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  • Open Access

    ARTICLE

    Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

    Jahangir Jabbar1, Muzammil Hussain2, Hassaan Malik2,*, Abdullah Gani3, Ali Haider Khan2, Muhammad Shiraz4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1827-1844, 2022, DOI:10.32604/cmc.2022.024965

    Abstract Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection of the wrist. For the… More >

  • Open Access

    ARTICLE

    Analyzing and Enabling the Harmonious Coexistence of Heterogeneous Industrial Wireless Networks

    Bilal Khan1, Danish Shehzad1, Numan Shafi1, Ga-Young Kim2,*, Muhammad Umar Aftab1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1671-1690, 2022, DOI:10.32604/cmc.2022.024918

    Abstract Nowadays multiple wireless communication systems operate in industrial environments side by side. In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold. Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems (iWCS) this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN (general-purpose WLAN) used for non-real time communication. In this paper, we present a Markov chain-based performance model that described the transmission failure of iWCS due to… More >

  • Open Access

    ARTICLE

    New Representative Collective Signatures Based on the Discrete Logarithm Problem

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 783-799, 2022, DOI:10.32604/cmc.2022.024677

    Abstract The representative collective digital signature scheme allows the creation of a unique collective signature on document M that represents an entire signing community consisting of many individual signers and many different signing groups, each signing group is represented by a group leader. On document M, a collective signature can be created using the representative digital signature scheme that represents an entire community consisting of individual signers and signing groups, each of which is represented by a group leader. The characteristic of this type of letter is that it consists of three elements (U, E, S), one of which (U) is… More >

  • Open Access

    ARTICLE

    Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

    Fauzi A. Bala1,2,*, Osman N. Ucan1, Oguz Bayat1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2187-2204, 2022, DOI:10.32604/cmc.2022.024205

    Abstract Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning (DL) due to its ability… More >

  • Open Access

    ARTICLE

    Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

    M. M. Lotfy1, Hazem M. El-Bakry2, M. M. Elgayar3, Shaker El-Sappagh4,5, G. Abdallah M. I1, A. A. Soliman1, Kyung Sup Kwak6,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193

    Abstract Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly… More >

  • Open Access

    ARTICLE

    Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

    E. A. Alabdulkreem1, Ahmed Sedik2, Abeer D. Algarni3,*, Ghada M. El Banby4, Fathi E. Abd El-Samie3,5, Naglaa F. Soliman3,6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1845-1861, 2022, DOI:10.32604/cmc.2022.023905

    Abstract Image fusion has become one of the interesting fields that attract researchers to integrate information from different image sources. It is involved in several applications. One of the recent applications is the robotic vision. This application necessitates image enhancement of both infrared (IR) and visible images. This paper presents a Robot Human Interaction System (RHIS) based on image fusion and deep learning. The basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured images. Then, an enhancement model is carried out on the fused image to increase its quality. Several image… More >

  • Open Access

    ARTICLE

    Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

    Jaeyong Kang1, Chul-Su Kim2, Jeong Won Kang3, Jeonghwan Gwak1,4,5,6,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1-14, 2022, DOI:10.32604/cmc.2022.023641

    Abstract The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. On the other hand, current periodic inspection and maintenance are unable to detect anomalies in an early stage. Also, building an accurate and stable system for detecting anomalies is extremely difficult. Therefore, we present an efficient model that use an ensemble of recurrent autoencoders to accurately detect the BOU abnormalities of metro trains. This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities in metro train braking systems.… More >

  • Open Access

    ARTICLE

    Multiple Forgery Detection in Video Using Convolution Neural Network

    Vinay Kumar1,*, Vineet Kansal2, Manish Gaur2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1347-1364, 2022, DOI:10.32604/cmc.2022.023545

    Abstract With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the… More >

  • Open Access

    ARTICLE

    New Collective Signatures Based on the Elliptic Curve Discrete Logarithm Problem

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 595-610, 2022, DOI:10.32604/cmc.2022.023168

    Abstract There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field. In this study, we use the elliptic curve discrete logarithm problem to build new collective signature schemes. The cryptosystem on elliptic curve allows to generate digital signatures with the same level of security as other cryptosystems but with smaller keys. To extend practical applicability and enhance the security level of the group signature protocols, we propose two new types of collective digital signature schemes based on the discrete logarithm problem on the elliptic curve: i) the collective digital signature scheme shared… More >

  • Open Access

    ARTICLE

    Early-Stage Segmentation and Characterization of Brain Tumor

    Syed Nauyan Rashid1, Muhammad Hanif2,*, Usman Habib2, Akhtar Khalil3, Omair Inam4, Hafeez Ur Rehman1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1001-1017, 2022, DOI:10.32604/cmc.2022.023135

    Abstract Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues. The life expectancy of patients diagnosed with gliomas decreases exponentially. Most gliomas are diagnosed in later stages, resulting in imminent death. On average, patients do not survive 14 months after diagnosis. The only way to minimize the impact of this inevitable disease is through early diagnosis. The Magnetic Resonance Imaging (MRI) scans, because of their better tissue contrast, are most frequently used to assess the brain tissues. The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors. Besides this,… More >

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