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Ana G. Sanchez-Reyna, Jose M. Celaya-Padilla, Carlos E. Galvan-Tejada, Huizilopoztli Luna-Garcia, Hamurabi Gamboa-Rosales, Andres Ramirez-Morales, Jorge Galvan-Tejada
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Engineering, Electrical & Electronic
Mahdi Ajdani, Hamidreza Ghaffary
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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
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A. Ponmalar, V Dhanakoti
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Javad Hassannataj Joloudari, Faezeh Azizi, Mohammad Ali Nematollahi, Roohallah Alizadehsani, Edris Hassannatajjeloudari, Issa Nodehi, Amir Mosavi
Summary: This study proposed a hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA) for diagnosing coronary artery disease (CAD). Through testing on a dataset, it was found that this model achieved the highest accuracy and outperformed other methods. The results demonstrated that support vector machine combined with genetic optimization algorithm could improve the accuracy of CAD diagnosis.
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Pelin Akin
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Alok Kumar Shukla
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Computer Science, Artificial Intelligence
Dzelila Mehanovic, Dino Keco, Jasmin Kevric, Samed Jukic, Adnan Miljkovic, Zerina Masetic
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NEURAL COMPUTING & APPLICATIONS
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Computer Science, Artificial Intelligence
Qiao Jin
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Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
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INFORMATION SCIENCES
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Zahid Halim, Muhammad Nadeem Yousaf, Muhammad Waqas, Muhammad Sulaiman, Ghulam Abbas, Masroor Hussain, Iftekhar Ahmad, Muhammad Hanif
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COMPUTERS & SECURITY
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Jie Zhang, Jinguang Sun, Hua He
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Computer Science, Artificial Intelligence
Tong Gao, Hao Chen
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Computer Science, Information Systems
Wenjuan Wang, Xuehui Du, Dibin Shan, Ruoxi Qin, Na Wang
Summary: This study utilizes deep learning to automatically extract essential feature representations in the cloud computing environment and designs a novel cloud intrusion detection system. By integrating deep learning and shallow learning techniques, it effectively reduces analytical overhead and achieves higher detection performance on intrusion detection evaluation datasets.
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