Home / Journals / CMC / Vol.76, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

    Mohammed Salahat1, Liaqat Ali1, Taher M. Ghazal2,3,*, Haitham M. Alzoubi4
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.036019
    Abstract Knowing each other is obligatory in a multi-agent collaborative environment. Collaborators may develop the desired know-how of each other in various aspects such as habits, job roles, status, and behaviors. Among different distinguishing characteristics related to a person, personality traits are an effective predictive tool for an individual’s behavioral pattern. It has been observed that when people are asked to share their details through questionnaires, they intentionally or unintentionally become biased. They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves. Such writings can effectively assess an individual’s personality traits… More >

  • Open AccessOpen Access

    ARTICLE

    IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

    Muath Jarrah1, Hussam Al Hamadi4,*, Ahmed Abu-Khadrah2, Taher M. Ghazal1,3
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 19-33, 2023, DOI:10.32604/cmc.2023.032775
    Abstract The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly. The IoMT is a novel reality transforming our daily lives. It can renovate modern healthcare by delivering a more personalized, protective, and collaborative approach to care. However, More >

  • Open AccessOpen Access

    ARTICLE

    Application of Blockchain Sharding Technology in Chinese Medicine Traceability System

    Fuan Xiao1, Tong Lai1, Yutong Guan1, Jiaming Hong1, Honglai Zhang1, Guoyu Yang2, Zhengfei Wang1,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 35-48, 2023, DOI:10.32604/cmc.2023.038937
    (This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
    Abstract Traditional Chinese Medicine (TCM) is one of the most promising programs for disease prevention and treatment. Meanwhile, the quality of TCM has garnered much attention. To ensure the quality of TCM, many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin. Although these schemes can ensure the integrity, sharability, credibility, and immutability of TCM more effectively, many problems are exposed with the rapid growth of TCM data in blockchains, such as expensive overhead, performance bottlenecks, and the traditional blockchain architecture is unsuitable for TCM data with… More >

  • Open AccessOpen Access

    ARTICLE

    Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning

    Changsheng Zhu1,2,*, Borui Zhao1, Naranjo Villota Jose Luis1, Zihao Gao1, Li Feng3
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 49-64, 2023, DOI:10.32604/cmc.2023.036729
    Abstract In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by… More >

  • Open AccessOpen Access

    ARTICLE

    MVCE-Net: Multi-View Region Feature and Caption Enhancement Co-Attention Network for Visual Question Answering

    Feng Yan1, Wushouer Silamu2, Yanbing Li1,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 65-80, 2023, DOI:10.32604/cmc.2023.038177
    Abstract Visual question answering (VQA) requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately. However, existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images, which limits the understanding depth of the image content. The images contain more than just visual objects, some images contain textual information about the scene, and slightly more complex images contain relationships between individual visual objects. Firstly, this paper proposes a model using image description for feature enhancement. This model encodes… More >

  • Open AccessOpen Access

    ARTICLE

    Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

    Pingping Li*, Jiuxin Cao
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 81-105, 2023, DOI:10.32604/cmc.2023.039076
    Abstract Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

    Qiu-yu Zhang*, Yu-gui Jia, Fang-Peng Li, Le-Tian Fan
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 107-128, 2023, DOI:10.32604/cmc.2023.038911
    (This article belongs to the Special Issue: Recent Advances on Security and Privacy of Multimedia Big Data in the Social Internet of Things)
    Abstract Existing speech retrieval systems are frequently confronted with expanding volumes of speech data. The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’ real-time retrieval requirements. This study proposes an efficient method for retrieving encryption speech, using unsupervised deep hashing and B+ tree dynamic index, which avoid privacy leakage of speech data and enhance the accuracy and efficiency of retrieval. The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan (DGHV) Fully Homomorphic Encryption (FHE) technique, which encrypts the original speech.… More >

  • Open AccessOpen 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 AccessOpen Access

    ARTICLE

    Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

    Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 133-150, 2023, DOI:10.32604/cmc.2023.039518
    (This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
    Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

