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Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Ahmad Al-Daraiseh³, Mohammad Bany Taha⁴, Mohammad Aladaileh²
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.043627
Abstract Amid the landscape of Cloud Computing (CC), the Cloud Datacenter (DC) stands as a conglomerate of physical servers, whose performance can be hindered by bottlenecks within the realm of proliferating CC services. A linchpin in CC’s performance, the Cloud Service Broker (CSB), orchestrates DC selection. Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck, endangering service quality. To tackle this, deploying an efficient CSB policy becomes imperative, optimizing DC selection to meet stringent Quality-of-Service (QoS) demands. Amidst numerous CSB policies, their implementation grapples with challenges like costs and availability. This article undertakes a holistic… More >

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Kai Long¹, Ayesha Saeed¹, Jinhua Zhang², Yara Diaeldin¹, Feiyu Lu¹, Tao Tao³, Yuhua Li^1,*, Pengwen Sun⁴, Jinshun Yan⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 43-67, 2024, DOI:10.32604/cmes.2023.031538
（This article belongs to the Special Issue: Structural Design and Optimization)
Abstract This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures. These structures, commonly encountered in engineering applications, often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables. As a result, sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges. Over the past several decades, topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds. In comparison to the large-scale equation solution,… More >

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Yue Xue¹, Dunqiu Fan², Shen Su^1,3,*, Jialu Fu¹, Ning Hu¹, Wenmao Liu², Zhihong Tian^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 69-101, 2024, DOI:10.32604/cmes.2023.031488
Abstract Decentralized finance (DeFi) is a general term for a series of financial products and services. It is based on blockchain technology and has attracted people’s attention because of its open, transparent, and intermediary free. Among them, the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention. However, the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years. Herein, we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues. To that end, we investigate the Ethereum-based DeFi security issues: 1)… More >

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Narayanan Ganesh¹, Rajendran Shankar², Miroslav Mahdal³, Janakiraman Senthil Murugan⁴, Jasgurpreet Singh Chohan⁵, Kanak Kalita^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 103-141, 2024, DOI:10.32604/cmes.2023.028018
Abstract Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have… More >

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Xin Liu¹, Jie Li^1,*, Jianwei Zhao², Bin Cao^2,*, Rongge Yan³, Zhihan Lyu⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 143-185, 2024, DOI:10.32604/cmes.2023.030391
Abstract Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the… More >

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Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388
（This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More >

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Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513
（This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

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Ming Gao^1,#, Weiwei Cai^1,#, Yizhang Jiang¹, Wenjun Hu³, Jian Yao², Pengjiang Qian^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015
Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides us with more powerful methods… More >

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Hui Zhang¹, Huaji Zhou^2,*, Li Wang¹, Feng Zhou^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 279-296, 2024, DOI:10.32604/cmes.2023.031497
Abstract This paper proposes a novel open set recognition method, the Spatial Distribution Feature Extraction Network (SDFEN), to address the problem of electromagnetic signal recognition in an open environment. The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors. The designed hybrid loss function considers both intra-class distance and inter-class distance, thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training. Consequently, this method allows unknown classes to occupy a larger… More >

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Jiaqun Wang^1,2, Guanxu Pan², Youhe Zhou², Xiaojing Liu^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 297-318, 2024, DOI:10.32604/cmes.2023.030622
Abstract In this study, a wavelet multi-resolution interpolation Galerkin method (WMIGM) is proposed to solve linear singularly perturbed boundary value problems. Unlike conventional wavelet schemes, the proposed algorithm can be readily extended to special node generation techniques, such as the Shishkin node. Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients. All the shape functions possess the Kronecker delta property, making the imposition of boundary conditions as easy as that in the finite element method. Four numerical examples are studied to demonstrate the validity and accuracy of the proposed… More >

