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  • Open Access

    EDITORIAL

    Deep Learning for COVID-19 Diagnosis via Chest Images

    Shuihua Wang1,2, Yudong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 129-132, 2023, DOI:10.32604/cmc.2023.040560

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

    Jahanzaib Latif1, Shanshan Tu1,*, Chuangbai Xiao1, Anas Bilal2, Sadaqat Ur Rehman3, Zohaib Ahmad4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

    Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

  • Open Access

    ARTICLE

    Ship Detection and Recognition Based on Improved YOLOv7

    Wei Wu1, Xiulai Li2, Zhuhua Hu1, Xiaozhang Liu3,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 489-498, 2023, DOI:10.32604/cmc.2023.039929

    Abstract In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data… More >

  • Open Access

    ARTICLE

    A Flexible Architecture for Cryptographic Applications: ECC and PRESENT

    Muhammad Rashid1,*, Omar S. Sonbul1, Muhammad Arif2, Furqan Aziz Qureshi3, Saud. S. Alotaibi4, Mohammad H. Sinky1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1009-1025, 2023, DOI:10.32604/cmc.2023.039901

    Abstract This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography (ECC) and PRESENT for cryptographic applications. The features of the proposed work are (i) computation of only the point multiplication operation of ECC over for a 163-bit key generation, (ii) execution of only the variant of an 80-bit PRESENT block cipher for data encryption & decryption and (iii) execution of point multiplication operation (ECC algorithm) along with the data encryption and decryption (PRESENT algorithm). To establish an area overhead for the flexible design, dedicated hardware architectures of ECC and PRESENT are implemented in the first step, and a sum of… More >

  • Open Access

    ARTICLE

    Novel Framework for Generating Criminals Images Based on Textual Data Using Identity GANs

    Mohamed Fathallah1,*, Mohamed Sakr2, Sherif Eletriby2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 383-396, 2023, DOI:10.32604/cmc.2023.039824

    Abstract Text-to-image generation is a vital task in different fields, such as combating crime and terrorism and quickly arresting lawbreakers. For several years, due to a lack of deep learning and machine learning resources, police officials required artists to draw the face of a criminal. Traditional methods of identifying criminals are inefficient and time-consuming. This paper presented a new proposed hybrid model for converting the text into the nearest images, then ranking the produced images according to the available data. The framework contains two main steps: generation of the image using an Identity Generative Adversarial Network (IGAN) and ranking of the… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

    Shah Faisal1, Kashif Javed1, Sara Ali1, Areej Alasiry2, Mehrez Marzougui2, Muhammad Attique Khan3,*, Jae-Hyuk Cha4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

    Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

  • Open Access

    ARTICLE

    Anomalous Situations Recognition in Surveillance Images Using Deep Learning

    Qurat-ul-Ain Arshad1, Mudassar Raza1, Wazir Zada Khan2, Ayesha Siddiqa2, Abdul Muiz2, Muhammad Attique Khan3,*, Usman Tariq4, Taerang Kim5, Jae-Hyuk Cha5,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752

    Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

  • Open Access

    ARTICLE

    Deletion and Recovery Scheme of Electronic Health Records Based on Medical Certificate Blockchain

    Baowei Wang1,2,*, Neng Wang1, Yuxiao Zhang1, Zenghui Xu1, Junhao Zhang1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 849-859, 2023, DOI:10.32604/cmc.2023.039749

    Abstract The trusted sharing of Electronic Health Records (EHRs) can realize the efficient use of medical data resources. Generally speaking, EHRs are widely used in blockchain-based medical data platforms. EHRs are valuable private assets of patients, and the ownership belongs to patients. While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals, it does not address the challenge of record sharing when patients revisit doctors. In order to solve this problem, this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain. This paper uses cross-chain technology to connect the… More >

  • Open Access

    ARTICLE

    MEM-TET: Improved Triplet Network for Intrusion Detection System

    Weifei Wang1, Jinguo Li1,*, Na Zhao2, Min Liu1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 471-487, 2023, DOI:10.32604/cmc.2023.039733

    Abstract With the advancement of network communication technology, network traffic shows explosive growth. Consequently, network attacks occur frequently. Network intrusion detection systems are still the primary means of detecting attacks. However, two challenges continue to stymie the development of a viable network intrusion detection system: imbalanced training data and new undiscovered attacks. Therefore, this study proposes a unique deep learning-based intrusion detection method. We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data. Then the original data is fed into the triplet network… More >

  • Open Access

    ARTICLE

    IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

    Sharad Saxena1, Shailendra Mishra2,*, Mohammed Baljon2,*, Shamiksha Mishra3, Sunil Kumar Sharma2, Prakhar Goel1, Shubham Gupta1, Vinay Kishore1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1027-1045, 2023, DOI:10.32604/cmc.2023.039677

    Abstract In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button.… More >

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