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  • Open Access


    An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

    Ejaz Khan1, Muhammad Zia Ur Rehman2, Fawad Ahmed3, Suliman A. Alsuhibany4,*, Muhammad Zulfiqar Ali5, Jawad Ahmad6

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3799-3814, 2023, DOI:10.32604/csse.2023.037131

    Abstract In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To… More >

  • Open Access


    A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

    Mousa Alhajlah*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6161-6175, 2023, DOI:10.32604/cmc.2023.032505

    Abstract Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to More >

  • Open Access


    Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

    Ayman Altameem1, Sandeep Kumar2, Ramesh Chandra Poonia3, Abdul Khader Jilani Saudagar4,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4719-4736, 2022, DOI:10.32604/cmc.2022.022177

    Abstract Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes More >

  • Open Access


    Hybrid Active Contour Mammographic Mass Segmentation and Classification

    K. Yuvaraj*, U. S. Ragupathy

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 823-834, 2022, DOI:10.32604/csse.2022.018837

    Abstract This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the… More >

  • Open Access


    A New Hybrid Feature Selection Method Using T-test and Fitness Function

    Husam Ali Abdulmohsin1,*, Hala Bahjat Abdul Wahab2, Abdul Mohssen Jaber Abdul Hossen3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3997-4016, 2021, DOI:10.32604/cmc.2021.014840


    Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes

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