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  • Open Access


    A Hybrid Deep Learning Intrusion Detection Model for Fog Computing Environment

    K. Kalaivani*, M. Chinnadurai

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 1-15, 2021, DOI:10.32604/iasc.2021.017515

    Abstract Fog computing extends the concept of cloud computing by providing the services of computing, storage, and networking connectivity at the edge between data centers in cloud computing environments and end devices. Having the intelligence at the edge enables faster real-time decision-making and reduces the amount of data forwarded to the cloud. When enhanced by fog computing, the Internet of Things (IoT) brings low latency and improves real time and quality of service (QoS) in IoT applications of augmented reality, smart grids, smart vehicles, and healthcare. However, both cloud and fog computing environments are vulnerable to several kinds of attacks that… More >

  • Open Access


    Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

    Jinseok Kim1, Babar Shah2, Ki-Il Kim3,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042

    Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.… More >

  • Open Access


    Hybrid Deep Learning Modeling for Water Level Prediction in Yangtze River

    Zhaoqing Xie1,*, Qing Liu2, Yulian Cao3

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246

    Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. Six representative reaches in Yangtze… More >

  • Open Access


    Smart Object Detection and Home Appliances Control System in Smart Cities

    Sulaiman Khan1, Shah Nazir1, Habib Ullah Khan2,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 895-915, 2021, DOI:10.32604/cmc.2021.013878

    Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More >

  • Open Access


    A Hybrid Deep Learning Model for COVID-19 Prediction and Current Status of Clinical Trials Worldwide

    Shwet Ketu*, Pramod Kumar Mishra

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1896-1919, 2021, DOI:10.32604/cmc.2020.012423

    Abstract Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid… More >

  • Open Access


    A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products: An Application for Medical-Tech Classification

    Inzamam Mashood Nasir1, Muhammad Attique Khan1,*, Majed Alhaisoni2, Tanzila Saba3, Amjad Rehman3, Tassawar Iqbal4

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1017-1033, 2020, DOI:10.32604/cmes.2020.010943

    Abstract Comic character detection is becoming an exciting and growing research area in the domain of machine learning. In this regard, recently, many methods are proposed to provide adequate performance. However, most of these methods utilized the custom datasets, containing a few hundred images and fewer classes, to evaluate the performances of their models without comparing it, with some standard datasets. This article takes advantage of utilizing a standard publicly dataset taken from a competition, and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN. In addition, to classify the superheroes… More >

  • Open Access


    Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

    Kun Zhu1, Nana Zhang1, Qing Zhang2, Shi Ying1, *, Xu Wang3

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1467-1486, 2020, DOI:10.32604/cmc.2020.011415

    Abstract Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the… More >

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