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  • Open Access

    ARTICLE

    Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

    Samina Amin1,*, M. Irfan Uddin1, M. Ali Zeb1, Ala Abdulsalam Alarood2, Marwan Mahmoud3, Monagi H. Alkinani4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733

    Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

  • Open Access

    ARTICLE

    Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases

    Nakhim Chea1, Yunyoung Nam2,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390

    Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual… More >

  • Open Access

    REVIEW

    A Survey on Machine Learning in Chemical Spectral Analysis

    Dongfang Yu, Jinwei Wang*

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 165-174, 2020, DOI:10.32604/jihpp.2020.010466

    Abstract Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple… More >

  • Open Access

    ARTICLE

    Image Denoising with GAN Based Model

    Peizhu Gong, Jin Liu*, Shiqi Lv

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 155-163, 2020, DOI:10.32604/jihpp.2020.010453

    Abstract Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not… More >

  • Open Access

    ARTICLE

    Tyre Inspection through Multi-State Convolutional Neural Networks

    C. Sivamani1, M. Rajeswari2, E. Golden Julie3, Y. Harold Robinson4, Vimal Shanmuganathan5, Seifedine Kadry6, Yunyoung Nam7,*

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.013705

    Abstract Road accident is a potential risk to the lives of both drivers and passers-by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while manufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre formation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to maintenance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of… More >

  • Open Access

    ARTICLE

    Identification of Thoracic Diseases by Exploiting Deep Neural Networks

    Saleh Albahli1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Md Tabrez Nafis4, Abdulelah Algosaibi5

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3139-3149, 2021, DOI:10.32604/cmc.2021.014134

    Abstract With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN)… More >

  • Open Access

    ARTICLE

    Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

    Nawaf N. Hamadneh1, Waqar A. Khan2, Waqar Ashraf3, Samer H. Atawneh4, Ilyas Khan5,*, Bandar N. Hamadneh6

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2787-2796, 2021, DOI:10.32604/cmc.2021.013228

    Abstract In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed… More >

  • Open Access

    ARTICLE

    A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

    Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

    Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

  • Open Access

    ARTICLE

    Automatic Channel Detection Using DNN on 2D Seismic Data

    Fahd A. Alhaidari1, Saleh A. Al-Dossary2, Ilyas A. Salih1,*, Abdlrhman M. Salem1, Ahmed S. Bokir1, Mahmoud O. Fares1, Mohammed I. Ahmed1, Mohammed S. Ahmed1

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 57-67, 2021, DOI:10.32604/csse.2021.013843

    Abstract Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image… More >

  • Open Access

    ARTICLE

    3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

    Khalil Khan1, Jehad Ali2, Kashif Ahmad3, Asma Gul4, Ghulam Sarwar5, Sahib Khan6, Qui Thanh Hoai Ta7, Tae-Sun Chung8, Muhammad Attique9,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1757-1770, 2021, DOI:10.32604/cmc.2020.013590

    Abstract Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images… More >

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