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Zheyi Chen^1,, Liuchang Xu^1,, Hongting Zheng¹, Luyao Chen¹, Amr Tolba^2,3, Liang Zhao⁴, Keping Yu^5,*, Hailin Feng^1,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1753-1808, 2024, DOI:10.32604/cmc.2024.052618
Abstract Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread… More >

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Yu Zhou¹, Yun Zhang¹, Guowei Li¹, Hang Yang¹, Wei Zhang¹, Ting Lyu², Yueqiang Xu^2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1809-1829, 2024, DOI:10.32604/cmc.2024.050975
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract In current research on task offloading and resource scheduling in vehicular networks, vehicles are commonly assumed to maintain constant speed or relatively stationary states, and the impact of speed variations on task offloading is often overlooked. It is frequently assumed that vehicles can be accurately modeled during actual motion processes. However, in vehicular dynamic environments, both the tasks generated by the vehicles and the vehicles’ surroundings are constantly changing, making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios. Taking into account the actual dynamic vehicular scenarios, this paper considers the real-time… More >

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Hina Gul¹, Sana Ullah¹, Ki-Il Kim^2,*, Farman Ali³
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1831-1850, 2024, DOI:10.32604/cmc.2024.052841
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super… More >

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Yanhua Liu^1,2,3, Yuting Han^1,2,3, Hui Chen^1,2,3, Baokang Zhao^4,*, Xiaofeng Wang⁴, Ximeng Liu^1,2,3
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1851-1866, 2024, DOI:10.32604/cmc.2024.051194
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network’s generalization capabilities. Experimental results show that the proposed method can efficiently distinguish More >

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Qussai M. Yaseen^1,2,*, Monther Aldwairi^2,3, Ahmad Manasrah^4,5
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1867-1888, 2024, DOI:10.32604/cmc.2024.051179
Abstract Enhancing the security of Wireless Sensor Networks (WSNs) improves the usability of their applications. Therefore, finding solutions to various attacks, such as the blackhole attack, is crucial for the success of WSN applications. This paper proposes an enhanced version of the AODV (Ad Hoc On-Demand Distance Vector) protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs, thereby avoiding them when delivering packets. The proposed version employs a network-based reputation system to select the best and most secure path to a destination. To achieve this goal, the proposed version utilizes the Watchdogs/Pathrater mechanisms in… More >

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Abdulraqeb Alhammadi^1,*, Zaid Ahmed Shamsan², Arijit De³
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1889-1905, 2024, DOI:10.32604/cmc.2024.051487
Abstract Indoor localization systems are crucial in addressing the limitations of traditional global positioning system (GPS) in indoor environments due to signal attenuation issues. As complex indoor spaces become more sophisticated, indoor localization systems become essential for improving user experience, safety, and operational efficiency. Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database, but this can increase the computational burden in the online phase. Bayesian networks, which integrate prior knowledge or domain expertise, are an effective solution for accurately determining indoor user locations. These networks use probabilistic reasoning to model relationships among… More >

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Jing Wang^1,2,*, Chen Zhang¹, Tianwen Lin¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1907-1925, 2024, DOI:10.32604/cmc.2024.052597
Abstract When existing deep learning models are used for road extraction tasks from high-resolution images, they are easily affected by noise factors such as tree and building occlusion and complex backgrounds, resulting in incomplete road extraction and low accuracy. We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt. Then, ConvNeXt is used as the backbone network, which cooperates with the perceptual analysis network UPerNet, retains the detection head of the semantic segmentation, and builds a new model ConvNeXt-UPerNet to suppress noise interference. Training on the open-source DeepGlobe and CHN6-CUG… More >

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Muhammad Ayoub Kamal^1,3, Muhammad Mansoor Alam^1,2,4,6, Aznida Abu Bakar Sajak¹, Mazliham Mohd Su’ud^2,5,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1927-1945, 2024, DOI:10.32604/cmc.2024.052169
Abstract In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The More >

