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  • Open Access


    High Performance Classification of Android Malware Using Ensemble Machine Learning

    Pagnchakneat C. Ouk1, Wooguil Pak2,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 381-398, 2022, DOI:10.32604/cmc.2022.024540

    Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts… More >

  • Open Access


    Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques

    A. Arunkumar1,*, D. Surendran2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 389-404, 2022, DOI:10.32604/csse.2022.020256

    Abstract Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning… More >

  • Open Access


    Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

    Jae-Hyun Ro1, Jong-Gyu Ha2, Woon-Sang Lee2, Young-Hwan You3, Hyoung-Kyu Song2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3625-3636, 2022, DOI:10.32604/cmc.2022.020596

    Abstract This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned More >

  • Open Access


    Ensemble Machine Learning Based Identification of Pediatric Epilepsy

    Shamsah Majed Alotaibi1, Atta-ur-Rahman1, Mohammed Imran Basheer1, Muhammad Adnan Khan2,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 149-165, 2021, DOI:10.32604/cmc.2021.015976

    Abstract Epilepsy is a type of brain disorder that causes recurrent seizures. It is the second most common neurological disease after Alzheimer’s. The effects of epilepsy in children are serious, since it causes a slower growth rate and a failure to develop certain skills. In the medical field, specialists record brain activity using an Electroencephalogram (EEG) to observe the epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate due to human errors; therefore, automated detection of epileptic pediatric seizures might be the optimal solution. This paper investigates… More >

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