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Priority Detector and Classifier Techniques Based on ML for the IoMT

Computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： rayan a.alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

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Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

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Computers, Materials & Continua 2020年第11期65卷 1097-1111页

作者： Iftikhar Ahmad rayan Atteah alsemmeari D.I.T Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

An IDS(intrusion detection system)provides a foremost front line mechanism to guard networks,systems,data,and ***’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security *** techniques have been in use but major concern in their implementation is variation in their detection *** performance of IDS lies in the accurate detection of attacks,and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms *** overcome this problem many researchers have used different machine learning *** techniques have limitations and do not efficiently perform on huge and complex data about systems and *** work focused on ELM(Extreme Learning Machine)technique due to its good capabilities in classification problems and dealing with huge *** ELM has different activation functions,but the problem is to find out which function is more suitable and performs well in *** work investigates this ***,Well-known activation functions like:sine,sigmoid and radial basis are explored,investigated and applied to measure their performance on the GA(Genetic Algorithm)features subset and with full features *** NSL-KDD dataset is used as a *** empirical results are analyzed,addressed and compared among different activation functions of the *** results show that the radial basis and sine functions perform better on GA feature set than the full feature set while the performance of the sigmoid function is almost equal on both features ***,the proposal of GA based feature selection reduced 21 features out of 41 that brought up to 98%accuracy and enhanced overall efficiency of extreme learning machine in intrusion detection.

关键词： Accuracy extreme learning machine sine function sigmoid function radial basis genetic algorithm NSL-KDD

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