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Jing Zhang^*, Guangli Wu, Shanshan Song
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1947-1965, 2024, DOI:10.32604/cmc.2024.051781
Abstract Video summarization aims to select key frames or key shots to create summaries for fast retrieval, compression, and efficient browsing of videos. Graph neural networks efficiently capture information about graph nodes and their neighbors, but ignore the dynamic dependencies between nodes. To address this challenge, we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network (TAMGCN), leveraging the attention mechanism to dynamically adjust dependencies between graph nodes. Specifically, we first segment shots and extract features of each frame, then compute the representative features of each shot. Subsequently, we utilize the attention mechanism to dynamically adjust More >

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Qiuyan Wang, Haibing Dong^*, Yongfei Huang, Zenglei Liu, Yundong Gou
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

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Gonghui Deng, Dunzhi Wu, Weizhen Chen^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1985-2003, 2024, DOI:10.32604/cmc.2024.052174
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at… More >

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Tareq Hamadneh¹, Khalid Kaabneh², Omar Alssayed³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Zoubida Benmamoun⁵, Zeinab Monrazeri⁶, Mohammad Dehghani^6,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2005-2030, 2024, DOI:10.32604/cmc.2024.052401
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is… More >

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ARTICLE

Zhongshang Chen, Ji Feng^*, Fapeng Cai, Degang Yang
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2031-2048, 2024, DOI:10.32604/cmc.2024.052114
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract In clustering algorithms, the selection of neighbors significantly affects the quality of the final clustering results. While various neighbor relationships exist, such as K-nearest neighbors, natural neighbors, and shared neighbors, most neighbor relationships can only handle single structural relationships, and the identification accuracy is low for datasets with multiple structures. In life, people’s first instinct for complex things is to divide them into multiple parts to complete. Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures. Taking inspiration from this, we propose a novel neighbor method: Shared Natural Neighbors (SNaN). More >

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Shujing Li, Zhangfei Li, Wenhui Cheng, Chenyang Qi, Linguo Li^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2049-2063, 2024, DOI:10.32604/cmc.2024.051928
Abstract To enhance the diversity and distribution uniformity of initial population, as well as to avoid local extrema in the Chimp Optimization Algorithm (CHOA), this paper improves the CHOA based on chaos initialization and Cauchy mutation. First, Sin chaos is introduced to improve the random population initialization scheme of the CHOA, which not only guarantees the diversity of the population, but also enhances the distribution uniformity of the initial population. Next, Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position (threshold) updating to avoid the CHOA falling More >

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ARTICLE

Dayong Wang^1,*, Kamalrulnizam Bin Abu Bakar¹, Babangida Isyaku²
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2065-2080, 2024, DOI:10.32604/cmc.2024.051944
（This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
Abstract The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,… More >

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ARTICLE

Atif Mehmood^1,2, Zhonglong Zheng^1,*, Rizwan Khan¹, Ahmad Al Smadi³, Farah Shahid^1,2, Shahid Iqbal⁴, Mutasem K. Alsmadi⁵, Yazeed Yasin Ghadi⁶, Syed Aziz Shah⁸, Mostafa M. Ibrahim⁷
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869
Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >

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Tajmal Hussain, Jungpyo Hong^*, Jongwon Seok^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2099-2119, 2024, DOI:10.32604/cmc.2024.050884
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things (IoT) and artificial intelligence (AI). Quality control is an important part of today’s smart manufacturing process, effectively reducing costs and enhancing operational efficiency. As technology in the industry becomes more advanced, identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process. In this study, we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques, incorporating a global… More >

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91

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Guodong Han, Hecheng Li^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2121-2137, 2024, DOI:10.32604/cmc.2024.053077
（This article belongs to the Special Issue: Trustworthy Wireless Computing Power Networks Assisted by Blockchain)
Abstract Currently, there is a growing trend among users to store their data in the cloud. However, the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks. Additionally, when users perform file operations, the semantic integrity of the data can be compromised. Ensuring both data integrity and semantic correctness has become a critical issue that requires attention. We introduce a pioneering solution called Sec-Auditor, the first of its kind with the ability to verify data integrity and semantic correctness simultaneously, while maintaining a constant communication cost independent of the audited… More >

