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ARTICLE

Ali Hamid Farea^1,*, Omar H. Alhazmi¹, Kerem Kucuk²
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1525-1545, 2024, DOI:10.32604/cmc.2023.045794
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to… More >

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Cong Jin¹, Jinjie Huang^1,2,*, Yuanjian Chen¹, Yuqing Gong¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1547-1568, 2024, DOI:10.32604/cmc.2023.047489
Abstract In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a more efficient architectural parameter regularization… More >

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ARTICLE

Huan Wang¹, Shi Qiu^1,2,*, Benyue Zhang¹, Lixuan Xiao³
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1569-1589, 2024, DOI:10.32604/cmc.2023.046821
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows:… More >

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Mustafa AlRifaee¹, Sally Almanasra^2,*, Adnan Hnaif³, Ahmad Althunibat³, Mohammad Abdallah³, Thamer Alrawashdeh³
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1591-1609, 2024, DOI:10.32604/cmc.2023.043520
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image… More >

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174

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ARTICLE

Hongliang Tian, Jiaming Wang^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1611-1629, 2024, DOI:10.32604/cmc.2023.046698
Abstract Blockchain, known for its secure encrypted ledger, has garnered attention in financial and data transfer realms, including the field of energy trading. However, the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions. The broadcast consensus authentication slows transaction speeds, and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing. To address these, an alliance blockchain scheme is proposed, reducing the resource-intensive identity verification among nodes. It integrates multi-signature functionality to fortify user resources and transaction security. A novel multi-signature process within this framework involves neutral nodes established through… More >

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170

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ARTICLE

Lanyao Zhang¹, Shichao Kan², Yigang Cen³, Xiaoling Chen¹, Linna Zhang^1,*, Yansen Huang^4,5
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924
Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample features to these two spaces… More >

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Yuchun Li^1,4, Mengxing Huang^1,*, Yu Zhang², Zhiming Bai³
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1649-1668, 2024, DOI:10.32604/cmc.2023.046883
Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the… More >

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125

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R. Sujatha, K. Nimala^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963
Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

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ARTICLE

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187
Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

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140

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ARTICLE

Gyu-Il Kim¹, Hyun Yoo², Han-Jin Cho³, Kyungyong Chung^4,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324
Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding Gaussian noise, with the noise… More >

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ARTICLE

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739
Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit logs, fits normal system entity… More >

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ARTICLE

Pengchao Li^1,2,3,*, Fang Xu^1,2,3,4, Jintao Wang^1,2, Haibing Guo⁴, Mingmin Liu⁴, Zhenjun Du⁴
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1755-1771, 2024, DOI:10.32604/cmc.2023.047057
Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a high-performance deep neural network algorithm… More >

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ARTICLE

Zhenhua Yu, Liangxue Bai, Ou Ye^*, Xuya Cong
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1773-1795, 2024, DOI:10.32604/cmc.2023.047130
Abstract Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social… More >

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119

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ARTICLE

Ziqi Wang^*, Weihan Tian, Baojiang Cui
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1797-1820, 2024, DOI:10.32604/cmc.2023.047051
Abstract The API used to access cloud services typically follows the Representational State Transfer (REST) architecture style. RESTful architecture, as a commonly used Application Programming Interface (API) architecture paradigm, not only brings convenience to platforms and tenants, but also brings logical security challenges. Security issues such as quota bypass and privilege escalation are closely related to the design and implementation of API logic. Traditional code level testing methods are difficult to construct a testing model for API logic and test samples for in-depth testing of API logic, making it difficult to detect such logical vulnerabilities. We propose RESTlogic for this purpose.… More >

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ARTICLE

Di Zhou¹, Jianxun Zhang^1,*, Chao Li², Yifan Guo¹, Bowen Li¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1821-1842, 2024, DOI:10.32604/cmc.2023.047236
Abstract Text perception is crucial for understanding the semantics of outdoor scenes, making it a key requirement for building intelligent systems for driver assistance or autonomous driving. Text information in car-mounted videos can assist drivers in making decisions. However, Car-mounted video text images pose challenges such as complex backgrounds, small fonts, and the need for real-time detection. We proposed a robust Car-mounted Video Text Detector (CVTD). It is a lightweight text detection model based on ResNet18 for feature extraction, capable of detecting text in arbitrary shapes. Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation (CATA) and… More >

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ARTICLE

Xiaohui Yang, Kun Zhang^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1843-1859, 2024, DOI:10.32604/cmc.2023.047660
Abstract Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,… More >

