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Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Computers, Materials & Continua 2023年第2期74卷 3279-3295页

作者： Mahmoud Ragab Maged Mostafa Mahmoud Amer H.Asseri hani choudhry Haitham A.Yacoub Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Cancer Biology Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah22252Saudi Arabia Department of Molecular Genetics and Enzymology Human Genetics and Genome Research InstituteNational Research CentreCairo12622Egypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Cell Biology Department Biotechnology Research InstituteNational Research CentreGiza12622Egypt

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based ***,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical *** study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)*** presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal *** accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image *** addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed ***,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal *** utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

关键词： Colorectal cancer deep transfer learning slime mould algorithm hyperparameter optimization biomedical imaging

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Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

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Computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab F.J.Alsolami hani choudhry Ibrahim Rizqallah Alzahrani Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control Systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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