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Zaed Mahdi^1,*, Nada Abdalhussien², Naba Mahmood¹, Rana Zaki^3,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2139-2159, 2024, DOI:10.32604/cmc.2024.053542
Abstract The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems… More >

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Wen Jiang^1,2, Mingshu Zhang^1,2,*, Xu'an Wang^1,3, Wei Bin^1,2, Xiong Zhang^1,2, Kelan Ren^1,2, Facheng Yan^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2161-2179, 2024, DOI:10.32604/cmc.2024.053762
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different… More >

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Mofadal Alymani¹, Mohamed Esmail Karar^2,*, Hazem Ibrahim Shehata^1,3
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2181-2197, 2024, DOI:10.32604/cmc.2024.052406
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed… More >

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Huanhuan Li^1,2,*, Hongchang Wei², Zheliang Chen², Yue Xu³
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2199-2219, 2024, DOI:10.32604/cmc.2024.052155
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges, such as low user utilization, unbalanced resource allocation, and extended adaptive allocation time. We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues. This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components. It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes. Furthermore, this study analyzes the single-hop and multi-hop modes in… More >

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ARTICLE

Baoxia Li¹, Wenzhuo Chen¹, Xiaojiang Tang¹, Shaohuang Bian¹, Yang Liu², Junwei Guo², Dan Zhang², Feng Huang^2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2221-2236, 2024, DOI:10.32604/cmc.2024.049862
Abstract Complex plasma widely exists in thin film deposition, material surface modification, and waste gas treatment in industrial plasma processes. During complex plasma discharge, the configuration, distribution, and size of particles, as well as the discharge glow, strongly depend on discharge parameters. However, traditional manual diagnosis methods for recognizing discharge parameters from discharge images are complicated to operate with low accuracy, time-consuming and high requirement of instruments. To solve these problems, by combining the two mechanisms of attention mechanism (strengthening the extraction of the channel feature) and shortcut connection (enabling the input information to be directly… More >

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Nisreen Innab^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2237-2258, 2024, DOI:10.32604/cmc.2024.049608
（This article belongs to the Special Issue: Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments )
Abstract Internet of Health Things (IoHT) is a subset of Internet of Things (IoT) technology that includes interconnected medical devices and sensors used in medical and healthcare information systems. However, IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication. In this article, we intend to address this shortcoming, and as a result, we propose a new scheme called, the certificateless anonymous authentication (CAA) scheme. The proposed scheme is based on hyperelliptic curve cryptography (HECC), an enhanced variant of elliptic curve cryptography (ECC)… More >

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ARTICLE

Mesut Ulu^1,*, Yusuf Sait Türkan², Kenan Mengüç³, Ersin Namlı², Tarık Küçükdeniz²
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2259-2281, 2024, DOI:10.32604/cmc.2024.052323
Abstract Today, urban traffic, growing populations, and dense transportation networks are contributing to an increase in traffic incidents. These incidents include traffic accidents, vehicle breakdowns, fires, and traffic disputes, resulting in long waiting times, high carbon emissions, and other undesirable situations. It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities. This study presents a model for forecasting the traffic incident duration of traffic events with high precision. The proposed model goes through a 4-stage process using various features to predict the… More >

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Meijing Li^*, Runqing Huang, Xianxian Qi
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2283-2299, 2024, DOI:10.32604/cmc.2024.053630
Abstract Chinese Clinical Named Entity Recognition (CNER) is a crucial step in extracting medical information and is of great significance in promoting medical informatization. However, CNER poses challenges due to the specificity of clinical terminology, the complexity of Chinese text semantics, and the uncertainty of Chinese entity boundaries. To address these issues, we propose an improved CNER model, which is based on multi-feature fusion and multi-scale local context enhancement. The model simultaneously fuses multi-feature representations of pinyin, radical, Part of Speech (POS), word boundary with BERT deep contextual representations to enhance the semantic representation of text… More >