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139

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ARTICLE

Zhenhui Li¹, Shuangping Xing¹, Lin Yu¹, Huiping Li¹, Fan Zhou¹, Guangqiang Yin¹, Xikai Tang², Zhiguo Wang^1,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1861-1879, 2024, DOI:10.32604/cmc.2023.046595
Abstract Software security analysts typically only have access to the executable program and cannot directly access the source code of the program. This poses significant challenges to security analysis. While it is crucial to identify vulnerabilities in such non-source code programs, there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods. However, these tools suffer from some shortcomings. In terms of targeted fuzzing, the path searching for target points is not streamlined enough, and the completely random testing leads to an excessively large search space. Additionally, when it comes to code similarity analysis,… More >

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129

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ARTICLE

Xiao Feng^1,2,3,*, Zheng Yuan¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1881-1895, 2024, DOI:10.32604/cmc.2023.047243
Abstract First, we propose a cross-domain authentication architecture based on trust evaluation mechanism, including registration, certificate issuance, and cross-domain authentication processes. A direct trust evaluation mechanism based on the time decay factor is proposed, taking into account the influence of historical interaction records. We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data. We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record. Then we propose an autoencoder-based trust clustering algorithm. We perform feature… More >

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143

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ARTICLE

Sang-min Lee, Namgi Kim^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1897-1914, 2024, DOI:10.32604/cmc.2023.046346
Abstract Recommendation Information Systems (RIS) are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet. Graph Convolution Network (GCN) algorithms have been employed to implement the RIS efficiently. However, the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process. To address this issue, we propose a Weighted Forwarding method using the GCN (WF-GCN) algorithm. The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning. By applying the WF-GCN… More >

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ARTICLE

Yuecheng Yu^1,*, Liming Cai¹, Anqi Ning¹, Jinlong Shi¹, Xudong Chen², Shixin Huang¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1915-1931, 2024, DOI:10.32604/cmc.2024.046125
（This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract Under the influence of air humidity, dust, aerosols, etc., in real scenes, haze presents an uneven state. In this way, the image quality and contrast will decrease. In this case, It is difficult to detect the target in the image by the universal detection network. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed in this paper. Firstly, in the training phase, the Gated Context Aggregation Network (GCANet) is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes. In the test phase, only the YOLOX branch needs to be… More >

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ARTICLE

Bashar Alshouha¹, Jesus Serrano-Guerrero^1,*, Francisco Chiclana², Francisco P. Romero¹, Jose A. Olivas¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1933-1956, 2024, DOI:10.32604/cmc.2023.046711
（This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
Abstract Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains, including education, e-commerce, or human resources. Traditional machine learning techniques have been broadly employed for personality trait identification; nevertheless, the development of new technologies based on deep learning has led to new opportunities to improve their performance. This study focuses on the capabilities of pre-trained language models such as BERT, RoBERTa, ALBERT, ELECTRA, ERNIE, or XLNet, to deal with the task of personality recognition. These models are able to capture structural features from textual content and comprehend a multitude… More >

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ARTICLE

Liya Yue¹, Pei Hu², Shu-Chuan Chu³, Jeng-Shyang Pan^3,4,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1957-1975, 2024, DOI:10.32604/cmc.2024.046962
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract Speech emotion recognition (SER) uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions. The number of features acquired with acoustic analysis is extremely high, so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system. The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy. First, we use the information gain and Fisher Score to sort the features extracted from signals. Then, we employ a multi-objective ranking method to evaluate these features and… More >

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146

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ARTICLE

Zhiqing Dong¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1977-1993, 2024, DOI:10.32604/cmc.2024.047233
Abstract With the advent of the information security era, it is necessary to guarantee the privacy, accuracy, and dependable transfer of pictures. This study presents a new approach to the encryption and compression of color images. It is predicated on 2D compressed sensing (CS) and the hyperchaotic system. First, an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security. Then, the processed images are concurrently encrypted and compressed using 2D CS. Among them, chaotic sequences replace traditional random measurement matrices to increase the system’s security. Third, the processed images are re-encrypted using a combination of… More >

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141

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ARTICLE

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478
Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >