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SangSeok Lee¹, HaeWon Moon¹, Lee Sael^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 319-336, 2024, DOI:10.32604/cmes.2023.031150
Abstract How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data? Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors. For example, air quality tensor data consists of multiple sensory values gathered from wide locations for a long time. Such data, accumulated over time, is redundant and consumes a lot of memory in its raw form. We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks. To… More >

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Hangtian Song^1,2, Xudong Chen^1,2, Chun Zhu³, Qian Yin⁴, Wei Wang^1,2, Qingxiang Meng^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 337-359, 2024, DOI:10.32604/cmes.2023.031496
Abstract When the geological environment of rock masses is disturbed, numerous non-persisting open joints can appear within it. It is crucial to investigate the effect of open joints on the mechanical properties of rock mass. However, it has been challenging to generate realistic open joints in traditional experimental tests and numerical simulations. This paper presents a novel solution to solve the problem. By utilizing the stochastic distribution of joints and an enhanced-fractal interpolation system (IFS) method, rough curves with any orientation can be generated. The Douglas-Peucker algorithm is then applied to simplify these curves by removing unnecessary points while preserving their… More >

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Lu Chen^1,#, Huaqiang Chen^2,#, Zhikai Pan⁷, Sheng Xu², Guangsheng Lai², Shuwen Chen^2,5,6, Shuihua Wang^3,8, Xiaodong Gu^2,6,*, Yudong Zhang^3,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 361-382, 2024, DOI:10.32604/cmes.2023.031229
Abstract Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model’s generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit… More >

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Hongle Guo^1,2, Yingchi Mao^1,2,*, Xiaoming He^1,2, Benteng Zhang^1,2, Tianfu Pang^1,2, Ping Ping^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 383-403, 2024, DOI:10.32604/cmes.2023.031466
Abstract Data sharing and privacy protection are made possible by federated learning, which allows for continuous model parameter sharing between several clients and a central server. Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate, but because the clients are independent, the central server cannot fully control their behavior. The central server has no way of knowing the correctness of the model parameters provided by each client in this round, so clients may purposefully or unwittingly submit anomalous data, leading to abnormal behavior, such as becoming malicious attackers or defective clients.… More >

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Chaoji Liu¹, Xingqiao Liu^1,*, Chong Chen², Kang Zhou¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 405-440, 2024, DOI:10.32604/cmes.2023.031040
Abstract Pose-invariant facial expression recognition (FER) is an active but challenging research topic in computer vision. Especially with the involvement of diverse observation angles, FER makes the training parameter models inconsistent from one view to another. This study develops a deep global multiple-scale and local patches attention (GMS-LPA) dual-branch network for pose-invariant FER to weaken the influence of pose variation and self-occlusion on recognition accuracy. In this research, the designed GMS-LPA network contains four main parts, i.e., the feature extraction module, the global multiple-scale (GMS) module, the local patches attention (LPA) module, and the model-level fusion model. The feature extraction module… More >

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Zhongqing Zhang¹, Bo Wan^1,*, Guicui Fu¹, Yutai Su^2,*, Zhaoxi Wu³, Xiangfen Wang¹, Xu Long²
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 441-458, 2024, DOI:10.32604/cmes.2023.043616
Abstract Sintered silver nanoparticles (AgNPs) are widely used in high-power electronics due to their exceptional properties. However, the material reliability is significantly affected by various microscopic defects. In this work, the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified, categorized, and quantified. Molecular dynamics (MD) simulations are employed to observe the failure evolution of different microscopic defects. The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion. At the same time, this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by… More >

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Xuanjie Li, Yuedong Xu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 459-481, 2024, DOI:10.32604/cmes.2023.043247
Abstract We are investigating the distributed optimization problem, where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions. Since nodes exchange optimization parameters through the wireless network, large-scale training models can create communication bottlenecks, resulting in slower training times. To address this issue, CHOCO-SGD was proposed, which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions. Nevertheless, most convex functions are not strongly convex (such as logistic regression or Lasso), which raises the question of whether this algorithm can be applied to… More >