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Uddagiri Sirisha^1,, Parvathaneni Naga Srinivasu^2,3,*, Panguluri Padmavathi⁴, Seongki Kim^5,, Aruna Pavate⁶, Jana Shafi⁷, Muhammad Fazal Ijaz^8,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2301-2330, 2024, DOI:10.32604/cmc.2024.053132
（This article belongs to the Special Issue: Deep Learning and IoT for Smart Healthcare)
Abstract Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus’ growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable.… More >

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Abdulrahman Alzahrani^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2331-2349, 2024, DOI:10.32604/cmc.2024.052804
Abstract The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets.… More >

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Yan Sun, Caiyun Liu, Jun Li, Yitong Liu^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2351-2362, 2024, DOI:10.32604/cmc.2024.048903
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract With the development of technology, the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal. The data of ICV (intelligent connected vehicles) is the key to organically maximizing their efficiency. However, in the context of increasingly strict global data security supervision and compliance, numerous problems, including complex types of connected vehicle data, poor data collaboration between the IT (information technology) domain and OT (operation technology) domain, different data format standards, lack of shared trust sources, difficulty in ensuring the quality of shared data, lack of data… More >

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Rong Wang¹, Xinxin Xu², Zijia Wang^3,*, Fei Ji¹, Nankun Mu⁴
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2363-2385, 2024, DOI:10.32604/cmc.2024.053564
Abstract Marine container terminal (MCT) plays a key role in the marine intelligent transportation system and international logistics system. However, the efficiency of resource scheduling significantly influences the operation performance of MCT. To solve the practical resource scheduling problem (RSP) in MCT efficiently, this paper has contributions to both the problem model and the algorithm design. Firstly, in the problem model, different from most of the existing studies that only consider scheduling part of the resources in MCT, we propose a unified mathematical model for formulating an integrated RSP. The new integrated RSP model allocates and… More >

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REVIEW

Zia Ullah^1,2,*, Lin Qi¹, E. J. Solteiro Pires², Arsénio Reis², Ricardo Rodrigues Nunes²
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2387-2421, 2024, DOI:10.32604/cmc.2024.047572
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way… More >

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S. Vidivelli^*, Manikandan Ramachandran^*, A. Dharunbalaji
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2423-2442, 2024, DOI:10.32604/cmc.2024.054360
Abstract This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on proficiency close by viability. We accomplish this by joining three key innovations: LangChain, Retrieval Augmented Generation (RAG), and enormous language models (LLMs) tweaked with execution proficient strategies like LoRA and QLoRA. LangChain takes into consideration fastidious fitting of chatbots to explicit purposes, guaranteeing engaged and important collaborations with clients. RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data, empowering them to give exhaustive and enlightening reactions to requests. This recovered data is then decisively woven into… More >

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Xiaoli Yuan, Jianxun Zhang^*, Xuejie Wang, Zhuhong Chu
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2443-2462, 2024, DOI:10.32604/cmc.2024.052285
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract Semantic segmentation of driving scene images is crucial for autonomous driving. While deep learning technology has significantly improved daytime image semantic segmentation, nighttime images pose challenges due to factors like poor lighting and overexposure, making it difficult to recognize small objects. To address this, we propose an Image Adaptive Enhancement (IAEN) module comprising a parameter predictor (Edip), multiple image processing filters (Mdif), and a Detail Processing Module (DPM). Edip combines image processing filters to predict parameters like exposure and hue, optimizing image quality. We adopt a novel image encoder to enhance parameter prediction accuracy by More >

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REVIEW

Abdullah Alenizi¹, Mohammad Sajid Mohammadi², Ahmad A. Al-Hajji², Arshiya Sajid Ansari^1,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2463-2494, 2024, DOI:10.32604/cmc.2024.051826
Abstract Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present, the security of secret or vital information has always been a significant problem. The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest. Therefore, several approaches, including steganography, have been developed by researchers over time to enable safe data transit. In this review, we have discussed image steganography based on Discrete Cosine Transform (DCT) algorithm, etc. We have also discussed image steganography based… More >

