Dr. Muhammad Awais - Department of Computer Science

Qualifications

PhD (Biomedical, Electrical and System Engineering), University of Bologna, Italy MS (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, Malaysia BS (Hons in Electronic Engineering), Mohammad Ali Jinnah University, Pakistan
Research	Contact	Teaching
Digital Health; Artificial Intelligence; Applied Machine Learning; Data analytics; Biomedical Signal Processing; Internet of Things (IoT); Industry 4.0;	Dr. Mohammad Awais Department of Computer Science Edge Hill University Ormskirk Lancashire L39 4QPPhone: 01695 65 7196 Email: Mohammad AwaisOffice: THF16	Fundamentals of Web Coding Application Development with Java

Bio

Muhammad Awais is a Senior Lecturer at the Department of Computer Science, Edge Hill University, UK. Prior to joining EHU, he worked as a Research Fellow in Artificial Intelligence and Data Analytics in Energy and Environment Institute (EEI), University of Hull, UK; Research Fellow in Digital Health and Machine Learning in School of Psychology, University of Leeds, UK; Research Associate in Digital Health and Data Analytics at the Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Italy and Visiting Researcher in Wearable Computing at GEMS, Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Norway. His research interests are in the domain of data mining, digital health, wearable sensing and analytics, signal processing, application based machine learning and deep learning to develop ICT (Information and communication Technologies) based systems for remote sensing, internet of things, industry 4.0, biomedical and health care domain. He received the B.S. electronic engineering degree from Mohammad Ali Jinnah University, Pakistan, M.S. degree in electrical and electronic engineering from Universiti Teknologi PETRONAS, Malaysia and PhD in Biomedical, Electrical and System Engineering from University of Bologna, Italy. He is member of Institute of Electrical and Electronic Engineers (IEEE), the IEEE Engineering in Medicine and Biology Society (EMBS) and Pakistan Engineering Council (PEC).

External Academic Activities

Member IEEE
Member IEEE EMBS
Registered Engineer PEC
Technical committee member for IoP 2019
Technical committee member for ScalCom 2019

Research Publications

Journal Papers:

M. Raza, M. Awais, K. Ali, N. Aslam, V.V. Paranthaman, M. Imran, “Establishing effective communications in disaster affected areas and artificial intelligence-based detection using social media platform”, in Future Generation Computer Systems, (Accepted), 2020.

M. Raza, M. Awais, N. Singh, M. Imran, S. Hussain, “Intelligent IoT Framework for indoor healthcare monitoring of Parkinson’s Disease Patients”, In IEEE Journal on Selected Areas in Communications, 2020.

M. Bogacz, S. Hess, C. Choudhury, C. Calastri, F. Mushtaq, M. Awais, A. Erath, M.V. Eggermond M. Nazemi,“Cycling in virtual reality: modelling behaviour in an immersive environment” Transportation Letters: The International Journal of Transportation Research, 2020, pp. 1-16.

S. Rahman, M. Irfan, M. Raza, K.M. Ghori, S. Yaqoob, M. Awais, “Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living.”, in International Journal of Environmental Research and Public Health, 17(3), 1082, 2020.

M. Bogacz, S. Hess, C. Choudhury, C. Calastri, F. Mushtaq, M.V. Eggermond, A. Erath, M. Awais, “A Comparison of Cycling Behaviour between Keyboard-Controlled and Instrumented Bicycle Experiments in Virtual Reality”, Transportation Research Record, 2020.

M. Raza, M. Awais, W. Ellahi, H. X. Nguyen, N Aslam, H. Le-minh, “Diagnosis and monitoring of Alzheimer’s patients using classical machine learning and deep learning techniques.” in Expert Systems with Applications, Vol 136, pp. 353-364, 2019.

M. Awais et al. “An Internet of Things based bed-egress alerting paradigm using wearable sensors in elderly care environment.” Sensors, Vol 11, 2498, 2019.

