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REVIEW

Yong Li^1,4, Qiming Liang^2,*, Bo Gan³, Xiaolong Cui⁴
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494
Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the… More >

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Premanand S., Sathiya Narayanan^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590
Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a… More >

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Xiaorui Zhang^1,2,3,*, Qijian Xie¹, Wei Sun^3,4, Yongjun Ren^1,2,3, Mithun Mukherjee⁵
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 47-61, 2023, DOI:10.32604/cmc.2023.042561
Abstract Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by… More >

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633

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ARTICLE

Shorouq Alshawabkeh, Li Wu^*, Daojun Dong, Yao Cheng, Liping Li, Mohammad Alanaqreh
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 63-77, 2023, DOI:10.32604/cmc.2023.042183
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC). Our findings reveal that Model… More >

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ARTICLE

Jianguo Wang¹, Kuan Zhang^1,*, Yuesheng Zhao^2,*, Xiaoling Wang², Muhammad Shamrooz Aslam²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506
Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm,… More >

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656

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ARTICLE

Yuyuan Xiang, Yangli Jia^*, Xiangliang Zhang, Zhenling Zhang
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 97-114, 2023, DOI:10.32604/cmc.2023.040751
Abstract Event extraction stands as a significant endeavor within the realm of information extraction, aspiring to automatically extract structured event information from vast volumes of unstructured text. Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data. Although researchers have proposed various methods to accomplish this task, most existing event extraction models cannot address these challenges because they are only applicable to text scenarios. To solve the above issues, this paper proposes a multi-modal event extraction method based on knowledge fusion. Specifically, for event-type… More >

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Bishrul Haq¹, Sher Muhammad Daudpota¹, Ali Shariq Imran², Zenun Kastrati^3,*, Waheed Noor⁴
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human… More >

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4

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ARTICLE

Yan Hong^1,2, Shihui Fang^2,*, Jingming Su², Wanqiu Xu², Yuhao Wei², Juan Wu², Zhen Yang^1,3,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 139-160, 2023, DOI:10.32604/cmc.2023.043424
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract In today’s information society, image encryption technology is crucial to protecting Internet security. However, traditional image encryption algorithms have problems such as insufficient chaotic characteristics, insufficient randomness of keys, and insecure Ribonucleic Acid (RNA) encoding. To address these issues, a chaos-RNA encryption scheme that combines chaotic maps and RNA encoding was proposed in this research. The initial values and parameters of the chaotic system are first generated using the Secure Hash Algorithm 384 (SHA-384) function and the plaintext image. Next, the Lü hyperchaotic system sequence was introduced to change the image’s pixel values to realize block scrambling, and further disturbance… More >

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Ahmed Abdu¹, Zhengjun Zhai^1,2, Hakim A. Abdo³, Redhwan Algabri^4,*, Sungon Lee^5,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 161-180, 2023, DOI:10.32604/cmc.2023.043680
Abstract Cross-project software defect prediction (CPDP) aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects. The existing CPDP approaches rely on static metrics or dynamic syntactic features, which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties, such as complex design patterns, relationships between multiple functions, and dependencies in different software projects, that are important for CPDP. This paper introduces a novel approach, a graph-based feature learning model for CPDP (GB-CPDP), that utilizes NetworkX to extract features and learn representations of program entities… More >

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ARTICLE

Xi Zhao¹, Haizheng Yu^1,*, Hong Bian²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 181-198, 2023, DOI:10.32604/cmc.2023.041479
Abstract In recent years, Pix2Pix, a model within the domain of GANs, has found widespread application in the field of image-to-image translation. However, traditional Pix2Pix models suffer from significant drawbacks in image generation, such as the loss of important information features during the encoding and decoding processes, as well as a lack of constraints during the training process. To address these issues and improve the quality of Pix2Pix-generated images, this paper introduces two key enhancements. Firstly, to reduce information loss during encoding and decoding, we utilize the U-Net++ network as the generator for the Pix2Pix model, incorporating denser skip-connection to minimize… More >

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199

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ARTICLE

Wenshu Xu, Mingwei Lin^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 199-222, 2023, DOI:10.32604/cmc.2023.041475
Abstract Industrial control systems (ICSs) are widely used in various fields, and the information security problems of ICSs are increasingly serious. The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts. Thus, this paper introduces the probabilistic linguistic term sets (PLTSs) to model the evaluation information of experts. Meanwhile, we propose a probabilistic linguistic multi-criteria decision-making (PL-MCDM) method to solve the information security assessment problem of ICSs. Firstly, we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods. Secondly, we use the Best Worst Method (BWM) method and Criteria… More >