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Shi Qiu¹, Hongbing Lu^1,*, Jun Shu², Ting Liang³, Tao Zhou⁴
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2495-2510, 2024, DOI:10.32604/cmc.2024.052476
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Colorectal cancer, a malignant lesion of the intestines, significantly affects human health and life, emphasizing the necessity of early detection and treatment. Accurate segmentation of colorectal cancer regions directly impacts subsequent staging, treatment methods, and prognostic outcomes. While colonoscopy is an effective method for detecting colorectal cancer, its data collection approach can cause patient discomfort. To address this, current research utilizes Computed Tomography (CT) imaging; however, conventional CT images only capture transient states, lacking sufficient representational capability to precisely locate colorectal cancer. This study utilizes enhanced CT images, constructing a deep feature network from the… More >

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Ahmad Rahdari^1,6, Ahmad Jalili², Mehdi Esnaashari³, Mehdi Gheisari^1,4,7,8,*, Alisa A. Vorobeva⁵, Zhaoxi Fang¹, Panjun Sun^1,*, Viktoriia M. Korzhuk⁵, Ilya Popov⁵, Zongda Wu¹, Hamid Tahaei¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.052994
Abstract Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes.… More >

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Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Ming Li⁴, Xianyu Wang², Cong Li²
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.052166
（This article belongs to the Special Issue: Exploring the Metaverse: Advancements in Future Networks Communication and Computing Technologies for Enhanced Quality of Experience)
Abstract When learning the structure of a Bayesian network, the search space expands significantly as the network size and the number of nodes increase, leading to a noticeable decrease in algorithm efficiency. Traditional constraint-based methods typically rely on the results of conditional independence tests. However, excessive reliance on these test results can lead to a series of problems, including increased computational complexity and inaccurate results, especially when dealing with large-scale networks where performance bottlenecks are particularly evident. To overcome these challenges, we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected… More >

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Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634
Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >

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Yangrong Chen^1,2, June Li^3,*, Yu Xia³, Ruiwen Zhang³, Lingling Li^1,2, Xiaoyu Li^1,2, Lin Ge^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2579-2609, 2024, DOI:10.32604/cmc.2024.053230
（This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)
Abstract Intelligent electronic devices (IEDs) are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions. In the context of the heightened security challenges within smart grids, IEDs pose significant risks due to inherent hardware and software vulnerabilities, as well as the openness and vulnerability of communication protocols. Smart grid security, distinct from traditional internet security, mainly relies on monitoring network security events at the platform layer, lacking an effective assessment mechanism for IEDs. Hence, we incorporate considerations for both cyber-attacks and physical faults, presenting security assessment indicators and… More >

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Mohammed Kaddi^1,*, Mohammed Omari², Moamen Alnatoor¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2611-2631, 2024, DOI:10.32604/cmc.2024.052048
Abstract Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks (WSN). A sensor network comprises several micro-sensors deployed randomly in an area of interest. A micro-sensor is provided with an energy resource to supply electricity to all of its components. However, the disposed energy resource is limited and battery replacement is generally infeasible. With this restriction, the sensors must conserve energy to prolong their lifetime. Various energy conservation strategies for WSNs have been presented in the literature, from the application to the physical layer. Most of these solutions focus only on… More >

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Md. Mahbubur Rahman Tusher¹, Fahmid Al Farid^2,*, Md. Al-Hasan¹, Abu Saleh Musa Miah¹, Susmita Roy Rinky¹, Mehedi Hasan Jim¹, Sarina Mansor², Md. Abdur Rahim³, Hezerul Abdul Karim^2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2633-2656, 2024, DOI:10.32604/cmc.2024.049296
Abstract The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name… More >

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Chengyu Wang, Xionglve Li, Tongqing Zhou, Zhiping Cai^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2657-2673, 2024, DOI:10.32604/cmc.2024.054061
Abstract Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD,… More >

