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  • Open Access


    Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

    Thavavel Vaiyapuri*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998

    Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating… 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 >

  • Open Access


    Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification

    Nany Katamesh, Osama Abu-Elnasr*, Samir Elmougy

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761

    Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by… More >

  • Open Access


    Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

    Xiaoli Qiang1, Muhammad Aamir2,*, Muhammad Naeem2, Shaukat Ali3, Adnan Aslam4, Zehui Shao1

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 841-856, 2021, DOI:10.32604/cmc.2021.012540

    Abstract COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world. Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases. In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July. For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied. EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs). For individual IMFs modelling, we… More >

  • Open Access


    A Multi-Agent Stacking Ensemble Hybridized with Vaguely Quantified Rough Set for Medical Diagnosis

    Ali M. Aseere1,*, Ayodele Lasisi2

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811

    Abstract In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the More >

  • Open Access


    CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images

    Haikel Alhichri*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399

    Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep… More >

  • Open Access


    Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

    Neelum Noreen1,*, Sellapan Palaniappan1, Abdul Qayyum2, Iftikhar Ahmad3, Madini O. Alassafi3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

    Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges… More >

  • Open Access


    Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology

    Jinping Zhang1,2, Youlai Jin1, Bin Sun1,*, Yuping Han3, Yang Hong4

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 755-770, 2021, DOI:10.32604/cmes.2021.012686

    Abstract The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult. Currently, some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, a new time-frequency analysis method based on the empirical mode decomposition (EMD) algorithm, to decompose non-stationary raw data in order to obtain relatively stationary components for further study. However, the endpoint effect in CEEMDAN is often neglected, which can lead to decomposition errors that reduce the accuracy of the research results. In this study, we processed an original runoff sequence using the radial basis… More >

  • Open Access


    Information Theoretic Weighted Fuzzy Clustering Ensemble

    Yixuan Wang1, Liping Yuan2,3, Harish Garg4, Ali Bagherinia5, Parvïn Hamïd6,7,8,*, Kim-Hung Pho9, Zulkefli Mansor10

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 369-392, 2021, DOI:10.32604/cmc.2021.012850

    Abstract In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted… More >

  • Open Access


    Robust Attack Detection Approach for IIoT Using Ensemble Classifier

    V. Priya1, I. Sumaiya Thaseen1, Thippa Reddy Gadekallu1, Mohamed K. Aboudaif2,*, Emad Abouel Nasr3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2457-2470, 2021, DOI:10.32604/cmc.2021.013852

    Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition,… More >

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