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  • Open Access

    ARTICLE

    DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

    Sara Saber1, Uswah Khairuddin2,*, Rubiyah Yusof2, Ahmed Madani1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3563-3576, 2021, DOI:10.32604/cmc.2021.017769 - 06 May 2021

    Abstract The identification of DNA binding proteins (DNABPs) is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g., in the study of the biological, biophysical, and biochemical effects of antibiotics, drugs, and steroids on DNA. This paper presents an efficient approach for DNABPs identification based on deep transfer learning, named “DTLM-DBP.” Two transfer learning methods are used in the identification process. The first is based on the pre-trained deep learning model as a feature’s extractor and classifier. Two different pre-trained Convolutional Neural Networks… More >

  • Open Access

    ARTICLE

    Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

    Kathiravan Srinivasan1, Lalit Garg2,*, Debajit Datta3, Abdulellah A. Alaboudi4, N. Z. Jhanjhi5, Rishav Agarwal3, Anmol George Thomas1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736 - 06 May 2021

    Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an More >

  • Open Access

    ARTICLE

    Improved Model of Eye Disease Recognition Based on VGG Model

    Ye Mu1,2,3,4, Yuheng Sun1, Tianli Hu1,2,3,4, He Gong1,2,3,4, Shijun Li1,2,3,4,*, Thobela Louis Tyasi5

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 729-737, 2021, DOI:10.32604/iasc.2021.016569 - 20 April 2021

    Abstract The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early More >

  • Open Access

    ARTICLE

    VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

    Muhammed Binsawad1,*, Marwan Albahar2, Abdullah Bin Sawad1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2791-2806, 2021, DOI:10.32604/cmc.2021.016141 - 13 April 2021

    Abstract The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated… More >

  • Open Access

    ARTICLE

    Detecting Driver Distraction Using Deep-Learning Approach

    Khalid A. AlShalfan1, Mohammed Zakariah2,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 689-704, 2021, DOI:10.32604/cmc.2021.015989 - 22 March 2021

    Abstract Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that detect driver actions and help them drive safely has increased. In these studies, although some distinct data types, such as the physical conditions of the driver, audio and visual features, and vehicle information, are used, the primary data source is images of the driver that include the face, arms, and hands taken with a camera inside the car. In this study, an architecture based on a convolution neural More >

  • Open Access

    ARTICLE

    Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

    Shaheen Khatoon1,*, Md Maruf Hasan1, Amna Asif1, Majed Alshmari1, Yun-Kiam Yap2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580 - 12 January 2021

    Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI)… More >

  • Open Access

    ARTICLE

    A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

    V. Sudha1,*, T. R. Ganeshbabu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008 - 30 October 2020

    Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as… More >

  • Open Access

    ARTICLE

    A Lane Detection Method Based on Semantic Segmentation

    Ling Ding1, 2, Huyin Zhang1, *, Jinsheng Xiao3, *, Cheng Shu3, Shejie Lu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 1039-1053, 2020, DOI:10.32604/cmes.2020.08268 - 01 March 2020

    Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses… More >

  • Open Access

    ARTICLE

    Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection

    Princy Matlani1,*, Manish Shrivastava1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985

    Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow… More >

  • Open Access

    ARTICLE

    Improved VGG Model for Road Traffic Sign Recognition

    Shuren Zhou1,2,*, Wenlong Liang1,2, Junguo Li1,2, Jeong-Uk Kim3

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

    Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG More >

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