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507

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189

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ARTICLE

Jiang Li, Jiutao Zhao, Qinhui Liu^*, Laizheng Zhu, Jinyi Guo, Weijiu Zhang
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 223-244, 2023, DOI:10.32604/cmc.2023.042429
Abstract Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through equal probability orthogonal experiments on… More >

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551

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ARTICLE

Cheng Feng^*, Dezhi Han, Chongqing Chen
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 245-261, 2023, DOI:10.32604/cmc.2023.042765
Abstract Person search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network (CNN) (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through… More >

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160

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ARTICLE

Mengjuan Li, Lei Niu^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 263-284, 2023, DOI:10.32604/cmc.2023.041193
（This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
Abstract In the era of artificial intelligence, cognitive computing, based on cognitive science; and supported by machine learning and big data, brings personalization into every corner of our social life. Recommendation systems are essential applications of cognitive computing in educational scenarios. They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress. The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model (LFCKT-ER). First, the model computes studentsʼ ability to understand each knowledge concept, and the learning progress of each knowledge concept, and the model consider their forgetting behavior… More >

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340

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177

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Zhaoan Wang¹, Shaoping Xiao^1,*, Cheryl Reuben², Qiyu Wang², Jun Wang²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366
Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can capture the intricate pulse patterns… More >

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325

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198

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ARTICLE

Zhen Zhen¹, Jian Gao^1,2,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 299-323, 2023, DOI:10.32604/cmc.2023.042090
Abstract In recent years, cyber attacks have been intensifying and causing great harm to individuals, companies, and countries. The mining of cyber threat intelligence (CTI) can facilitate intelligence integration and serve well in combating cyber attacks. Named Entity Recognition (NER), as a crucial component of text mining, can structure complex CTI text and aid cybersecurity professionals in effectively countering threats. However, current CTI NER research has mainly focused on studying English CTI. In the limited studies conducted on Chinese text, existing models have shown poor performance. To fully utilize the power of Chinese pre-trained language models (PLMs) and conquer the problem… More >

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248

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REVIEW

Mozamel M. Saeed^1,*, Zaher Al Aghbari²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 325-348, 2023, DOI:10.32604/cmc.2023.036894
Abstract Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber security of email in different… More >

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4

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ARTICLE

Shuqin Zhang, Xinyu Su^*, Peiyu Shi, Tianhui Du, Yunfei Han
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 349-377, 2023, DOI:10.32604/cmc.2023.040964
（This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
Abstract Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate and map multi-source threat information,… More >

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ARTICLE

Abdelwahed Berguiga^1,2,*, Ahlem Harchay^1,2, Ayman Massaoudi^1,2, Mossaad Ben Ayed³, Hafedh Belmabrouk⁴
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667
Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

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Ali Khan^1,4, Somaiya Khan², Bilal Hassan³, Rizwan Khan^1,4, Zhonglong Zheng^1,4,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 403-424, 2023, DOI:10.32604/cmc.2023.040251
Abstract Smoking has an economic and environmental impact on society due to the toxic substances it emits. Convolutional Neural Networks (CNNs) need help describing low-level features and can miss important information. Moreover, accurate smoker detection is vital with minimum false alarms. To answer the issue, the researchers of this paper have turned to a self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. To effectively enforce the smoking prohibition in non-smoking locations, this work presents a Vision Transformer-inspired model called SmokerViT for detecting smokers. Moreover, this research utilizes a locally curated dataset of 1120 images… More >

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Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561
（This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

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P. Kuppusamy^1,*, M. Sanjay¹, P. V. Deepashree¹, C. Iwendi²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 445-466, 2023, DOI:10.32604/cmc.2023.042675
Abstract The infrastructure and construction of roads are crucial for the economic and social development of a region, but traffic-related challenges like accidents and congestion persist. Artificial Intelligence (AI) and Machine Learning (ML) have been used in road infrastructure and construction, particularly with the Internet of Things (IoT) devices. Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing traffic-related problems. This study aims to use You Only Look Once version 7 (YOLOv7), Convolutional Block Attention Module (CBAM), the most optimized object-detection algorithm, to detect and identify traffic signs, and analyze effective combinations of adaptive… More >