    Oussama Accouche1,*, Rajan Kumar Gangadhari2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 151-173, 2023, DOI:10.32604/cmc.2023.038648
    (This article belongs to the Special Issue: Energy Efficiency and Energy Consumption of Sensor Network for Applications, Including the Various Methods of Energy Storage)
    Abstract Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from… More >

  • Open AccessOpen Access

    ARTICLE

    Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

    Huaxin Zhou1,2, Ziying Fang3, Yilin Wang4, Mengjun Tong1,2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 175-194, 2023, DOI:10.32604/cmc.2023.037342
    (This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data.… More >

  • Open AccessOpen Access

    ARTICLE

    Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

    Sarab AlMuhaideb*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients… More >

  • Open AccessOpen Access

    ARTICLE

    XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

    Yuna Han1, Hangbae Chang2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous… More >

  • Open AccessOpen Access

    ARTICLE

    Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

    Peng Liu1,*, Ke Xu1,2, Hua Gong1,2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 239-258, 2023, DOI:10.32604/cmc.2023.038232
    Abstract Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry, the coordinated scheduling problem of parallel machine production and transportation is investigated. The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance. A non-cooperative game model is established, considering the competition and self-interest behavior of jobs from different customers for machine resources. The job from different customers is mapped to the players in the game model, the corresponding optional processing machine and location are mapped to the… More >

  • Open AccessOpen Access

    ARTICLE

    Quantum-Enhanced Blockchain: A Secure and Practical Blockchain Scheme

    Ang Liu1,2, Xiu-Bo Chen1,*, Gang Xu3, Zhuo Wang4, Xuefen Feng5, Huamin Feng6
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 259-277, 2023, DOI:10.32604/cmc.2023.039397
    Abstract The rapid advancement of quantum technology poses significant security risks to blockchain systems. However, quantum technology can also provide solutions for enhancing blockchain security. In this paper, we propose a quantum-enhanced blockchain scheme to achieve a high level of security against quantum computing attacks. We first discuss quantum computing attacks on classic blockchains, including attacks on hash functions, digital signatures, and consensus mechanisms. We then introduce quantum technologies, such as a quantum hash function (QHF), a quantum digital signature (QDS), and proof of authority (PoA) consensus mechanism, into our scheme to improve the security of More >

  • Open AccessOpen Access

    ARTICLE

    Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

    Dawei Liang1,2,4, Meng Wu1,*, Yan Hu3
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 279-293, 2023, DOI:10.32604/cmc.2023.035279
    Abstract As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship… More >

  • Open AccessOpen Access

    ARTICLE

    Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

    Deye Jiang1, Yiguang Wang2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244
    Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of… More >

  • Open AccessOpen Access

    ARTICLE

    Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

    Bhawna Goyal1,*, Ayush Dogra2, Rahul Khoond1, Dawa Chyophel Lepcha1, Vishal Goyal3, Steven L. Fernandes4
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 311-327, 2023, DOI:10.32604/cmc.2023.038398
    (This article belongs to the Special Issue: Susceptibility to Adversarial Attacks and Defense in Deep Learning Systems)
    Abstract The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely… More >

  • Open AccessOpen Access

    ARTICLE

    Unsupervised Anomaly Detection Approach Based on Adversarial Memory Autoencoders for Multivariate Time Series

    Tianzi Zhao1,2,3,4, Liang Jin1,2,3,*, Xiaofeng Zhou1,2,3, Shuai Li1,2,3, Shurui Liu1,2,3,4, Jiang Zhu1,2,3
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 329-346, 2023, DOI:10.32604/cmc.2023.038595
    Abstract The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

    Niharika Gupta1, Baijnath Kaushik1, Mohammad Khalid Imam Rahmani2,*, Saima Anwar Lashari2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 347-366, 2023, DOI:10.32604/cmc.2023.038864
    Abstract Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision… More >

  • Open AccessOpen Access

    ARTICLE

    Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

    Mona Jamjoom1, Ahmed Elhadad2, Hussein Abulkasim3,*, Safia Abbas4
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310
    Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, More >

  • Open AccessOpen 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… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

