Abdulhamit Subasi

Effat University, Computer Science, Faculty Member

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Dr. Abdulhamit Subasi graduated from Hacettepe University in 1990. He took his M.Sc. degree from Middle East Technical University in 1993, and his Ph.D. degree from Sakarya University in 2001, all in Electrical and Electronics Engineering. From 2001 until 2009 he worked as an Assistant Professor in the Department of Electrical and Electronics engineering at Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey. In 2006 he was senior researcher at Georgia Institute of Technology, School of Electrical and Computer Engineering, Georgia, USA. He worked as a visiting Assistant Professor at International University of Sarajevo, Sarajevo, Bosnia and Herzegovina from 2008 to 2009. He is appointed as an Associate Professor of Information Technology. He worked as a Dean of Engineering Faculty at International Burch University, Sarajevo, Bosnia and Herzegovina from 2009 to 2013. He is appointed as a Professor of Electrical and Electronics Engineering at International Burch University, Sarajevo, Bosnia and Herzegovina at 2012. Since 2015, he is working as a Professor of Computer Science at Effat University, Jeddah, Saudi Arabia. His areas of interest are data mining, machine learning, pattern recognition, biomedical signal processing, computer networks and security. He has worked on several projects related with biomedical signal processing and pattern classification. Dr. Subasi has served as a program organizing committee member of the national and international conferences. He is editorial board member of several scientific journals. Moreover, he is voluntarily serving as a technical publication reviewer for many respected scientific journals and conferences. He has lots of published journal and conference papers on his research areas. He has more than 900 ISI Web of Science citations and 2400 Google citations to his publications.

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Practical Machine Learning for Data Analysis Using Python

Biomedical Signal Analysis and Its Usage in Healthcare

Biomedical Engineering and its Applications in Healthcare

Biomedical signals are collected from a body that can be at the organ level, cell level, or molec... more Biomedical signals are collected from a body that can be at the organ level, cell level, or molecular level. There are different biomedical signals including the electroencephalogram (EEG), which is the electrical activity from the brain; the electrocardiogram (ECG), which is the electrical activity from the heart; the electromyogram (EMG), which is the electrical activity from the muscle sound signals; the electroneurogram; the electroretinogram from the eye; and so on (Muthuswamy, Biomedical signal analysis. In: Myer Kutz (ed) Standard handbook of biomedical engineering and design, vol 14. McGraw-Hill Education, New York, pp 1–18. 2004). Biomedical signals are primarily used to diagnose or detect specific pathological or physiological conditions. Additionally, these signals are employed to analyze biological systems in the healthcare. The aims of signal processing are signal denoising, precise recognition of signal model through analysis, feature extraction and dimension reduction for decisive function or dysfunction, and prediction of future functional or pathological events by employing machine learning techniques. The objective of this chapter is to present how biomedical signals are used in the healthcare and what are the steps of biomedical signal analysis.

Intrusion Detection in Smart Healthcare Using Bagging Ensemble Classifier

IFMBE Proceedings

Critical infrastructure systems such as ehealthcare, which are crucial to government, society, an... more Critical infrastructure systems such as ehealthcare, which are crucial to government, society, and commerce, are directly related to people's lives. In order to offer extremely secured critical infrastructure systems, a reliable, scalable, and robust threat detection system must be established to effectively alleviate cyber threats. Moreover, big data from threat monitoring systems cause severe trials for cyber-attack since increasing number of devices in the structure and the quantity of monitoring data accumulated from IoT based healthcare devices need to prevent cyberattacks. Intrusion detection systems, or IDSs, have become an important component in maintaining the security on a corporate network. The popularity of Internet usage comprises many risks of network attacks. Intrusion detection aims to discover unusual access or attacks to secure internal networks. To build our proposed intrusion detection system, Machine learning techniques are utilized with benchmark attack data. In this paper, Bagging ensemble classifier for intrusion detection in a smart healthcare environment is proposed. Bagging ensemble classifier with Random Forest achieved better performance (97.67% accuracy) as compared to its counterpart classifier models for intrusion detection in a smart healthcare with a benchmark data. Experimental results have revealed the practicality of Bagging ensemble classifier by accomplishing a better performance for the cyber security threats in a smart healthcare environment.

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