Habib Dhahri^1,2,*, Besma Rabhi³, Slaheddine Chelbi⁴, Omar Almutiry¹, Awais Mahmood¹, Adel M. Alimi³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3259-3274, 2021, DOI:10.32604/cmc.2021.018449 - 24 August 2021

Abstract The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, More >

Khalid Mahmood Aamir¹, Muhammad Bilal², Muhammad Ramzan^1,3, Muhammad Attique Khan⁴, Yunyoung Nam^5,*, Seifedine Kadry⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3829-3844, 2021, DOI:10.32604/cmc.2021.017835 - 24 August 2021

Abstract Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnology Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at… More >

Sadia Kiran¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Yunyoung Nam^4,*, Robertas Damaševičius⁵, Muhammad Sharif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800 - 24 August 2021

Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

Jiyoung Yun, Mainak Basak, Myung-Mook Han^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266 - 24 August 2021

Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients… More >

Muhammad Ramzan¹, Mudassar Raza¹, Muhammad Sharif¹, Muhammad Attique Khan², Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920 - 24 August 2021

Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the… More >

Shabana Habib¹, Noreen Fayyaz Khan^2,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504 - 24 August 2021

Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used… More >

Muhammad Azam¹, Tanvir Ahmed¹, Rehan Ahmad², Ateeq Ur Rehman³, Fahad Sabah¹, Rao Muhammad Asif^4,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 853-867, 2021, DOI:10.32604/iasc.2021.019101 - 20 August 2021

Abstract Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing… More >

I.-Hung Wang¹, Mahardi², Kuang-Chyi Lee^2,*, Shinn-Liang Chang¹

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 995-1005, 2021, DOI:10.32604/iasc.2021.019020 - 20 August 2021

Abstract The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general More >

Muhammad Alyas Khan¹, Mushtaq Ali¹, Mohsin Shah², Toqeer Mahmood³, Muneer Ahmad⁴, NZ Jhanjhi⁵, Mohammad Arif Sobhan Bhuiyan^6,*, Emad Sami Jaha⁷

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 771-785, 2021, DOI:10.32604/iasc.2021.018039 - 20 August 2021

Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using… More >