    Jian Feng*, Yuwen Wang, Shaojian Chen
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 397-413, 2023, DOI:10.32604/cmc.2023.038741
    Abstract Session-based Recommendation (SBR) aims to accurately recommend a list of items to users based on anonymous historical session sequences. Existing methods for SBR suffer from several limitations: SBR based on Graph Neural Network often has information loss when constructing session graphs; Inadequate consideration is given to influencing factors, such as item price, and users’ dynamic interest evolution is not taken into account. A new session recommendation model called Price-aware Session-based Recommendation (PASBR) is proposed to address these limitations. PASBR constructs session graphs by information lossless approaches to fully encode the original session information, then introduces More >

  • Open AccessOpen Access

    ARTICLE

    Smart Shoes Safety System for the Blind People Based on (IoT) Technology

    Ammar Almomani1,2,*, Mohammad Alauthman3, Amal Malkawi2, Hadeel Shwaihet2, Batool Aldigide2, Donia Aldabeek2, Karmen Abu Hamoodeh2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 415-436, 2023, DOI:10.32604/cmc.2023.036266
    (This article belongs to the Special Issue: Security, Privacy and Trust Management for IoT-based Blockchain)
    Abstract People’s lives have become easier and simpler as technology has proliferated. This is especially true with the Internet of Things (IoT). The biggest problem for blind people is figuring out how to get where they want to go. People with good eyesight need to help these people. Smart shoes are a technique that helps blind people find their way when they walk. So, a special shoe has been made to help blind people walk safely without worrying about running into other people or solid objects. In this research, we are making a new safety system… More >

  • Open AccessOpen Access

    ARTICLE

    A New Multi Chaos-Based Compression Sensing Image Encryption

    Fadia Ali Khan1, Jameel Ahmed1, Suliman A. Alsuhibany2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 437-453, 2023, DOI:10.32604/cmc.2023.032236
    (This article belongs to the Special Issue: Security, Privacy and Trust Management for IoT-based Blockchain)
    Abstract The advancements in technology have substantially grown the size of image data. Traditional image encryption algorithms have limited capabilities to deal with the emerging challenges in big data, including compression and noise toleration. An image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this study. The proposed scheme is built on the intriguing characteristics of an orthogonal matrix. Gram Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic map. Following the diffusion process, a block-wise random permutation of the More >

  • Open AccessOpen Access

    ARTICLE

    Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

    Md. Salah Uddin Afsar1, Mohammad Rashed Iqbal Faruque1,*, Sabirin Abdullah1, Mohammad Tariqul Islam2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 455-469, 2023, DOI:10.32604/cmc.2023.036655
    (This article belongs to the Special Issue: Recent Developments in Antennas and Wireless Propagation)
    Abstract This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic… More >

  • Open AccessOpen 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
    (This article belongs to the Special Issue: Multimedia Encryption and Information Security)
    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… More >

  • Open AccessOpen 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
    (This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Facial Expression Recognition Model Depending on Optimized Support Vector Machine

    Amel Ali Alhussan1, Fatma M. Talaat2, El-Sayed M. El-kenawy3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Doaa Sami Khafaga1,*, Mona Alnaggar7
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368
    (This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >

  • Open AccessOpen Access

    ARTICLE

    Unsupervised Log Anomaly Detection Method Based on Multi-Feature

    Shiming He1, Tuo Deng1, Bowen Chen1, R. Simon Sherratt2, Jin Wang1,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392
    Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based… More >

  • Open AccessOpen Access

    ARTICLE

    A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

    Xiuye Liu, Aihua Wu*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 543-559, 2023, DOI:10.32604/cmc.2023.039004
    Abstract The human motion generation model can extract structural features from existing human motion capture data, and the generated data makes animated characters move. The 3D human motion capture sequences contain complex spatial-temporal structures, and the deep learning model can fully describe the potential semantic structure of human motion. To improve the authenticity of the generated human motion sequences, we propose a multi-task motion generation model that consists of a discriminator and a generator. The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17… More >

