Open Access
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2023 Impact Factor 2.0; Scopus CiteScore (Impact per Publication 2023): 5.3; SNIP (Source Normalized Impact per Paper 2023): 0.73; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
REVIEW
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 >
Open Access
ARTICLE
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 >
Open Access
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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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
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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 >
Open Access
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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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
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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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
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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 >
Open Access
REVIEW
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 >
Open Access
ARTICLE
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 >
Open Access
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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 >
Open Access
REVIEW
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 >
Open Access
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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 >
Open Access
REVIEW
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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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 >
Graphic Abstract
Open Access
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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 >
Open Access
ARTICLE
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 >
Open Access
REVIEW
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 >
Open Access
REVIEW
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
REVIEW
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2873-2895, 2024, DOI:10.32604/cmc.2024.052571
（This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract Four typical theories on the formation of thermal tears: strength, liquid film, intergranular bridging, and solidification shrinkage compensation theories. From these theories, a number of criteria have been derived for predicting the formation of thermal cracks, such as the stress-based Niyama, Clyne, and RDG (Rapaz-Dreiser-Grimaud) criteria. In this paper, a mathematical model of horizontal centrifugal casting was established, and numerical simulation analysis was conducted for the centrifugal casting process of cylindrical Al-Cu alloy castings to investigate the effect of the centrifugal casting process conditions on the microstructure and hot tearing sensitivity of alloy castings by… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2897-2915, 2024, DOI:10.32604/cmc.2024.052570
（This article belongs to the Special Issue: Trustworthy Wireless Computing Power Networks Assisted by Blockchain)
Abstract As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835
（This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2979-3000, 2024, DOI:10.32604/cmc.2024.053627
Abstract Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3001-3019, 2024, DOI:10.32604/cmc.2024.053369
Abstract With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3021-3045, 2024, DOI:10.32604/cmc.2024.053632
Abstract Massive computational complexity and memory requirement of artificial intelligence models impede their deployability on edge computing devices of the Internet of Things (IoT). While Power-of-Two (PoT) quantization is proposed to improve the efficiency for edge inference of Deep Neural Networks (DNNs), existing PoT schemes require a huge amount of bit-wise manipulation and have large memory overhead, and their efficiency is bounded by the bottleneck of computation latency and memory footprint. To tackle this challenge, we present an efficient inference approach on the basis of PoT quantization and model compression. An integer-only scalar PoT quantization (IOS-PoT)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.053680
Abstract The widespread availability of digital multimedia data has led to a new challenge in digital forensics. Traditional source camera identification algorithms usually rely on various traces in the capturing process. However, these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms. Convolutional Neural Networks (CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices. However, their performances is not ideal in case of distinguishing between individual devices of the same model, because cameras of the same model typically use the same… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3085-3106, 2024, 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
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600
Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599
Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3139-3171, 2024, DOI:10.32604/cmc.2024.050834
Abstract Image steganography is one of the prominent technologies in data hiding standards. Steganographic system performance mostly depends on the embedding strategy. Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image. The randomization strategies in data embedding techniques may utilize random domains, pixels, or region-of-interest for concealing secrets into a cover image, preventing information from being discovered by an attacker. The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility, but it is challenging. A systematic approach is More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3173-3192, 2024, DOI:10.32604/cmc.2024.051705
Abstract As image manipulation technology advances rapidly, the malicious use of image tampering has alarmingly escalated, posing a significant threat to social stability. In the realm of image tampering localization, accurately localizing limited samples, multiple types, and various sizes of regions remains a multitude of challenges. These issues impede the model’s universality and generalization capability and detrimentally affect its performance. To tackle these issues, we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization. Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain, and captures richer traces of tampering… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3193-3219, 2024, DOI:10.32604/cmc.2024.054377
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488
Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3243-3261, 2024, DOI:10.32604/cmc.2024.051710
Abstract In this paper, considering the cost of base station, coverage, call quality, and other practical factors, a multi-objective optimal site planning scheme is proposed. Firstly, based on practical needs, mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives, coverage objectives, and quality objectives. Then, a multi-objective optimization model was established by combining threshold and traffic volume constraints. In order to reduce the time complexity of optimization, a non-dominated sorting genetic algorithm (NSGA) is used to solve the multi-objective optimization problem of site planning. Finally, a strategy for clustering… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3263-3287, 2024, 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
REVIEW
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893
Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3315-3332, 2024, DOI:10.32604/cmc.2024.052582
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management. However, vehicles come in a range of sizes, which is challenging to detect, affecting the traffic monitoring system’s overall accuracy. Deep learning is considered to be an efficient method for object detection in vision-based systems. In this paper, we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5 (YOLOv5) detector combined with a segmentation technique. The model consists of six steps. In the first step, all the extracted traffic sequence images are subjected… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3333-3349, 2024, DOI:10.32604/cmc.2024.050544
Abstract In the rapidly evolving urban landscape, outdoor parking lots have become an indispensable part of the city’s transportation system. The growth of parking lots has raised the likelihood of spontaneous vehicle combustion, a significant safety hazard, making smoke detection an essential preventative step. However, the complex environment of outdoor parking lots presents additional challenges for smoke detection, which necessitates the development of more advanced and reliable smoke detection technologies. This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots. First, we develop a novel… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3351-3377, 2024, DOI:10.32604/cmc.2024.051574
Abstract The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3379-3403, 2024, DOI:10.32604/cmc.2024.054337
Abstract This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method (DEM) simulation, design of experiments (DOE), and metaheuristic optimization algorithms. Specifically, the study employs the firefly algorithm (FA), a metaheuristic optimization technique, to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions. The experimental methodology involves several key steps: screening experiments to identify significant factors affecting bucket elevator operation, central composite design (CCD) experiments to further explore these factors, and response surface methodology (RSM)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3405-3420, 2024, DOI:10.32604/cmc.2024.052144
Abstract Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions. The NeRF (Neural Radiance Fields) algorithm, suitable for underwater scenes or scattering media, is also evolving. Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency. This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction. First, we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium… More >
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