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ARTICLE

Nader Abdel Karim^1,*, Osama A. Khashan^2,*, Waleed K. Abdulraheem³, Moutaz Alazab¹, Hasan Kanaker⁴, Mahmoud E. Farfoura⁵, Mohammad Alshinwan^5,6
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2023-2045, 2024, DOI:10.32604/cmc.2023.044304
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract As the extensive use of cloud computing raises questions about the security of any personal data stored there, cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment. A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware. The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment. An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance; Each hypervisor should be examined to meet… More >

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Muhammad Rizwan^1,2, Muhammad Faheem Mushtaq¹, Maryam Rafiq², Arif Mehmood³, Isabel de la Torre Diez⁴, Monica Gracia Villar^5,6,7, Helena Garay^5,8,9, Imran Ashraf^10,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2047-2066, 2024, DOI:10.32604/cmc.2024.037347
Abstract Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter… More >

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ARTICLE

Sherly Alphonse^*, Harshit Verma
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2067-2086, 2024, DOI:10.32604/cmc.2024.046070
Abstract Although lots of research has been done in recognizing facial expressions, there is still a need to increase the accuracy of facial expression recognition, particularly under uncontrolled situations. The use of Local Directional Patterns (LDP), which has good characteristics for emotion detection has yielded encouraging results. An innovative end-to-end learnable High Response-based Local Directional Pattern (HR-LDP) network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work. By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions, this network considerably minimizes the number of network… More >

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REVIEW

Shahriar Md Arman¹, Tao Yang^1,*, Shahadat Shahed², Alanoud Al Mazroa³, Afraa Attiah⁴, Linda Mohaisen⁴
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2087-2110, 2024, DOI:10.32604/cmc.2024.047870
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people’s biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field’s existing literature. We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric… More >

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Lieping Zhang^1,2, Xiaoxu Shi^1,2, Liu Tang^1,2, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou^4,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2111-2136, 2024, DOI:10.32604/cmc.2024.047235
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping (SLAM) algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes’ deflection probability into the random sampling function so that the global search tree can detect frontier… More >

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S. Jayanthi^*, S. Prasanna Devi
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975
Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate our system, we have rigorously… More >

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Jinyang Yu^1,2, Xiao Zhang^1,2,3,*, Jinjiang Wang^1,2, Yuchen Zhang^1,2, Yulong Shi^1,2, Linxuan Su^1,2, Leijie Zeng^1,2,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2159-2179, 2024, DOI:10.32604/cmc.2024.047340
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The proliferation of Internet of Things (IoT) systems has resulted in the generation of substantial data, presenting new challenges in reliable storage and trustworthy sharing. Conventional distributed storage systems are hindered by centralized management and lack traceability, while blockchain systems are limited by low capacity and high latency. To address these challenges, the present study investigates the reliable storage and trustworthy sharing of IoT data, and presents a novel system architecture that integrates on-chain and off-chain data manage systems. This architecture, integrating blockchain and distributed storage technologies, provides high-capacity, high-performance, traceable, and verifiable data storage and access. The on-chain system,… More >

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Thanh-Lam Nguyen¹, Hao Kao¹, Thanh-Tuan Nguyen², Mong-Fong Horng^1,*, Chin-Shiuh Shieh^1,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2181-2205, 2024, DOI:10.32604/cmc.2024.047387
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract Since its inception, the Internet has been rapidly evolving. With the advancement of science and technology and the explosive growth of the population, the demand for the Internet has been on the rise. Many applications in education, healthcare, entertainment, science, and more are being increasingly deployed based on the internet. Concurrently, malicious threats on the internet are on the rise as well. Distributed Denial of Service (DDoS) attacks are among the most common and dangerous threats on the internet today. The scale and complexity of DDoS attacks are constantly growing. Intrusion Detection Systems (IDS) have been deployed and have demonstrated… More >

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ARTICLE

Afnan M. Alhassan^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf… More >

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ARTICLE

Yanjun Yu¹, Lei Yu^1,*, Huiqi Wang², Haodong Zheng¹, Yi Deng¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2225-2243, 2024, DOI:10.32604/cmc.2024.047641
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Bone age assessment (BAA) helps doctors determine how a child’s bones grow and develop in clinical medicine. Traditional BAA methods rely on clinician expertise, leading to time-consuming predictions and inaccurate results. Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations. This operation is costly and subjective. To address these problems, we propose a multi-scale attentional densely connected network (MSADCN) in this paper. MSADCN constructs a multi-scale dense connectivity mechanism, which can avoid overfitting, obtain the local features effectively and prevent gradient vanishing even in limited training data. First, MSADCN designs… More >

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Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323
Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a… More >

