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ARTICLE

Manar Ahmed Hamza^1,*, Hanan Abdullah Mengash², Noha Negm³, Radwa Marzouk², Abdelwahed Motwakel¹, Abu Sarwar Zamani¹
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050
Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of… More >

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Jingtang Luo¹, Shiying Yao¹, Changming Zhao^2,*, Jie Xu³, Jim Feng⁴
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4207-4216, 2023, DOI:10.32604/cmc.2023.034439
Abstract The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a… More >

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385

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ARTICLE

Muhammad Saad Salman¹, Ibrahim M. Almanjahie^2,3, AmanUllah Yasin¹, Ammara Nawaz Cheema^1,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4217-4229, 2023, DOI:10.32604/cmc.2023.034960
Abstract At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed… More >

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327

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ARTICLE

So-Eun Jeon¹, Sun-Jin Lee¹, Eun-Young Lee¹, Yeon-Ji Lee², Jung-Hwa Ryu², Jung-Hyun Moon², Sun-Min Yi², Il-Gu Lee^1,2,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4231-4253, 2023, DOI:10.32604/cmc.2023.034287
Abstract Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and tele-education are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and anti-malware systems that use signature-based… More >

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436

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ARTICLE

Hala J. Alshahrani¹, Abdulkhaleq Q. A. Hassan², Khaled Tarmissi³, Amal S. Mehanna⁴, Abdelwahed Motwakel^5,*, Ishfaq Yaseen⁵, Amgad Atta Abdelmageed⁵, Mohamed I. Eldesouki⁶
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4255-4272, 2023, DOI:10.32604/cmc.2023.034821
Abstract Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform… More >

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427

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ARTICLE

Xiaorui Zhang^1,2,3,*, Chunlin Yuan¹, Wei Sun^3,4, Sunil Kumar Jha⁵
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975
Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional… More >

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399

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ARTICLE

Muhammad Hammad Waseem¹, Malik Sajjad Ahmed Nadeem^1,*, Ishtiaq Rasool Khan², Seong-O-Shim³, Wajid Aziz¹, Usman Habib⁴
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710
Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the prediction ability and the convergence… More >

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352

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ARTICLE

Cong Lin¹, Chenghao Qiu¹, Can Wu¹, Siling Feng^1,*, Mengxing Huang^1,2,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249
Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >

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403

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ARTICLE

Siling Feng¹, Yinjie Chen¹, Mengxing Huang^1,2,*, Feng Shu¹
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4341-4355, 2023, DOI:10.32604/cmc.2023.037154
Abstract Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks that are located far from… More >

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376

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ARTICLE

Jungyeon Lee¹, Malik Summair Asghar^1,2, HyungWon Kim^1,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372
Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the architecture of an analog convolutional… More >

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425

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ARTICLE

Chunzhi Wang, Weidong Cao^*, Yalin Hu, Jinhang Liu
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4377-4393, 2023, DOI:10.32604/cmc.2023.037625
Abstract According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center… More >

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ARTICLE

Mohd Asyraf Zulkifley^1,*, Ali Fayyaz Munir², Mohd Edil Abd Sukor³, Muhammad Hakimi Mohd Shafiai⁴
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4395-4418, 2023, DOI:10.32604/cmc.2023.036094
Abstract A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country’s economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However,… More >

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579

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ARTICLE

Arwa Saif Fadel^1,2,*, Osama Ahmed Abulnaja¹, Mostafa Elsayed Saleh¹
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112
Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the… More >

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441

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ARTICLE

Zhiguo Chen^1,2,*, Jiabing Cao^1,2
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4445-4465, 2023, DOI:10.32604/cmc.2023.038639
Abstract The Corona Virus Disease 2019 (COVID-19) effect has made telecommuting and remote learning the norm. The growing number of Internet-connected devices provides cyber attackers with more attack vectors. The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques, making it difficult to classify and detect malware using conventional approaches. Therefore, this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning (VMCTE). VMCTE has a strong anti-interference ability. Even if malware uses obfuscation, fuzzing, encryption, and other techniques to evade detection, it can be accurately classified into its corresponding malware family. Unlike traditional… More >

