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  • Open Access

    ARTICLE

    Deep Learning Based Intelligent Industrial Fault Diagnosis Model

    R. Surendran1,*, Osamah Ibrahim Khalaf2, Carlos Andres Tavera Romero3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6323-6338, 2022, DOI:10.32604/cmc.2022.021716

    Abstract In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on… More >

  • Open Access

    ARTICLE

    Real Time Feature Extraction Deep-CNN for Mask Detection

    Hanan A. Hosni Mahmoud, Norah S. Alghamdi, Amal H. Alharbi*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1423-1434, 2022, DOI:10.32604/iasc.2022.020586

    Abstract COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique… More >

  • Open Access

    ARTICLE

    Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

    Raniyah Wazirali1, Rami Ahmed2,*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 305-320, 2022, DOI:10.32604/csse.2022.020504

    Abstract The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required… More >

  • Open Access

    ARTICLE

    Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms

    K. K. Thyagharajan, G. Nirmala*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 357-371, 2022, DOI:10.32604/csse.2022.020258

    Abstract In this paper, copy-move forgery in image is detected for single image with multiple manipulations such as blurring, noise addition, gray scale conversion, brightness modifications, rotation, Hu adjustment, color adjustment, contrast changes and JPEG Compression. However, traditional algorithms detect only copy-move attacks in image and never for different manipulation in single image. The proposed LLP (Laterally linked pixel) algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detection of copied region and kernel tricks is applied for detection of multiple manipulations in single forged image. LLP algorithm consists of two channels such… More >

  • Open Access

    ARTICLE

    Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

    Shibli Nisar1, Muhammad Asghar Khan2,*, Fahad Algarni3, Abdul Wakeel1, M. Irfan Uddin4, Insaf Ullah2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2991-3004, 2022, DOI:10.32604/cmc.2022.020376

    Abstract One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for… More >

  • Open Access

    ARTICLE

    Unified Detection of Obfuscated and Native Android Malware

    Pagnchakneat C. Ouk1, Wooguil Pak2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3099-3116, 2022, DOI:10.32604/cmc.2022.020202

    Abstract The Android operating system has become a leading smartphone platform for mobile and other smart devices, which in turn has led to a diversity of malware applications. The amount of research on Android malware detection has increased significantly in recent years and many detection systems have been proposed. Despite these efforts, however, most systems can be thwarted by sophisticated Android malware adopting obfuscation or native code to avoid discovery by anti-virus tools. In this paper, we propose a new static analysis technique to address the problems of obfuscating and native malware applications. The proposed system provides a unified technique for… More >

  • Open Access

    ARTICLE

    Hybrid Active Contour Mammographic Mass Segmentation and Classification

    K. Yuvaraj*, U. S. Ragupathy

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 823-834, 2022, DOI:10.32604/csse.2022.018837

    Abstract This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the segmentation techniques are compared with… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Rice Plant Disease Detection Model

    R. P. Narmadha1,*, N. Sengottaiyan2, R. J. Kavitha3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679

    Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant… More >

  • Open Access

    ARTICLE

    Detecting Lung Cancer Using Machine Learning Techniques

    Ashit Kumar Dutta*

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1007-1023, 2022, DOI:10.32604/iasc.2022.019778

    Abstract In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports the process of detecting cancers with Computer Tomography (CT) images. Lung cancer is one of the perilous diseases that increases the mortality rate exponentially. IoT based image classifiers have the ability to detect cancer at an early stage and increases the life span of a patient. It supports oncologist to monitor and evaluate the health condition of a patient. Also, it can decipher cancer risk marker and act upon them. The process of feature extraction and selection from… More >

  • Open Access

    ARTICLE

    Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

    Hanan A. Hosni Mahmoud*, Doaa S. Khafga, Amal H. Alharbi

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 805-821, 2022, DOI:10.32604/csse.2022.019633

    Abstract Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure… More >

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