K. M. Ghori, R.A. Abbasi, M. Awais, M. Imran, A. Ullah, L. Szathmary, “Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection.” in IEEE Access, 8, 16033-16048, 2019.

M. Awais, Chiari, L., Ihlen, E. A. F., Helbostad, J. L., & Palmerini, L. “Physical Activity Classification for Elderly People in Free-Living Conditions”. In IEEE journal of biomedical and health informatics (JBHI), 23(1), 197-207, 2019.

M. Irfan, N. Saad, A. Alwadie, M. Awais, M.A. Sheikh, A. Glowacz, V. Kumar, “An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults.” In Journal of Failure Analysis and Prevention, 1-8, 2018.

M. Awais, N. Badruddin, M. Drieberg, “A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability.” In Sensors, vol. 17, no. 9, pp 1991, 2017.

M. Awais, L. Palmerini, A. Bourke, E.A. Ihlen, J. Helbostad, L. Chiari, “Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study”, in Sensors, vol. 16, no. 12, pp. 2105, 2016

Conference Papers:

K. M. Ghori, R.A. Abbasi, M. Awais, M. Imran, A. Ullah, L. Szathmary, “Impact of Feature Selection on Non-technical Loss Detection.” In Proceeding of 6th IEEE Conference on Data Science and Machine Learning Applications (CDMA), pp. 19-24., 2020.

M. Irfan, N. Saad, A. Ali, K.V. Kumar, M.A. Sheikh, M. Awais, “A Condition Monitoring System for the Analysis of Bearing Distributed Faults” in IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0911-0915, 2019.

M. Bogacz, S. Hess, C. Choudhury, C. Calastri, F. Mushtaq, M. Awais, M.V. Eggermond, A. Erath, “Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: application to virtual reality cycling” in International Choice Modelling Conference (ICMC), 2019.

M. Awais, N. Badruddin, M. Drieberg. “EEG Brain Connectivity Analysis to Detect Driver Drowsiness Using Coherence.” IEEE International Conference on Frontiers of Information Technology (FIT), pp. 110-114, 2017.

M. Awais, L. Palmerini, and L. Chiari, “Physical Activity Classification Using Body Worn Inertial Sensors in a Multi-Sensor Setup”, 2nd International Forum on Research and Technologies for Society and Industry (RTSI 2016), Bologna, Italy, pp. 1-4, 2016.

M. Awais, “Physical activity classification meeting daily life conditions.” In Proc. ACM Int. Adjunct Conf. on Pervasive and Ubiquitous Computing, pp. 393-398, 2016.

M. Awais, S. Mellone, L. Chiari. “Physical activity classification meets daily life: Review on existing methodologies and open challenges.” in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5050-3, 2015.

M. Awais, N. Badruddin, M. Drieberg,” Driver Drowsiness Detection Using EEG Power Spectrum Analysis”, in IEEE Region 10 Symposium (TENSYMP), pp. 244-247, 2014.

M. Awais, N. Badruddin, M. Drieberg, “A non-invasive approach to detect drowsiness in a monotonous driving environment.” In IEEE Region 10 Conf. (TENCON), pp. 1-4, 2014.

M. Awais, N. Badruddin, M. Drieberg, “A simulator based study to evaluate driver drowsiness using electroencephalogram.” In Int. Conf. on Intelligent and Advanced Systems (ICIAS), pp. 1-5, 2014.

M. Awais, N. Badruddin and M. Drieberg, “Automated eye blink detection and tracking using template matching,” IEEE Student Conference on Research and Development, Putrajaya, 2013, pp. 79-83.

H. Raza, H.M Shafique, m.Y. Wani, M. Awais. “A modified iterative version of adaptive Kalman channel equalization for multipath fading environment.” in IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (CSUDET), pp. 52-55, 2013.

M. Raza, G. Ahmed, N. M. Khan, M. Awais and Q. Badar, “A comparative analysis of energy-aware routing protocols in wireless sensor networks,” International Conference on Information and Communication Technologies, Karachi, 2011, pp. 1-5.