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403

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ARTICLE

Heba G. Mohamed¹, Fadwa Alrowais², Mohammed Abdullah Al-Hagery³, Mesfer Al Duhayyim^4,*, Anwer Mustafa Hilal⁵, Abdelwahed Motwakel⁵
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4467-4483, 2023, DOI:10.32604/cmc.2023.036822
Abstract As the Internet of Things (IoT) endures to develop, a huge count of data has been created. An IoT platform is rather sensitive to security challenges as individual data can be leaked, or sensor data could be used to cause accidents. As typical intrusion detection system (IDS) studies can be frequently designed for working well on databases, it can be unknown if they intend to work well in altering network environments. Machine learning (ML) techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy. This article introduces… More >

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ARTICLE

Fatma Taher¹, Hamoud Alshammari², Lobna Osman³, Mohamed Elhoseny⁴, Abdulaziz Shehab^5,2,*, Eman Elayat⁶
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118
Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps,… More >

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495

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ARTICLE

Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu^*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446
Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective channel attention (ECA) module is… More >

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ARTICLE

Zhiping Liang^*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4519-4534, 2023, DOI:10.32604/cmc.2023.037035
Abstract In view of the various adverse effects, fake news detection has become an extremely important task. So far, many detection methods have been proposed, but these methods still have some limitations. For example, only two independently encoded unimodal information are concatenated together, but not integrated with multimodal information to complete the complementary information, and to obtain the correlated information in the news content. This simple fusion approach may lead to the omission of some information and bring some interference to the model. To solve the above problems, this paper proposes the Fake News Detection model based on BLIP (FNDB). First,… More >

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319

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ARTICLE

Anoud A. Mahmoud^1,*, Hanaa A. Sayed^2,3, Sara S. Mohamed¹
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4535-4552, 2023, DOI:10.32604/cmc.2023.037087
Abstract Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based… More >

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376

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ARTICLE

Md. Moddassir Alam¹, Md Mottahir Alam², Muhammad Moinuddin^2,3, Mohammad Tauheed Ahmad⁴, Jabir Hakami⁵, Anis Ahmad Chaudhary⁶, Asif Irshad Khan⁷, Tauheed Khan Mohd^8,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4553-4571, 2023, DOI:10.32604/cmc.2023.032118
Abstract Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based… More >

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755

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316

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ARTICLE

Tusongjiang. Kari¹, Lin Du¹, Aisikaer. Rouzi², Xiaojing Ma^1,*, Zhichao Liu¹, Bo Li¹
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954
Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >

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811

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ARTICLE

Baraa Wasfi Salim¹, Bzar Khidir Hussan², Zainab Salih Ageed³, Subhi R. M. Zeebaree^4,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4593-4609, 2023, DOI:10.32604/cmc.2023.037514
Abstract Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOML-BR) technique using body sensor data. The presented ITSOML-BR technique collects data from… More >

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622

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ARTICLE

Ratih Hikmah Puspita¹, Jehad Ali^1,*, Byeong-hee Roh²
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4611-4631, 2023, DOI:10.32604/cmc.2023.033598
Abstract 5G use cases, for example enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and an ultra-reliable low latency communication (URLLC), need a network architecture capable of sustaining stringent latency and bandwidth requirements; thus, it should be extremely flexible and dynamic. Slicing enables service providers to develop various network slice architectures. As users travel from one coverage region to another area, the call must be routed to a slice that meets the same or different expectations. This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks. Rules of thumb which indicates the accuracy… More >

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ARTICLE

Sameh Abd El-Ghany^1,2, A. A. Abd El-Aziz^1,3,*
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4633-4648, 2023, DOI:10.32604/cmc.2023.035779
Abstract Coronary artery disease (CAD) is one of the most authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of human-trained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned random forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk… More >

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311

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REVIEW

Nur Laila Ab Ghani^1,2,*, Izzatdin Abdul Aziz^1,2, Said Jadid AbdulKadir^1,2
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987
Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >

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