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Search Results (13)
  • Open Access


    Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

    Mohamed Abouhawwash1,2, S. Sridevi3, Suma Christal Mary Sundararajan4, Rohit Pachlor5, Faten Khalid Karim6, Doaa Sami Khafaga6,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812

    Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In… More >

  • Open Access


    Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

    B. Divya*, M. Santhi

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1881-1898, 2023, DOI:10.32604/iasc.2023.031573

    Abstract Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring and detection. The four-step image… More >

  • Open Access


    Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

    Muhammad Irfan1, Khalil Ullah2, Fazal Muhammad3,*, Salman Khan3, Faisal Althobiani4, Muhammad Usman5, Mohammed Alshareef4, Shadi Alghaffari4, Saifur Rahman1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337

    Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with… More >

  • Open Access


    Research on Known Vulnerability Detection Method Based on Firmware Analysis

    Wenjing Wang1, Tengteng Zhao1, Xiaolong Li1,*, Lei Huang1, Wei Zhang1, Hui Guo2

    Journal of Cyber Security, Vol.4, No.1, pp. 1-15, 2022, DOI:10.32604/jcs.2022.026816

    Abstract At present, the network security situation is becoming more and more serious. Malicious network attacks such as computer viruses, Trojans and hacker attacks are becoming more and more rampant. National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society. At the same time, with the rapid development of Internet of Things and the arrival of 5G era, IoT devices as an important part of industrial Internet system, have become an important target of infiltration attacks by hostile forces. This paper describes the challenges facing… More >

  • Open Access


    Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

    Erssa Arif1,*, Syed Khuram Shahzad2, Muhammad Waseem Iqbal3, Muhammad Arfan Jaffar4, Abdullah S. Alshahrani5, Ahmed Alghamdi6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571

    Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.… More >

  • Open Access


    Deep Learning-Based Automatic Detection and Evaluation on Concrete Surface Bugholes

    Fujia Wei1,2,*, Liyin Shen1, Yuanming Xiang2, Xingjie Zhang2, Yu Tang2, Qian Tan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 619-637, 2022, DOI:10.32604/cmes.2022.019082

    Abstract Concrete exterior quality is one of the important metrics in evaluating construction project quality. Among the defects affecting concrete exterior quality, bughole is one of the most common imperfections, thus detecting concrete bughole accurately is significant for improving concrete exterior quality and consequently the quality of the whole project. This paper presents a deep learning-based method for detecting concrete surface bugholes in a more objective and automatic way. The bugholes are identified in concrete surface images by Mask R-CNN. An evaluation metric is developed to indicate the scale of concrete bughole. The proposed approach can detect bugholes in an instance… More >

  • Open Access


    Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

    Atif Naseer1,*, Enrique Nava Baro1, Sultan Daud Khan2, Yolanda Vila3, Jennifer Doyle4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5321-5344, 2022, DOI:10.32604/cmc.2022.020886

    Abstract The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine… More >

  • Open Access


    Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks

    Mohamed Yacin Sikkandar1,*, S. Sabarunisha Begum2, Abdulaziz A. Alkathiry3, Mashhor Shlwan N. Alotaibi1, Md Dilsad Manzar4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4279-4291, 2022, DOI:10.32604/cmc.2022.020571

    Abstract Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification of KOA images based on… More >

  • Open Access


    Optimized Convolutional Neural Network for Automatic Detection of COVID-19

    K. Muthumayil1, M. Buvana2, K. R. Sekar3, Adnen El Amraoui4,*, Issam Nouaouri4, Romany F. Mansour5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.017178

    Abstract The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature… More >

  • Open Access


    Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

    Habib Dhahri1,2,*, 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

    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, which were fed to two… More >

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