  • Open AccessOpen Access

    ARTICLE

    Generation of Low-Delay and High-Stability Multicast Tree

    Deshun Li1, Zhenchen Wang2, Yucong Wei2, Jiangyuan Yao1,*, Yuyin Tan2, Qiuling Yang1, Zhengxia Wang1, Xingcan Cao3
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 561-572, 2023, DOI:10.32604/cmc.2023.033332
    Abstract Delay and stability are two key factors that affect the performance of multicast data transmission in a network. However, current algorithms of tree generation hardly meet the requirements of low delay and high stability simultaneously. Given a general network, the generation algorithm of a multicast tree with minimum delay and maximum stability is an NP-hard problem, without a precise and efficient algorithm. To address these challenges, this paper studies the generation of low-delay and high-stability multicast trees under the model of spanning tree based on stability probability, degree-constrained, edge-weighted for multicast (T-SDE). A class of algorithms… More >

  • Open AccessOpen Access

    ARTICLE

    Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

    Halah Hasan Mahmoud1, Marwan Kadhim Mohammed Al-Shammari1, Gehad Abdullah Amran2,3,*, Elsayed Tag eldin4,*, Ala R. Alareqi5, Nivin A. Ghamry6, Ehaa ALnajjar7, Esmail Almosharea8
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 573-589, 2023, DOI:10.32604/cmc.2023.039363
    (This article belongs to the Special Issue: Intelligent Technologies and Applications for Future Wireless Communications)
    Abstract Utilizing artificial intelligence (AI) to protect smart coastal cities has become a novel vision for scientific and industrial institutions. One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles (UVs) for anti-submarine attacks. This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy. The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach, and the Re-fragmentation strategy is used in the Network… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT

    Chaoyang Li*, Bohao Jiang, Yanbu Guo, Xiangjun Xin
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 591-606, 2023, DOI:10.32604/cmc.2023.038129
    Abstract Blockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves the cross-institution data sharing ability, there still exist the problems of authentication difficulty and privacy leakage. This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing. This BIoMT system is divided into four layers: perceptual, network, platform, and application. The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger.… More >

  • Open AccessOpen Access

    ARTICLE

    Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

    Jeongha Lee1, Hyokyung Bahn2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236
    Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when… More >

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    ARTICLE

    Harnessing Blockchain to Address Plasma Donation Network Challenges

    Shivani Batra1, Mohammad Zubair Khan2,*, Gatish Priyadarshi3, Ayman Noor4, Talal H. Noor5, Namrata Sukhija6, Prakash Srivastava7
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 631-646, 2023, DOI:10.32604/cmc.2023.039241
    Abstract Plasma therapy is an extensively used treatment for critically unwell patients. For this procedure, a legitimate plasma donor who can continue to supply plasma after healing is needed. However, significant dangers are associated with supply management, such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal fabrication. Also, from an ideological standpoint, less powerful people may be exploited throughout the contribution process. Moreover, there is a danger to the logistics system because there are now just some plasma shippers. This research intends to investigate the blockchain-based solution for More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

    Wajiha Rahim Khan1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Majed Alhaisoni4, Usman Tariq5, Jae-Hyuk Cha6,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188
    (This article belongs to the Special Issue: Cancer Diagnosis using Deep Learning, Federated Learning, and Features Optimization Techniques)
    Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes… More >

  • Open AccessOpen Access

    ARTICLE

    Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

    Wadhah Mohammed M. Aqlan1, Ghassan Ahmed Ali2,*, Khairan Rajab2, Adel Rajab2, Asadullah Shaikh2, Fekry Olayah2, Shehab Abdulhabib Saeed Alzaeemi3,*, Kim Gaik Tay3, Mohd Adib Omar1, Ernest Mangantig4
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 665-686, 2023, DOI:10.32604/cmc.2023.039228
    Abstract Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

    Muhammad Saad1, Maaz Bin Ahmad1,*, Muhammad Asif2, Muhammad Khalid Khan1, Toqeer Mahmood3, Elsayed Tag Eldin4,*, Hala Abdel Hameed5,6
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 687-709, 2023, DOI:10.32604/cmc.2023.038335
    (This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
    Abstract Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact… More >

