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  • Open Access


    Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

    Zeyad Ghaleb Al-Mekhlafi1, Ebrahim Mohammed Senan2, Taha H. Rassem3, Badiea Abdulkarem Mohammed4,5,*, Nasrin M. Makbol5, Adwan Alownie Alanazi1, Tariq S. Almurayziq1, Fuad A. Ghaleb6

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492

    Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to… More >

  • Open Access


    Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

    D. Jerusha*, T. Jaya

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1121-1132, 2022, DOI:10.32604/csse.2022.022997

    Abstract Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a… More >

  • Open Access


    Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

    Hung-Hsiang Wang1, Chih-Ping Chen2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 237-254, 2022, DOI:10.32604/iasc.2022.020665

    Abstract Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the… More >

  • Open Access


    Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

    Shinjin Kang1, Soo Kyun Kim2,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3725-3736, 2021, DOI:10.32604/cmc.2021.016828

    Abstract In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular… More >

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