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  • Open Access

    ARTICLE

    Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

    Mohammad Khalid Imam Rahmani1, Fahmina Taranum2, Reshma Nikhat3, Md. Rashid Farooqi3, Mohammed Arshad Khan4,*

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1181-1198, 2022, DOI:10.32604/csse.2022.022014

    Abstract The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify… More >

  • Open Access

    ARTICLE

    Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

    S. Karthiga*, A. M. Abirami

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 851-866, 2022, DOI:10.32604/csse.2022.021935

    Abstract Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed.… More >

  • Open Access

    ARTICLE

    Improving Date Fruit Classification Using CycleGAN-Generated Dataset

    Dina M. Ibrahim1,2,*, Nada M. Elshennawy2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419

    Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN)… More >

  • Open Access

    ARTICLE

    A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

    Hunmin Lee1, Inseop Na2, Kamoliddin Bultakov3, Youngchul Kim3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5413-5425, 2022, DOI:10.32604/cmc.2022.023172

    Abstract In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as… More >

  • Open Access

    ARTICLE

    An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

    Muhammad Asif Saleem1, Muhammad Aamir1,2, * ,*, Rosziati Ibrahim1, Norhalina Senan1, Tahir Alyas3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6053-6067, 2022, DOI:10.32604/cmc.2022.022215

    Abstract Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing More >

  • Open Access

    ARTICLE

    Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

    G. Sriram1, T. R. Ganesh Babu2, R. Praveena2,*, J. V. Anand3

    Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 29-40, 2022, DOI:10.32604/mcb.2022.016966

    Abstract Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an… More >

  • Open Access

    ARTICLE

    Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

    Fayadh Alenezi*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339

    Abstract Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified… More >

  • Open Access

    ARTICLE

    Deep Neural Networks for Gun Detection in Public Surveillance

    Erssa Arif1,*, Syed Khuram Shahzad2, Rehman Mustafa1, Muhammad Arfan Jaffar3, Muhammad Waseem Iqbal4

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061

    Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature… More >

  • Open Access

    ARTICLE

    A Study on Classification and Detection of Small Moths Using CNN Model

    Sang-Hyun Lee*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1987-1998, 2022, DOI:10.32604/cmc.2022.022554

    Abstract Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism

    K. Prabhu1,*, S. SathishKumar2, M. Sivachitra3, S. Dineshkumar2, P. Sathiyabama4

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 415-426, 2022, DOI:10.32604/csse.2022.019749

    Abstract Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, emotions and behaviors can be analyzed in FER. Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces. Recently, Deep Learning Techniques (DLT) have gained popularity in applications of real-world problems including recognition of human emotions. The human face reflects emotional states and human intentions. An expression is the most natural and powerful way of communicating non-verbally. Systems which form communications between the two are More >

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