals. Sensors, 21(16), 5456. https://doi.org/10.3390/s21165456  3.84 ISI & SCOPUS [Q1]
• Kaliappan, J., Srinivasan, K., Qaisar, S. M., Sundararajan, K., Chang, C. Y., & Suganthan, C. (2021). Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate. Frontiers in Public Health, NA-NA. https://doi.org/10.3389/fpubh.2021.729795  6.46 ISI & SCOPUS [Q1]
• Qaisar, S. M. (2020). Event-Driven Coulomb Counting for Effective Online Approximation of Li-ion Battery State of Charge. Energies, 13(21), 5600.https://doi.org/10.3390/en13215600 3.25 ISI & SCOPUS [Q1]
• Qaisar, S. M., &Subasi, A. (2020). Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques. Physical and Engineering Sciences in Medicine, 43(2), 623-634.https://doi.org/10.1007/s13246-020-00863-6 7.10 ISI & SCOPUS [Q1]
• Qaisar, S. M. (2019). Efficient mobile systems based on adaptive rate signal processing. Computers & Electrical Engineering, 79, 106462.https://doi.org/10.1016/j.compeleceng.2019.106462 4.15 ISI & SCOPUS [Q1]