  • Open AccessOpen Access

    ARTICLE

    Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

    Muhammad Irfan1, Ahmad Shaf2,*, Tariq Ali2, Umar Farooq2, Saifur Rahman1, Salim Nasar Faraj Mursal1, Mohammed Jalalah1, Samar M. Alqhtani3, Omar AlShorman4
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 711-729, 2023, DOI:10.32604/cmc.2023.038176
    (This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433
    (This article belongs to the Special Issue: Smart Solutions to Develop New Technologies for Healthcare)
    Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two… More >

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    ARTICLE

    NTRU_ SSS: Anew Method Signcryption Post Quantum Cryptography Based on Shamir’s Secret Sharing

    Asma Ibrahim Hussein1,*, Abeer Tariq MaoLood2, Ekhlas Khalaf Gbashi2
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 753-769, 2023, DOI:10.32604/cmc.2023.039804
    Abstract With the advent of quantum computing, numerous efforts have been made to standardize post-quantum cryptosystems with the intention of (eventually) replacing Elliptic Curve Cryptography (ECC) and Rivets-Shamir-Adelman (RSA). A modified version of the traditional N-Th Degree Truncated Polynomial Ring (NTRU) cryptosystem called NTRU Prime has been developed to reduce the attack surface. In this paper, the Signcryption scheme was proposed, and it is most efficient than others since it reduces the complexity and runs the time of the code execution, and at the same time, provides a better security degree since it ensures the integrity… More >

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    ARTICLE

    Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform

    Leilei Li1, Yansong Wang2, Dongjie Zhu2,*, Xiaofang Li3, Haiwen Du4, Yixuan Lu2, Rongning Qu3, Russell Higgs5
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 771-791, 2023, DOI:10.32604/cmc.2023.038268
    Abstract With the rapid development of Internet technology, the issues of network asset detection and vulnerability warning have become hot topics of concern in the industry. However, most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks, which cannot meet the current needs of the industry. To address the above issues, this paper proposes a distributed network asset detection and vulnerability warning platform (Dis-NDVW) based on distributed systems and multiple detection tools. Specifically, this paper proposes a distributed message subscription and publication system based on Zookeeper and Kafka, which endows Dis-NDVW… More >

  • Open AccessOpen Access

    ARTICLE

    Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

    Deepthi K. Oommen*, J. Arunnehru
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640
    Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the… More >

  • Open AccessOpen Access

    ARTICLE

    Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

    Semi Park, Kyungho Lee*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 813-826, 2023, DOI:10.32604/cmc.2023.035313
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study,… More >

  • Open AccessOpen Access

    ARTICLE

    A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

    Junqi Guo1,2, Qingyun Xiong1,*, Minghui Yang1, Ziyun Zhao1
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 827-848, 2023, DOI:10.32604/cmc.2023.036450
    (This article belongs to the Special Issue: Recent Advances on Security and Privacy of Multimedia Big Data in the Social Internet of Things)
    Abstract Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time… More >

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    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… More >

  • Open AccessOpen Access

    ARTICLE

    Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

    Sara Khalid1, Jamal Hussain Shah1,*, Muhammad Sharif1, Muhammad Rafiq2, Gyu Sang Choi3,*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 861-879, 2023, DOI:10.32604/cmc.2023.035595
    (This article belongs to the Special Issue: Recent Advances in Hyper Parameters Optimization, Features Optimization, and Deep Learning for Video Surveillance and Biometric Applications)
    Abstract Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians. Consequently, traffic signs have been of great importance for every civilized country, which makes researchers give more focus on the automatic detection of traffic signs. Detecting these traffic signs is challenging due to being in the dark, far away, partially occluded, and affected by the lighting or the presence of similar objects. An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues. This technique aimed to devise… More >

  • Open AccessOpen Access

    ARTICLE

    Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

    Mohammed A. Al Ghamdi*
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 881-894, 2023, DOI:10.32604/cmc.2023.037826
    Abstract Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively… More >

  • Open AccessOpen 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
    (This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    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 More >

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