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Sicheng Zhu, Dingli Liu^*, Weijun Liu, Ying Li, Tian Zhou
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 483-513, 2024, DOI:10.32604/cmes.2023.044809
Abstract In recent years, frequent fire disasters have led to enormous damage in China. Effective firefighting rescues can minimize the losses caused by fires. During the rescue processes, the travel time of fire trucks can be severely affected by traffic conditions, changing the effective coverage of fire stations. However, it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions. This paper addresses this issue by combining the traveling time calculation model with the effective coverage simulation model. In addition, it proposes a new index of total effective coverage area (TECA) based on the time-weighted average… More >

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Cheng Han^1,*, Zhengguang Xu^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464
Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables. Since pattern category variables have… More >

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Yuchen Zhou¹, Hongtao Huo¹, Zhiwen Hou¹, Lingbin Bu¹, Yifan Wang¹, Jingyi Mao¹, Xiaojun Lv², Fanliang Bu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 537-563, 2024, DOI:10.32604/cmes.2023.044895
Abstract Graph Convolutional Neural Networks (GCNs) have been widely used in various fields due to their powerful capabilities in processing graph-structured data. However, GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions, resulting in substantial distortions. Moreover, most of the existing GCN models are shallow structures, which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures. To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations, we propose… More >

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Shubin Zheng^1,3, Yue Wang², Liming Li^2,3,*, Xieqi Chen^2,3, Lele Peng^2,3, Zhanhao Shang²
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 565-592, 2024, DOI:10.32604/cmes.2023.043832
（This article belongs to the Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
Abstract Regular fastener detection is necessary to ensure the safety of railways. However, the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways. Existing supervised inspection methods have insufficient detection ability in cases of imbalanced samples. To solve this problem, we propose an approach based on deep convolutional neural networks (DCNNs), which consists of three stages: fastener localization, abnormal fastener sample generation based on saliency detection, and fastener state inspection. First, a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions. Then, the foreground clip region of a fastener image… More >

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Nilkanth Mukund Deshpande^1,2, Shilpa Gite^3,4,*, Biswajeet Pradhan^5,6, Abdullah Alamri⁷, Chang-Wook Lee^8,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704
（This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More >

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Nadeem Ahmed^1,*, Khalid Mohammadani², Ali Kashif Bashir^3,4,5, Marwan Omar⁶, Angel Jones⁷, Fayaz Hassan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 633-659, 2024, DOI:10.32604/cmes.2023.031342
（This article belongs to the Special Issue: Cooperative ML/DL and Intelligent Networks towards Vehicle Health Monitoring System (VHMS))
Abstract Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles (IoV). However, intricate security challenges are intertwined with technological progress: Vehicular ad hoc Networks (VANETs), a core component of IoV, face security issues, particularly the Black Hole Attack (BHA). This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability; also, BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether. Recognizing the importance of this challenge, we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor (AODV-RL). The significance of… More >

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Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255
（This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

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Hong Qian^1,2, Haixin Wang^1,*, Guangji Wang³, Qingyun Yan⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 679-698, 2024, DOI:10.32604/cmes.2023.043377
（This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the analytic network process with subjective… More >

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Congjun Rao¹, Mengxi Li¹, Tingting Huang^2,*, Feiyu Li¹
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898
（This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

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Puja S. Prasad¹, Adepu Sree Lakshmi¹, Sandeep Kautish², Simar Preet Singh³, Rajesh Kumar Shrivastava³, Abdulaziz S. Almazyad⁴, Hossam M. Zawbaa⁵, Ali Wagdy Mohamed^6,7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 725-739, 2024, DOI:10.32604/cmes.2023.030640
（This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
Abstract Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities. The proposed framework consists of two-phase methodologies. In the first phase, the pupil’s diameter is calculated by applying stimulus (light) in one eye… More >

