M
Mohammed Diykh
Researcher at University of Southern Queensland
Publications - 24
Citations - 710
Mohammed Diykh is an academic researcher from University of Southern Queensland. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 9, co-authored 16 publications receiving 407 citations. Previous affiliations of Mohammed Diykh include Thi Qar University.
Papers
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Journal ArticleDOI
EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity
Mohammed Diykh,Yan Li,Peng Wen +2 more
TL;DR: The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier.
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An Efficient DDoS TCP Flood Attack Detection and Prevention System in a Cloud Environment
TL;DR: The proposed CS_DDoS system offers a solution to securing stored records by classifying the incoming packets and making a decision based on the classification results, which yields the best performance when the LS-SVM classifier is adopted.
Journal ArticleDOI
Complex networks approach for EEG signal sleep stages classification
Mohammed Diykh,Yan Li +1 more
TL;DR: The research results indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders and provide better EEG sleep signals classification compared with the existing approaches reported.
Journal ArticleDOI
Classification of epileptic EEG signals based on simple random sampling and sequential feature selection
TL;DR: A new method which extracts and selects features from multi-channel EEG signals using simple random sampling technique and sequential feature selection algorithm to select the key features and to reduce the dimensionality of the data.
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Classify epileptic EEG signals using weighted complex networks based community structure detection
TL;DR: The proposed method was efficient in detecting epileptic seizures in EEG signals using a least support vector machine, k -means, Naive Bayes, and K -nearest classifiers.