Home / Journals / CMC / Vol.77, No.2, 2023
Special lssues
Table of Content
  • Open AccessOpen Access

    ARTICLE

    DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

    Xiaoqi Xin*, Dezhi Han, Mingming Cui
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2407-2425, 2023, DOI:10.32604/cmc.2023.042308
    Abstract Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect the performance of models. To… More >

  • Open AccessOpen Access

    ARTICLE

    Programmable Logic Controller Block Monitoring System for Memory Attack Defense in Industrial Control Systems

    Mingyu Lee1, Jiho Shin2, Jung Taek Seo3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2427-2442, 2023, DOI:10.32604/cmc.2023.041774
    (This article belongs to this Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
    Abstract Cyberattacks targeting industrial control systems (ICS) are becoming more sophisticated and advanced than in the past. A programmable logic controller (PLC), a core component of ICS, controls and monitors sensors and actuators in the field. However, PLC has memory attack threats such as program injection and manipulation, which has long been a major target for attackers, and it is important to detect these attacks for ICS security. To detect PLC memory attacks, a security system is required to acquire and monitor PLC memory directly. In addition, the performance impact of the security system on the PLC makes it difficult to… More >

  • Open AccessOpen Access

    ARTICLE

    Mobile-Deep Based PCB Image Segmentation Algorithm Research

    Lisang Liu1, Chengyang Ke1,*, He Lin2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2443-2461, 2023, DOI:10.32604/cmc.2023.042582
    Abstract Aiming at the problems of inaccurate edge segmentation, the hole phenomenon of segmenting large-scale targets, and the slow segmentation speed of printed circuit boards (PCB) in the image segmentation process, a PCB image segmentation model Mobile-Deep based on DeepLabv3+ semantic segmentation framework is proposed. Firstly, the DeepLabv3+ feature extraction network is replaced by the lightweight model MobileNetv2, which effectively reduces the number of model parameters; secondly, for the problem of positive and negative sample imbalance, a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability; in… More >

  • Open AccessOpen Access

    ARTICLE

    Approach to Simplify the Development of IoT Systems that Interconnect Embedded Devices Using a Single Program

    Enol Matilla Blanco1, Jordán Pascual Espada1, Rubén Gonzalez Crespo2,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2463-2480, 2023, DOI:10.32604/cmc.2023.042793
    (This article belongs to this Special Issue: Trends in Machine Learning and Internet of Things for Industrial Applications)
    Abstract Many Internet of Things (IoT) systems are based on the intercommunication among different devices and centralized systems. Nowadays, there are several commercial and research platforms available to simplify the creation of such IoT systems. However, developing these systems can often be a tedious task. To address this challenge, a proposed solution involves the implementation of a unified program or script that encompasses the entire system, including IoT devices functionality. This approach is based on an abstraction, integrating the control of the devices in a single program through a programmable object. Subsequently, the proposal processes the unified script to generate the… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

    Abdul Qadir Khan1, Guangmin Sun1,*, Yu Li1, Anas Bilal2, Malik Abdul Manan1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2481-2504, 2023, DOI:10.32604/cmc.2023.043239
    (This article belongs to this Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
    Abstract In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to generate a diverse set of… More >

  • Open AccessOpen Access

    ARTICLE

    Consortium Chain Consensus Vulnerability and Chain Generation Mechanism

    Rui Qiao, Shi Dong*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2505-2527, 2023, DOI:10.32604/cmc.2023.043476
    (This article belongs to this Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
    Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived. When either of the… More >

  • Open AccessOpen Access

    ARTICLE

    Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

    Muhammad Tahir1,2,*, Mingchu Li1,2, Irfan Khan1,2, Salman A. Al Qahtani3, Rubia Fatima4, Javed Ali Khan5, Muhammad Shahid Anwar6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2529-2544, 2023, DOI:10.32604/cmc.2023.042403
    (This article belongs to this Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics.… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

    Medhat A. Tawfeek1,2, Nacim Yanes3,4, Leila Jamel5,*, Ghadah Aldehim5, Mahmood A. Mahmood1,6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179
    Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

  • Open AccessOpen Access

    ARTICLE

    A Mathematical Approach for Generating a Highly Non-Linear Substitution Box Using Quadratic Fractional Transformation

    Abid Mahboob1, Muhammad Asif2, Rana Muhammad Zulqarnain3,*, Imran Saddique4, Hijaz Ahmad5, Sameh Askar6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2565-2578, 2023, DOI:10.32604/cmc.2023.040371
    (This article belongs to this Special Issue: Multimedia Encryption and Information Security)
    Abstract Nowadays, one of the most important difficulties is the protection and privacy of confidential data. To address these problems, numerous organizations rely on the use of cryptographic techniques to secure data from illegal activities and assaults. Modern cryptographic ciphers use the non-linear component of block cipher to ensure the robust encryption process and lawful decoding of plain data during the decryption phase. For the designing of a secure substitution box (S-box), non-linearity (NL) which is an algebraic property of the S-box has great importance. Consequently, the main focus of cryptographers is to achieve the S-box with a high value of… More >

  • Open AccessOpen Access

    ARTICLE

    Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

    Abdennasser Dahmani1, Yamina Ammi2, Nadjem Bailek3,4,*, Alban Kuriqi5,6, Nadhir Al-Ansari7,*, Salah Hanini2, Ilhami Colak8, Laith Abualigah9,10,11,12,13,14, El-Sayed M. El-kenawy15
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625
    (This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic… More >

  • Open AccessOpen Access

    ARTICLE

    MF2-DMTD: A Formalism and Game-Based Reasoning Framework for Optimized Drone-Type Moving Target Defense

    Sang Seo1, Jaeyeon Lee2, Byeongjin Kim2, Woojin Lee2, Dohoon Kim3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2595-2628, 2023, DOI:10.32604/cmc.2023.042668
    Abstract Moving-target-defense (MTD) fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations. However, the existing naive MTD studies were conducted focusing only on wired network mutations. And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations, such as hostility, mobility, and dependency. Therefore, to solve these conceptual limitations, this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle… More >

  • Open AccessOpen Access

    ARTICLE

    A Trusted Edge Resource Allocation Framework for Internet of Vehicles

    Yuxuan Zhong1, Siya Xu1, Boxian Liao1, Jizhao Lu2, Huiping Meng2, Zhili Wang1, Xingyu Chen1,*, Qinghan Li3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2629-2644, 2023, DOI:10.32604/cmc.2023.035526
    Abstract With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving… More >

  • Open AccessOpen Access

    REVIEW

    Ensuring User Privacy and Model Security via Machine Unlearning: A Review

    Yonghao Tang1, Zhiping Cai1,*, Qiang Liu1, Tongqing Zhou1, Qiang Ni2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2645-2656, 2023, DOI:10.32604/cmc.2023.032307
    Abstract As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy. Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The data provider hopes that the model trainer can prove to them the confidentiality of the model. Trainer will be required to withdraw data when the trust collapses. In the meantime, trainers hope to forget the injected data to regain security when finding… More >

  • Open AccessOpen Access

    ARTICLE

    Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

    Yujun Zhang*, Dezhi Han, Peng Chen
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311
    Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >

Per Page:

Share Link