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Juhong Tie, Hui Peng^*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 741-757, 2024, DOI:10.32604/cmes.2023.043353
（This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
Abstract Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different level parallel sub-networks to fully… More >

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Raveena Selvanarayanan¹, Surendran Rajendran^1,*, Youseef Alotaibi²
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 759-782, 2024, DOI:10.32604/cmes.2023.044084
（This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
Abstract Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions. To overcome the existing challenge,… More >

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Lejun Zhang^1,2,3,*, Junjie Zhang¹, Kentaroh Toyoda⁴, Yuan Liu², Jing Qiu², Zhihong Tian², Ran Guo⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 783-800, 2024, DOI:10.32604/cmes.2023.043469
（This article belongs to the Special Issue: The Bottleneck of Blockchain Techniques: Scalability, Security and Privacy Protection)
Abstract Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant… More >

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D. Dorathy Prema Kavitha¹, L. Francis Raj¹, Sandeep Kautish^2,#, Abdulaziz S. Almazyad³, Karam M. Sallam⁴, Ali Wagdy Mohamed^5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902
（This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional… More >

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Long Chen¹, Yan Yu¹, Yanpeng Shang¹, Zhonghou Wang^1,*, Jing Zhang²
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 817-846, 2024, DOI:10.32604/cmes.2023.031595
（This article belongs to the Special Issue: Structural Design and Optimization)
Abstract Gears are pivotal in mechanical drives, and gear contact analysis is a typically difficult problem to solve. Emerging isogeometric analysis (IGA) methods have developed new ideas to solve this problem. In this paper, a three-dimensional body parametric gear model of IGA is established, and a theoretical formula is derived to realize single-tooth contact analysis. Results were benchmarked against those obtained from commercial software utilizing the finite element analysis (FEA) method to validate the accuracy of our approach. Our findings indicate that the IGA-based contact algorithm successfully met the Hertz contact test. When juxtaposed with the FEA approach, the IGA method… More >

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Chuang Lu¹, Leilei Chen^2,3, Jinling Luo⁴, Haibo Chen^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 847-872, 2024, DOI:10.32604/cmes.2023.044446
（This article belongs to the Special Issue: Structural Design and Optimization)
Abstract We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces. The existing structural optimization methods mainly contain shape and topology schemes, with the former changing the surface geometric profile of the structure and the latter changing the material distribution topology or hole topology of the structure. In the present acoustic performance optimization, the coordinates of the control points in the subdivision surfaces fine mesh are selected as the shape design parameters of the structure, the artificial density of the sound absorbing material covered on the… More >

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Canghong Jin^1,*, Jiapeng Chen¹, Shuyu Wu¹, Hao Wu², Shuoping Wang¹, Jing Ying³
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230
（This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

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Yu Li, Mingxiao Li, Dongyang Ou^*, Junjie Guo, Fangyuan Pan
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350
（This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple network models. However, advanced mining… More >

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Yue Mei^1,2,3, Jianwei Deng^1,2, Dongmei Zhao^1,2, Changjiang Xiao^1,2, Tianhang Wang⁴, Li Dong⁵, Xuefeng Zhu^1,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 911-935, 2024, DOI:10.32604/cmes.2023.043810
（This article belongs to the Special Issue: Machine Learning Based Computational Mechanics)
Abstract Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate the proposed method. The reconstructed… More >

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Peiheng He, Jiye Zhang^*, Lan Zhang, Jiaqi Wang, Yuzhe Ma
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 937-953, 2024, DOI:10.32604/cmes.2023.043700
（This article belongs to the Special Issue: Computer Modeling in Vehicle Aerodynamics)
Abstract The running stability of high-speed train is largely constrained by the wheel-rail coupling relationship, and the continuous wear between the wheel and rail surfaces will profoundly affect the dynamic performance of the train. In recent years, under the background of increasing train speed, some scientific researchers have proposed a new idea of using the lift force generated by the aerodynamic wings (aero-wing) installed on the roof to reduce the sprung load of the carriage in order to alleviate the wear and tear of the wheel and rail. Based on the bidirectional running characteristics of high-speed train, this paper proposes a… More >