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2027

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6

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ARTICLE

Wenkai Qin¹, Tianliang Lu^1,*, Lu Zhang², Shufan Peng¹, Da Wan¹
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >

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Shadi Al-Sarawi¹, Mohammed Anbar^1,*, Basim Ahmad Alabsi², Mohammad Adnan Aladaileh³, Shaza Dawood Ahmed Rihan²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 491-515, 2023, DOI:10.32604/cmc.2023.041108
Abstract The Internet of Things (IoT) consists of interconnected smart devices communicating and collecting data. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standard protocol for Internet Protocol Version 6 (IPv6) in the IoT. However, RPL is vulnerable to various attacks, including the sinkhole attack, which disrupts the network by manipulating routing information. This paper proposes the Unweighted Voting Method (UVM) for sinkhole node identification, utilizing three key behavioral indicators: DODAG Information Object (DIO) Transaction Frequency, Rank Harmony, and Power Consumption. These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant… More >

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ARTICLE

Litian Huang, Xinguo Yu, Lei Niu^*, Zihan Feng
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 517-539, 2023, DOI:10.32604/cmc.2023.041206
（This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
Abstract Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information. The three-phase scheme… More >

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Hao Meng, Xinguo Yu^*, Bin He, Litian Huang, Liang Xue, Zongyou Qiu
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 541-555, 2023, DOI:10.32604/cmc.2023.041508
（This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
Abstract Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set of qualia-quantity models to identify… More >

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Mohammed Aly^1,*, Abdullah Shawan Alotaibi²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 557-582, 2023, DOI:10.32604/cmc.2023.042493
Abstract Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection.… More >

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Fadhil Mukhlif^1,*, Norafida Ithnin¹, Roobaea Alroobaea², Sultan Algarni³, Wael Y. Alghamdi², Ibrahim Hashem⁴
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 583-599, 2023, DOI:10.32604/cmc.2023.038757
Abstract The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research… More >

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Fiaz Majeed¹, Afzaal Ahmad¹, Muhammad Awais Hassan², Muhammad Shafiq^3,*, Jin-Ghoo Choi³, Habib Hamam^4,5,6,7
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 601-614, 2023, DOI:10.32604/cmc.2023.036074
Abstract Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word mismatches due to context changes… More >

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Allam Abumwais^*, Mujahed Eleyat
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 615-632, 2023, DOI:10.32604/cmc.2023.038810
Abstract Recent architectures of multi-core systems may have a relatively large number of cores that typically ranges from tens to hundreds; therefore called many-core systems. Such systems require an efficient interconnection network that tries to address two major problems. First, the overhead of power and area cost and its effect on scalability. Second, high access latency is caused by multiple cores’ simultaneous accesses of the same shared module. This paper presents an interconnection scheme called N-conjugate Shuffle Clusters (NCSC) based on multi-core multi-cluster architecture to reduce the overhead of the just mentioned problems. NCSC eliminated the need for router devices and… More >

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Fuad A. M. Al-Yarimi^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 633-645, 2023, DOI:10.32604/cmc.2023.038858
Abstract Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for… More >

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Fayaz Muhammad¹, Jahangir Khan¹, Asad Ullah¹, Fasee Ullah¹, Razaullah Khan², Inayat Khan², Mohammed ElAffendi³, Gauhar Ali^3,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748
（This article belongs to the Special Issue: Emerging Trends, Advances and Challenges of IoT in Healthcare and Education)
Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

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Sidra Abbas¹, Gabriel Avelino Sampedro^2,3, Shtwai Alsubai⁴, Ahmad Almadhor⁵, Tai-hoon Kim^6,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 665-680, 2023, DOI:10.32604/cmc.2023.041031
Abstract Cardiac disease is a chronic condition that impairs the heart’s functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning (ML) and Deep Learning (DL) to identify heart abnormalities swiftly and consistently. Various approaches have been applied to predict and treat heart disease utilizing ML and DL. This paper proposes a Machine and Deep Learning-based Stacked Model (MDLSM) to predict heart disease accurately. ML approaches such as eXtreme Gradient Boosting (XGB),… More >