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S. Abinaya^*, K. Uttej Kumar
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167
Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users increases. To triumph over the… More >

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Shiyan Zhang^1,*, Ruohan Xu², Zhangbo Xu³, Cenhua Yu¹, Yuyang Jiang¹, Yuting Zhao⁴
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2287-2308, 2024, DOI:10.32604/cmc.2023.046550
（This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
Abstract The 6th generation mobile networks (6G) network is a kind of multi-network interconnection and multi-scenario coexistence network, where multiple network domains break the original fixed boundaries to form connections and convergence. In this paper, with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness, this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration. Due to the conflict between the utility of different flows, the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions. Regarding the tradeoff between… More >

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Huixiang Xu^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253
Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting all this data to a… More >

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Zhaolin Lv¹, Yuexia Zhao², Hongyue Kang^3,*, Zhenyu Gao³, Yuhang Qin⁴
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2337-2360, 2024, DOI:10.32604/cmc.2023.045826
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract Flexible job shop scheduling problem (FJSP) is the core decision-making problem of intelligent manufacturing production management. The Harris hawk optimization (HHO) algorithm, as a typical metaheuristic algorithm, has been widely employed to solve scheduling problems. However, HHO suffers from premature convergence when solving NP-hard problems. Therefore, this paper proposes an improved HHO algorithm (GNHHO) to solve the FJSP. GNHHO introduces an elitism strategy, a chaotic mechanism, a nonlinear escaping energy update strategy, and a Gaussian random walk strategy to prevent premature convergence. A flexible job shop scheduling model is constructed, and the static and dynamic FJSP is investigated to minimize… More >

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Cai Ming Liu^1,2,3, Yan Zhang^1,2,*, Zhihui Hu^1,2, Chunming Xie¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2361-2389, 2024, DOI:10.32604/cmc.2023.045282
（This article belongs to the Special Issue: Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments )
Abstract Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods. This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method. The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements. Then, to improve the accuracy of similarity calculation, a quantitative matching method is proposed. The model uses mathematical methods to train and evolve immune elements, increasing the diversity of… More >

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Ruchun Jia, Jianwei Zhang^*, Yi Lin
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through privacy, integrity, and availability. The… More >

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Moona Kanwal^1,2,*, Najeed A. Khan¹, Aftab A. Khan³
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2419-2440, 2024, DOI:10.32604/cmc.2024.047223
Abstract The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types… More >

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Vaishnawi Priyadarshni¹, Sanjay Kumar Sharma¹, Mohammad Khalid Imam Rahmani^2,*, Baijnath Kaushik³, Rania Almajalid^2,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963
Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion… More >

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Yan Li, Qiyuan Wang^*, Kaidi Jia
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

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Longle Cheng^1,2, Xiru Li¹, Shiyu Fang², Wansu Pan¹, He Zhao^1,*, Haibo Tan¹, Xiaofeng Li^1,2
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2491-2509, 2024, DOI:10.32604/cmc.2024.047166
Abstract Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely… More >

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Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

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Saba Awan^1,*, Zahid Mehmood^2,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic… More >

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Dayu Xu^1,#, Jiaming Lü^1,#, Xuyao Zhang², Hongtao Zhang^1,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2557-2573, 2024, DOI:10.32604/cmc.2024.045932
Abstract Data stream clustering is integral to contemporary big data applications. However, addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research. This paper aims to elevate the efficiency and precision of data stream clustering, leveraging the TEDA (Typicality and Eccentricity Data Analysis) algorithm as a foundation, we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm. The original TEDA algorithm, grounded in the concept of “Typicality and Eccentricity Data Analytics”, represents an evolving and recursive method that requires no prior knowledge. While the… More >

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Junde Chen¹, Jiajia Yuan², Weirong Chen³, Adnan Zeb⁴, Md Suzauddola⁵, Yaser A. Nanehkaran^2,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2575-2591, 2024, DOI:10.32604/cmc.2024.048522
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Missing value is one of the main factors that cause dirty data. Without high-quality data, there will be no reliable analysis results and precise decision-making. Therefore, the data warehouse needs to integrate high-quality data consistently. In the power system, the electricity consumption data of some large users cannot be normally collected resulting in missing data, which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate. For the problem of missing electricity consumption data, this study proposes a group method of data handling (GMDH) based data interpolation method in distribution… More >

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Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2593-2616, 2024, DOI:10.32604/cmc.2024.046304
Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence… More >

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