Open Access
ARTICLE
Meijing Li*, Runqing Huang, Xianxian Qi
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Mohammed Kaddi1,*, Mohammed Omari2, Moamen Alnatoor1
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Yousef Ibrahim Daradkeh1,*, Volodymyr Gorokhovatskyi2, Iryna Tvoroshenko2,*, Medien Zeghid1,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051709
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors. The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency. The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations. It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.… More >
Open Access
ARTICLE
Baoxia Li1, Wenzhuo Chen1, Xiaojiang Tang1, Shaohuang Bian1, Yang Liu2, Junwei Guo2, Dan Zhang2, Feng Huang2,*
CMC-Computers, Materials & Continua, 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 >
Open Access
REVIEW
Wufei Wu1, Javad Hassannataj Joloudari2,3,4, Senthil Kumar Jagatheesaperumal5, Kandala N. V. P. S. Rajesh6, Silvia Gaftandzhieva7,*, Sadiq Hussain8, Rahimullah Rabih9, Najibullah Haqjoo10, Mobeen Nazar11, Hamed Vahdat-Nejad9, Rositsa Doneva12
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Rong Wang1, Xinxin Xu2, Zijia Wang3,*, Fei Ji1, Nankun Mu4
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Xiaoli Yuan, Jianxun Zhang*, Xuejie Wang, Zhuhong Chu
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Mofadal Alymani1, Mohamed Esmail Karar2,*, Hazem Ibrahim Shehata1,3
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Abdulraqeb Alhammadi1,*, Zaid Ahmed Shamsan2, Arijit De3
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Mesut Ulu1,*, Yusuf Sait Türkan2, Kenan Mengüç3, Ersin Namlı2, Tarık Küçükdeniz2
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Zaed Mahdi1,*, Nada Abdalhussien2, Naba Mahmood1, Rana Zaki3,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Abdulrahman Alzahrani*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Shi Qiu1, Hongbing Lu1,*, Jun Shu2, Ting Liang3, Tao Zhou4
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Wen Jiang1,2, Mingshu Zhang1,2,*, Xu'an Wang1,3, Wei Bin1,2, Xiong Zhang1,2, Kelan Ren1,2, Facheng Yan1,2
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Jing Wang1,2,*, Chen Zhang1, Tianwen Lin1
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Gonghui Deng, Dunzhi Wu, Weizhen Chen*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Jing Zhang*, Guangli Wu, Shanshan Song
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Atif Mehmood1,2, Zhonglong Zheng1,*, Rizwan Khan1, Ahmad Al Smadi3, Farah Shahid1,2, Shahid Iqbal4, Mutasem K. Alsmadi5, Yazeed Yasin Ghadi6, Syed Aziz Shah8, Mostafa M. Ibrahim7
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Huanhuan Li1,2,*, Hongchang Wei2, Zheliang Chen2, Yue Xu3
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Tareq Hamadneh1, Khalid Kaabneh2, Omar Alssayed3, Gulnara Bektemyssova4,*, Galymzhan Shaikemelev4, Dauren Umutkulov4, Zoubida Benmamoun5, Zeinab Monrazeri6, Mohammad Dehghani6,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Shibin Zhao*, Junhu Zhu, Jianshan Peng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.041949
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract In recent years, the rapid development of computer software has led to numerous security problems, particularly software vulnerabilities. These flaws can cause significant harm to users’ privacy and property. Current security defect detection technology relies on manual or professional reasoning, leading to missed detection and high false detection rates. Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes, reducing missed alarms and false alarms. So, this project aims to study Java source code defect detection methods for defects like null pointer… More >
Open Access
ARTICLE
Guodong Han, Hecheng Li*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Muhammad Ayoub Kamal1,3, Muhammad Mansoor Alam1,2,4,6, Aznida Abu Bakar Sajak1, Mazliham Mohd Su’ud2,5,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Zhongshang Chen, Ji Feng*, Fapeng Cai, Degang Yang
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Dayong Wang1,*, Kamalrulnizam Bin Abu Bakar1, Babangida Isyaku2
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Shujing Li, Zhangfei Li, Wenhui Cheng, Chenyang Qi, Linguo Li*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Yanhua Liu1,2,3, Yuting Han1,2,3, Hui Chen1,2,3, Baokang Zhao4,*, Xiaofeng Wang4, Ximeng Liu1,2,3
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Yu Zhou1, Yun Zhang1, Guowei Li1, Hang Yang1, Wei Zhang1, Ting Lyu2, Yueqiang Xu2,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Tajmal Hussain, Jungpyo Hong*, Jongwon Seok*
CMC-Computers, Materials & Continua, 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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