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Uzair Aslam Bhatti^1,*, Sibghat Ullah Bazai², Shumaila Hussain¹, Shariqa Fakhar³, Chin Soon Ku^4,*, Shah Marjan⁵, Por Lip Yee⁶, Liu Jing¹
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 681-697, 2023, DOI:10.32604/cmc.2023.037958
Abstract Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages… More >

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Aaqib Inam¹, Zhu Li^1,*, Salah-ud-din Khokhar², Zubia Zafar³, Muhammad Imran⁴
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 699-715, 2023, DOI:10.32604/cmc.2023.039707
（This article belongs to the Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)
Abstract Mosquitoes are of great concern for occasionally carrying noxious diseases (dengue, malaria, zika, and yellow fever). To control mosquitoes, it is very crucial to effectively monitor their behavioral trends and presence. Traditional mosquito repellent works by heating small pads soaked in repellant, which then diffuses a protected area around you, a great alternative to spraying yourself with insecticide. But they have limitations, including the range, turning them on manually, and then waiting for the protection to kick in when the mosquitoes may find you. This research aims to design a fuzzy-based controller to solve the above issues by automatically determining… More >

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Dana F. Doghramachi^1,*, Siddeeq Y. Ameen²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 717-732, 2023, DOI:10.32604/cmc.2023.041186
Abstract The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a… More >

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C. Rajathi, P. Rukmani^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 733-756, 2023, DOI:10.32604/cmc.2023.041507
Abstract Considering the recent developments in the digital environment, ensuring a higher level of security for networking systems is imperative. Many security approaches are being constantly developed to protect against evolving threats. An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies. This research work aimed to create a more diverse and effective ensemble model. To this end, selected six classification models, Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) from existing study to run as independent models. Once the… More >

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Ju Hyeon Lee¹, Jiho Shin², Jung Taek Seo^3,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 757-779, 2023, DOI:10.32604/cmc.2023.039461
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making it difficult to detect cyberattacks.… More >

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Yazeed Yasin Ghadi¹, Mohammed S. Alshehri², Sultan Almakdi², Oumaima Saidani^3,*, Nazik Alturki³, Fawad Masood⁴, Muhammad Shahbaz Khan⁵
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 781-797, 2023, DOI:10.32604/cmc.2023.042777
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Securing digital image data is a key concern in today’s information-driven society. Effective encryption techniques are required to protect sensitive image data, with the Substitution-box (S-box) often playing a pivotal role in many symmetric encryption systems. This study introduces an innovative approach to creating S-boxes for encryption algorithms. The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme. The nonlinearity measure of the proposed S-boxes is 112. These qualities significantly enhance its resistance to common cryptographic attacks, ensuring high image data security. Furthermore, to assess the robustness of the S-boxes, an encryption system… More >

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Guangjun Liang^1,2,3, Jianfang Xin^4,*, Qun Wang^1,2, Xueli Ni^1,2,3, Xiangmin Guo^1,2,3, Pu Chen¹
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 799-825, 2023, DOI:10.32604/cmc.2023.038403
Abstract As a subversive concept, the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic, industrial, and investment circles. However, while the metaverse brings unprecedented opportunities for transformation to human society, it also contains related risks. Metaverse is a digital living space with information infrastructure, interoperability system, content production system, and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities. Through social incentives and governance rules, the metaverse reflects the digital migration of human society. This article will conduct… More >

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Muhammad Ali Khan¹, Rao Naveed Bin Rais^2,*, Osman Khalid¹
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 827-843, 2023, DOI:10.32604/cmc.2023.040756
Abstract The Internet of Things (IoT) comprises numerous resource-constrained devices that generate large volumes of data. The inherent vulnerabilities in IoT infrastructure, such as easily spoofed IP and MAC addresses, pose significant security challenges. Traditional routing protocols designed for wired or wireless networks may not be suitable for IoT networks due to their limitations. Therefore, the Routing Protocol for Low-Power and Lossy Networks (RPL) is widely used in IoT systems. However, the built-in security mechanism of RPL is inadequate in defending against sophisticated routing attacks, including Sybil attacks. To address these issues, this paper proposes a centralized and collaborative approach for… More >

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Yixia Chen^1,2, Mingwei Lin^1,2,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 845-866, 2023, DOI:10.32604/cmc.2023.040970
Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase efficiency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term set (PHFLTS). To this end,… More >

