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  • Open Access

    ARTICLE

    3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

    Jing Zhang1,2, Keping Yu3,*, Zheng Wen4, Xin Qi3, Anup Kumar Paul5

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2087-2104, 2021, DOI:10.32604/cmc.2020.014220

    Abstract The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter… More >

  • Open Access

    ARTICLE

    Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

    G. Arun Sampaul Thomas1, Y. Harold Robinson2, E. Golden Julie3, Vimal Shanmuganathan4, Seungmin Rho5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1613-1629, 2021, DOI:10.32604/cmc.2020.013443

    Abstract Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have retinopathy and those which do… More >

  • Open Access

    ARTICLE

    An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

    K. Shankar1,*, Eswaran Perumal1, Mohamed Elhoseny2, Phong Thanh Nguyen3

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1665-1680, 2021, DOI:10.32604/cmc.2020.013251

    Abstract Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The… More >

  • Open Access

    ARTICLE

    Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

    Kashif Iqbal1,2, Sagheer Abbas1, Muhammad Adnan Khan3,*, Atifa Athar4, Muhammad Saleem Khan1, Areej Fatima3, Gulzar Ahmad1

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1595-1612, 2021, DOI:10.32604/cmc.2020.013231

    Abstract The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Fruit Diseases: An Application for Precision Agriculture

    Inzamam Mashood Nasir1, Asima Bibi2, Jamal Hussain Shah2, Muhammad Attique Khan1, Muhammad Sharif2, Khalid Iqbal3, Yunyoung Nam4, Seifedine Kadry5,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945

    Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

  • Open Access

    ARTICLE

    A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network

    Kyungsuk Jang1, Gun Jin Yun2,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

    Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression of the genetic programming. Stress… More >

  • Open Access

    ARTICLE

    Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

    Shahan Yamin Siddiqui1,2, Sagheer Abbas1, Muhammad Adnan Khan3,*, Iftikhar Naseer4, Tehreem Masood4, Khalid Masood Khan3, Mohammed A. Al Ghamdi5, Sultan H. Almotiri5

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1719-1732, 2021, DOI:10.32604/cmc.2020.012585

    Abstract The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

    K. V. Praveen1, P. M. Joe Prathap2, S. Dhanasekaran3, I. S. Hephzi Punithavathi4, P. Duraipandy5, Irina V. Pustokhina6, Denis A. Pustokhin7,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1987-2003, 2021, DOI:10.32604/cmc.2020.012398

    Abstract Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents… More >

  • Open Access

    ARTICLE

    Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

    Nazeer Muhammad1, Rubab2, Nargis Bibi3, Oh-Young Song4, Muhammad Attique Khan5,*, Sajid Ali Khan6

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257

    Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully… More >

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