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Huihui Zhai¹, Dongqi Jiao², Haichao Zhou^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 955-975, 2024, DOI:10.32604/cmes.2023.043322
（This article belongs to the Special Issue: Computer Modeling in Vehicle Aerodynamics)
Abstract The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25% of the total drag on modern vehicles. In this study, the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated. Subsequently, the 35° Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction. The Fluent software is employed for this investigation, with an inlet velocity of 40 m/s. The accuracy of the numerical study is validated by comparing it with experimental results obtained from the classical Ahmed model.… More >

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Mustufa Haider Abidi^*, Hisham Alkhalefah, Mohamed K. Aboudaif
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169
（This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical records of patients. Once an… More >

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Pingfan Li, Jiumei Xiao^*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 999-1016, 2024, DOI:10.32604/cmes.2023.044059
（This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)
Abstract Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties. However, issues such as high initial stress and low energy-absorbing efficiency arise. This study proposes a novel energy-absorbing structure in which a straight tube is combined with a conical tube and a bamboo-inspired bulkhead structure is introduced. This configuration allows the conical tube to flip outward first and then fold together with the straight tube. This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward. Through finite element numerical simulation, the specific energy absorption capacity of the… More >

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Siti Nur Ainsyah Ghani¹, Noor Fadiya Mohd Noor^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1017-1037, 2024, DOI:10.32604/cmes.2023.031372
（This article belongs to the Special Issue: Numerical Modeling and Simulations on Non-Newtonian Flow Problems)
Abstract Variant graphene, graphene oxides (GO), and graphene nanoplatelets (GNP) dispersed in blood-based copper (Cu) nanoliquids over a leaning permeable cylinder are the focus of this study. These forms of graphene are highly beneficial in the biological and medical fields for cancer therapy, anti-infection measures, and drug delivery. The non-Newtonian Sutterby (blood-based) hybrid nanoliquid flows are generalized within the context of the Tiwari-Das model to simulate the effects of radiation and heating sources. The governing partial differential equations are reformulated into a nonlinear set of ordinary differential equations using similar transformational expressions. These equations are then transformed into boundary value problems… More >

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Elif Varol Altay, Osman Altay, Yusuf Özçevik^*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1039-1094, 2024, DOI:10.32604/cmes.2023.029404
（This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
Abstract Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale… More >

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Mani Parimala¹, Karthikeyan Prakash¹, Ashraf Al-Quran^2,*, Muhammad Riaz³, Saeid Jafari⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1095-1118, 2024, DOI:10.32604/cmes.2023.031193
（This article belongs to the Special Issue: Control Systems and Machine Learning for Intelligent Computing)
Abstract The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and optimization under uncertainty. The activity times in the critical path method (CPM) representation procedures approach are initially static, but in the Project Evaluation and Review Technique (PERT) approach, they are probabilistic. This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy (LDF) environment. The LDF expected task time, LDF variance, LDF critical path, and LDF total expected time for determining the project network are all computed using LDF… More >

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Shupeng Qiu, Chujin Lin, Wei Zhao^*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1119-1134, 2024, DOI:10.32604/cmes.2023.030915
（This article belongs to the Special Issue: New Trends on Meshless Method and Numerical Analysis)
Abstract In this paper, we consider the Chan–Vese (C-V) model for image segmentation and obtain its numerical solution accurately and efficiently. For this purpose, we present a local radial basis function method based on a Gaussian kernel (GA-LRBF) for spatial discretization. Compared to the standard radial basis function method, this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain. Additionally, since the Gaussian function has the property of dimensional separation, the GA-LRBF method is suitable for dealing with isotropic images. Finally, a numerical scheme that couples GA-LRBF… More >

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