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  • Open Access

    ARTICLE

    Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

    Anwer Mustafa Hilal1, Badria Sulaiman Alfurhood2, Fahd N. Al-Wesabi3,4, Manar Ahmed Hamza1,*, Mesfer Al Duhayyim5, Huda G. Iskandar4,6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 143-157, 2022, DOI:10.32604/cmc.2022.021502 - 03 November 2021

    Abstract Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment… More >

  • Open Access

    ARTICLE

    Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

    Abdulsamad A. AL-Marghilani*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 809-824, 2022, DOI:10.32604/cmc.2022.021185 - 03 November 2021

    Abstract Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness… More >

  • Open Access

    ARTICLE

    An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

    T. Gopalakrishnan1, Periakaruppan Sudhakaran2, K. C. Ramya3, K. Sathesh Kumar4, Fahd N. Al-Wesabi5,6,*, Manal Abdullah Alohali7, Anwer Mustafa Hilal8

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 305-320, 2022, DOI:10.32604/cmc.2022.020914 - 03 November 2021

    Abstract Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for… More >

  • Open Access

    ARTICLE

    Deep Learning Based Audio Assistive System for Visually Impaired People

    S. Kiruthika Devi*, C. N. Subalalitha

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1205-1219, 2022, DOI:10.32604/cmc.2022.020827 - 03 November 2021

    Abstract Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the… More >

  • Open Access

    ARTICLE

    An Efficient Internet Traffic Classification System Using Deep Learning for IoT

    Muhammad Basit Umair1, Zeshan Iqbal1, Muhammad Bilal2, Jamel Nebhen4, Tarik Adnan Almohamad3, Raja Majid Mehmood5,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 407-422, 2022, DOI:10.32604/cmc.2022.020727 - 03 November 2021

    Abstract Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify… More >

  • Open Access

    ARTICLE

    IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

    Anurag Jain1, Kusum Yadav2, Hadeel Fahad Alharbi2, Shamik Tiwari1,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 423-438, 2022, DOI:10.32604/cmc.2022.020238 - 03 November 2021

    Abstract Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Approach for Arabic Visual Speech Recognition

    Nadia H. Alsulami1,*, Amani T. Jamal1, Lamiaa A. Elrefaei2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 85-108, 2022, DOI:10.32604/cmc.2022.019450 - 03 November 2021

    Abstract Lip-reading technologies are rapidly progressing following the breakthrough of deep learning. It plays a vital role in its many applications, such as: human-machine communication practices or security applications. In this paper, we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning algorithms. The Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native speakers. The primary purpose is to provide a high-performance model in terms of enhancing the preprocessing phase. Firstly, we extract keyframes from… More >

  • Open Access

    ARTICLE

    An Integrated Deep Learning Framework for Fruits Diseases Classification

    Abdul Majid1, Muhammad Attique Khan1, Majed Alhaisoni2, Muhammad Asfand E. yar3, Usman Tariq4, Nazar Hussain1, Yunyoung Nam5,*, Seifedine Kadry6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1387-1402, 2022, DOI:10.32604/cmc.2022.017701 - 03 November 2021

    Abstract Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus,… More >

  • Open Access

    ARTICLE

    Automatic License Plate Recognition System for Vehicles Using a CNN

    Parneet Kaur1, Yogesh Kumar1, Shakeel Ahmed2,*, Abdulaziz Alhumam2, Ruchi Singla3, Muhammad Fazal Ijaz4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 35-50, 2022, DOI:10.32604/cmc.2022.017681 - 03 November 2021

    Abstract Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with More >

  • Open Access

    ARTICLE

    Target Classification of Marine Debris Using Deep Learning

    Anum Aleem1, Samabia Tehsin1,*, Sumaira Kausar1, Amina Jameel2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 73-85, 2022, DOI:10.32604/iasc.2022.021583 - 26 October 2021

    Abstract Marine Debris is human-created waste dumped into the sea or ocean. It pollutes the aquatic environment and hence very dangerous for ocean species. Removal of marine debris from ocean is necessary to eliminate pollution and to secure aquatic life. A robust and automatic system is essential that detects unnecessary litter of plastic and other garbage at real-time. In this study, we have proposed deep learning based architecture for the detection and classification of marine debris. Histogram Equalization technique combined with Median Filter is used to enhance the contrast of images and to remove noise. Experiments… More >

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