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Yixin Tang^*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 867-891, 2023, DOI:10.32604/cmc.2023.041550
Abstract Cartoons serve as significant sources of entertainment for children and adolescents. However, numerous animated videos contain unsuitable content, such as violence, eroticism, abuse, and vehicular accidents. Current content detection methods rely on manual inspection, which is resource-intensive, time-consuming, and not always reliable. Therefore, more efficient detection methods are necessary to safeguard young viewers. This paper addresses this significant problem by proposing a novel deep learning-based system, AnimeNet, designed to detect varying degrees of violent and erotic content in videos. AnimeNet utilizes a novel Convolutional Neural Network (CNN) model to extract image features effectively, classifying violent and erotic scenes in videos… More >

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340

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Chengfei Hua^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862
Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization of topic features. In light… More >

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Peiyuan Jiang¹, Weijun Pan^1,*, Jian Zhang¹, Teng Wang¹, Junxiang Huang²
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 911-940, 2023, DOI:10.32604/cmc.2023.041772
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition (ASR) technology in the Air Traffic Control (ATC) field. This paper presents a novel cascaded model architecture, namely Conformer-CTC/Attention-T5 (CCAT), to build a highly accurate and robust ATC speech recognition model. To tackle the challenges posed by noise and fast speech rate in ATC, the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms. On the decoding side, the Attention mechanism is integrated to facilitate precise alignment between input features and output characters. The Text-To-Text… More >

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Yongzhi Min^1,2,*, Jiafeng Li³, Yaxing Li¹
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 941-962, 2023, DOI:10.32604/cmc.2023.041182
Abstract To guarantee the safety of railway operations, the swift detection of rail surface defects becomes imperative. Traditional methods of manual inspection and conventional nondestructive testing prove inefficient, especially when scaling to extensive railway networks. Moreover, the unpredictable and intricate nature of defect edge shapes further complicates detection efforts. Addressing these challenges, this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network (UPerNet) tailored for rail surface defect detection. Notably, the Swin Transformer Tiny version (Swin-T) network, underpinned by the Transformer architecture, is employed for adept feature extraction. This approach capitalizes on the global information present in the image… More >

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Farman Ali¹, Khalid Zaman², Babar Shah³, Tariq Hussain⁴, Habib Ullah⁵, Altaf Hussain⁵, Daehan Kwak^6,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 963-981, 2023, DOI:10.32604/cmc.2023.040628
（This article belongs to the Special Issue: AI-driven Cybersecurity in Cyber Physical Systems enabled Healthcare, Current Challenges, Requirements and Future research Foresights)
Abstract The paper presents a new protocol called Link Stability and Transmission Delay Aware (LSTDA) for Flying Ad-hoc Network (FANET) with a focus on network corridors (NC). FANET consists of Unmanned Aerial Vehicles (UAVs) that face challenges in avoiding transmission loss and delay while ensuring stable communication. The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network. The protocol uses a Red-Black (R-B) tree to achieve maximum channel utilization and an advanced relay approach. The paper evaluates LSTDA in terms of End-to-End Delay (E2ED), Packet Delivery Ratio… More >

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Zhihong Ouyang^*, Lei Xue, Feng Ding, Yongsheng Duan
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 983-1008, 2023, DOI:10.32604/cmc.2023.042222
Abstract Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering approach generates an additional set… More >

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Somin Park¹, Mpabulungi Mark¹, Bogyung Park², Hyunki Hong^1,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1009-1030, 2023, DOI:10.32604/cmc.2023.041332
Abstract Speech emotion recognition is essential for frictionless human-machine interaction, where machines respond to human instructions with context-aware actions. The properties of individuals’ voices vary with culture, language, gender, and personality. These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition (SER). This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models. In the proposed approach, two wav2vec-based modules (a speaker-identification network and an emotion classification network) are trained with the Arcface loss. The speaker-identification network has a… More >

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Jiwei Fan, Xiaogang Yang^*, Ruitao Lu, Qingge Li, Siyu Wang
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1031-1052, 2023, DOI:10.32604/cmc.2023.039582
Abstract In recent years, many visual positioning algorithms have been proposed based on computer vision and they have achieved good results. However, these algorithms have a single function, cannot perceive the environment, and have poor versatility, and there is a certain mismatch phenomenon, which affects the positioning accuracy. Therefore, this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle (UAV) environment perception and multi-modal image-matching fusion location. This algorithm was based on the single-shot object detector based on multi-level feature pyramid network (M2Det) algorithm and… More >

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