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Yiming Tang^1,2, Jianwei Gao^1,*, Yifan Huang¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1063-1088, 2024, DOI:10.32604/cmc.2024.049341
（This article belongs to the Special Issue: Emerging Trends in Fuzzy Logic)
Abstract The α-universal triple I (α-UTI) method is a recognized scheme in the field of fuzzy reasoning, which was proposed by our research group previously. The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent, which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference. Therefore, in this study, the interval robustness (embodied as the interval stability) of the α-UTI method is explored in the interval-valued fuzzy environment. To begin with, the stability of the α-UTI method is explored for the case of an individual rule,… More >

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Wuyan Liang¹, Xiaolong Xu^2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >

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Lin Wang¹, Juan Zhao², Hu Song³, Xiaolong Xu^4,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688
（This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable… More >

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Anas W. Abulfaraj^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1137-1156, 2024, DOI:10.32604/cmc.2024.047945
（This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
Abstract The utilization of visual attention enhances the performance of image classification tasks. Previous attention-based models have demonstrated notable performance, but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences. Neural-Controlled Differential Equations (N-CDE’s) and Neural Ordinary Differential Equations (NODE’s) are extensively utilized within this context. N-CDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity. To this end, an attentive neural network has been proposed to generate attention maps, which uses two different types of N-CDE’s, one for adopting hidden layers and the other to generate… More >

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Sapiah Sakri¹, Shakila Basheer¹, Zuhaira Muhammad Zain¹, Nurul Halimatul Asmak Ismail^2,*, Dua’ Abdellatef Nassar¹, Manal Abdullah Alohali¹, Mais Ayman Alharaki¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051
Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes… More >

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Jiaxiang Wang¹, Zhengyi Li¹, Peng Shi¹, Hongying Yu², Dongbai Sun^1,3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1187-1204, 2024, DOI:10.32604/cmc.2024.046929
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet is meticulously tailored to address the blurring in images capturing the microstructure of materials.… More >

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Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman¹, Taiki Watanabe¹, Issei Jozume¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1205-1222, 2024, DOI:10.32604/cmc.2024.046954
Abstract Person identification is one of the most vital tasks for network security. People are more concerned about their security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints and faces have been widely used for person identification, which has the risk of information leakage as a result of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person using vision and sensor-based techniques. In existing studies, most of the researchers used very… More >

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Yuancong Chai, Yuefei Zhu^*, Wei Lin, Ding Li
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904
Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >

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Yuyuan Li^1,2, Lang Li^1,2,*, Yu Ou^1,2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1245-1262, 2024, DOI:10.32604/cmc.2024.049882
Abstract The Inner Product Masking (IPM) scheme has been shown to provide higher theoretical security guarantees than the Boolean Masking (BM). This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security. Some previous work unfolds when certain (adversarial and implementation) conditions are met, and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour. In this paper, we investigate the security characteristics of IPM under different conditions. In adversarial condition, the security properties of first-order IPMs obtained through parametric characterization are preserved in the face… More >

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Yakui Liu^1,2,3,*, Xing Jiang¹, Ruikang Xu¹, Yihao Cui¹, Chenhui Yu¹, Jingqi Yang¹, Jishuai Zhou¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1263-1279, 2024, DOI:10.32604/cmc.2024.048864
Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to… More >

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Jun Wang^1,2, Linxi Zhang^1,2, Hao Zhang¹, Funan Peng^1,*, Mohammed A. El-Meligy³, Mohamed Sharaf³, Qiang Fu¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1281-1299, 2024, DOI:10.32604/cmc.2024.048495
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based… More >

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Sharaf J. Malebary^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917
（This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

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Chunbin Qin^*, Xiaotian Ran
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1319-1334, 2024, DOI:10.32604/cmc.2024.048850
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lacking unique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenes severely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deep learning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheral computing devices. To address these challenges, this study proposes a novel unsupervised image stitching method based on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networks and attention mechanisms. The methodology is partitioned into three distinct… More >

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Xue Zhao, Shaojun Tao, Hongying Tang, Jiang Wang^*, Baoqing Li^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1335-1351, 2024, DOI:10.32604/cmc.2024.047996
Abstract In recent years, target tracking has been considered one of the most important applications of wireless sensor network (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally critical objectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. The proposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH) election, pre-selection, and task set selection mechanisms, where the latter two kinds of selections form a two-layer selection mechanism. The CH election innovatively introduces the movement trend of the target and establishes a scoring mechanism to determine the optimal CH, which can… More >

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Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small scales to capture global contextual… More >

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Yuhang Wang^1,*, Zhiqin He¹, Qinmu Wu¹, Tingsheng Lu², Yu Tang¹, Maoyun Zhu¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379
Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and the residual connection between the… More >