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Singamaneni Krishnapriya^*, Sukhvinder Singh
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2675-2719, 2024, DOI:10.32604/cmc.2024.052447
Abstract The increase in number of people using the Internet leads to increased cyberattack opportunities. Advanced Persistent Threats, or APTs, are among the most dangerous targeted cyberattacks. APT attacks utilize various advanced tools and techniques for attacking targets with specific goals. Even countries with advanced technologies, like the US, Russia, the UK, and India, are susceptible to this targeted attack. APT is a sophisticated attack that involves multiple stages and specific strategies. Besides, TTP (Tools, Techniques, and Procedures) involved in the APT attack are commonly new and developed by an attacker to evade the security system.… More >

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Tareq Hamadneh¹, Khalid Kaabneh², Ibraheem AbuFalahah³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Sayan Omarov⁵, Zeinab Monrazeri⁶, Frank Werner⁷, Mohammad Dehghani^6,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2721-2741, 2024, DOI:10.32604/cmc.2024.054317
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird’s… More >

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Abdallah Ghourabi^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163
Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

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Nannan Chong^1,2,*, Fan Yang¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2761-2783, 2024, DOI:10.32604/cmc.2024.052217
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract In minimally invasive surgery, endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities. However, in clinical operating environments, endoscopic images often suffer from challenges such as low texture, uneven illumination, and non-rigid structures, which affect feature observation and extraction. This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images, leading to treatment and postoperative recovery issues for patients. To address these challenges, this paper introduces, for the first time, a Cross-Channel Multi-Modal… More >

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Wufei Wu¹, Javad Hassannataj Joloudari^2,3,4, Senthil Kumar Jagatheesaperumal⁵, Kandala N. V. P. S. Rajesh⁶, Silvia Gaftandzhieva^7,*, Sadiq Hussain⁸, Rahimullah Rabih⁹, Najibullah Haqjoo¹⁰, Mobeen Nazar¹¹, Hamed Vahdat-Nejad⁹, Rositsa Doneva¹²
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2785-2813, 2024, DOI:10.32604/cmc.2024.053037
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional advantages of IoV include online communication services, accident prevention, cost reduction, and enhanced traffic regularity. Despite these benefits, IoV technology is susceptible to cyber-attacks, which can exploit vulnerabilities in the vehicle network, leading to perturbations, disturbances, non-recognition of traffic signs, accidents, and vehicle immobilization. This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning (DTL) models for Intrusion Detection Systems in the Internet of Vehicles (IDS-IoV) based on anomaly… More >

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Mengmeng Zhao^1,2,3, Haipeng Peng^1,2,*, Lixiang Li^1,2, Yeqing Ren^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2815-2837, 2024, DOI:10.32604/cmc.2024.053765
Abstract In the Industrial Internet of Things (IIoT), sensors generate time series data to reflect the working state. When the systems are attacked, timely identification of outliers in time series is critical to ensure security. Although many anomaly detection methods have been proposed, the temporal correlation of the time series over the same sensor and the state (spatial) correlation between different sensors are rarely considered simultaneously in these methods. Owing to the superior capability of Transformer in learning time series features. This paper proposes a time series anomaly detection method based on a spatial-temporal network and… More >

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Jalil Abbas^1,*, Arshad Ahmad², Syed Muqsit Shaheed³, Rubia Fatima⁴, Sajid Shah⁵, Mohammad Elaffendi⁵, Gauhar Ali⁵
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2839-2855, 2024, DOI:10.32604/cmc.2024.052218
（This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
Abstract The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the… More >

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Chun-Ming Wu¹, Mei-Ling Ren^2,*, Jin Lei², Zi-Mu Jiang³
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2857-2872, 2024, DOI:10.32604/cmc.2024.053708
（This article belongs to the Special Issue: Artificial Intelligence Driven Innovations in Integrating Communications, Image and Signal Processing Applications)
Abstract To address the issues of incomplete information, blurred details, loss of details, and insufficient contrast in infrared and visible image fusion, an image fusion algorithm based on a convolutional autoencoder is proposed. The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map. A multi-scale convolution attention module is suggested to enhance the communication of feature information. At the same time, the feature transformation module is introduced to learn more robust feature representations, aiming to preserve the integrity of… More >

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