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Jinlin Xu^1,2, Wansu Pan^1,*, Longle Cheng^1,2, Haibo Tan^1,2, Munan Yuan^1,*, Xiaofeng Li^1,2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1399-1418, 2024, DOI:10.32604/cmc.2024.049622
Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation of service requirements and network resources. To address these issues, we propose a multi-constraint path optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters and constructs a composite quality… More >

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Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883
Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >

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Chunming Wu¹, Wukai Liu^2,*, Xin Ma³
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1441-1461, 2024, DOI:10.32604/cmc.2024.048136
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… More >

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Kazim Jawad¹, Muhammad Ahmad², Majdah Alvi³, Muhammad Bux Alvi^3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1463-1480, 2024, DOI:10.32604/cmc.2024.047466
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu… More >

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Zezheng Meng, Zefeng Cai, Jie Feng^*, Hanjie Ma, Haixiang Zhang, Shaohua Li
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The… More >

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Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >

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Xiaohui Yang, Peiyin Zhao^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1521-1538, 2024, DOI:10.32604/cmc.2024.047666
Abstract Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer from privacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharing scheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computational overhead of encryption and decryption reaches a lightweight constant level, and supports keyword search and policy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technology is utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the data to achieve controllability of… More >

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Fayez Alfayez^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1539-1560, 2024, DOI:10.32604/cmc.2024.046443
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three picture segmentation algorithms and three… More >

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Linlin Yuan^1,2, Tiantian Zhang^1,3, Yuling Chen^1,*, Yuxiang Yang¹, Huang Li¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a… More >

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Emmanuel Oluwatobi Asani^1,2,3, Aderemi Elisha Okeyinka⁴, Sunday Adeola Ajagbe^5,6, Ayodele Ariyo Adebiyi¹, Roseline Oluwaseun Ogundokun^1,2,7,*, Temitope Samson Adekunle⁸, Pragasen Mudali⁵, Matthew Olusegun Adigun⁵
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1581-1597, 2024, DOI:10.32604/cmc.2024.047898
Abstract The study presents the Half Max Insertion Heuristic (HMIH) as a novel approach to solving the Travelling Salesman Problem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) and Nearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics, focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative that minimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting with an initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half… More >

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Tianshu Wei¹, Jinjie Huang^1,2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1599-1612, 2024, DOI:10.32604/cmc.2024.048098
Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can further enhance the consistency between… More >

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Qiuyang Wang, Zhen Xu^*, Lei Yang
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1613-1630, 2024, DOI:10.32604/cmc.2024.047915
Abstract Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm (CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors. Two MCs… More >

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Maria Gul¹, Imran Ali Khan¹, Gohar Zaman², Atta Rahman^3,*, Jamaluddin Mir², Sardar Asad Ali Biabani^4,5, May Issa Aldossary⁶, Mustafa Youldash⁷, Ashraf Saadeldeen⁸, Maqsood Mahmud⁹, Asiya Abdus Salam⁶, Dania Alkhulaifi³, Abdullah AlTurkey³
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1631-1657, 2024, DOI:10.32604/cmc.2024.048289
Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically, etc. The proposed work presents… More >

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Zhanfeng Wang¹, Lisha Yao^2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1659-1677, 2024, DOI:10.32604/cmc.2024.048304
Abstract Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images, which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomes these limitations by vectorizing information through increased directionality and magnitude, ensuring that spatial information is not overlooked. Therefore, this study proposes a novel expression recognition technique called CAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining and integrating features extracted by a convolutional neural network before introducing them into CapsNet, our model enhances facial recognition capabilities. Compared to traditional neural network models, our… More >

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Dingping Chen¹, Zhiheng Zhu², Jinyang Fu^1,3, Jilin He^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1679-1703, 2024, DOI:10.32604/cmc.2024.049048
Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >

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Aodi Liu, Na Wang^*, Xuehui Du, Dibin Shan, Xiangyu Wu, Wenjuan Wang
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1705-1726, 2024, DOI:10.32604/cmc.2024.049011
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy management efficiency and difficulty in accurately describing the access control policy. To overcome these problems, this paper proposes a big data access control mechanism based on a two-layer permission decision structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are introduced in the ABAC model as business constraints between entities. The proposed mechanism implements a two-layer permission decision structure composed of the inherent attributes of… More >

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Parth Khandelwal¹, Harshit², Indranil Manna^1,3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1727-1755, 2024, DOI:10.32604/cmc.2024.042752
（This article belongs to the Special Issue: Application of Soft Computing in Techniques in Materials Development)
Abstract Metallic alloys for a given application are usually designed to achieve the desired properties by devising experiments based on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises. However, the influence of process parameters and material properties is often non-linear and non-colligative. In recent years, machine learning (ML) has emerged as a promising tool to deal with the complex interrelation between composition, properties, and process parameters to facilitate accelerated discovery and development of new alloys and functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles, to design novel copper alloys for achieving… More >

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