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REVIEW

Arshiya S. Ansari^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2865-2892, 2024, DOI:10.32604/cmc.2024.045908 - 26 March 2024
Abstract Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look. Whereas vehicular ad hoc networks (VANETs), which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services, as they are an essential component of modern smart transportation systems. VANETs steganography has been suggested by many authors for secure, reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection. This paper aims to determine whether using steganography is… More >

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Jongwook Si¹, Sungyoung Kim^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2893-2908, 2024, DOI:10.32604/cmc.2023.046081 - 26 March 2024
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In the context of high compression rates applied to Joint Photographic Experts Group (JPEG) images through lossy compression techniques, image-blocking artifacts may manifest. This necessitates the restoration of the image to its original quality. The challenge lies in regenerating significantly compressed images into a state in which these become identifiable. Therefore, this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks (GAN). The generator in this network is based on the U-Net architecture. It features a new hourglass structure that preserves the characteristics… More >

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1072

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385

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Jiadong Wu, Yinan Wang^*, Zhiwei Li^*, Lun Lu, Qingjiang Li
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240 - 26 March 2024
Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance… More >

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529

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REVIEW

Ekanayake Mudiyanselage Chulabhaya Lankanatha Ekanayake^1,2, Abubakar Sulaiman Gezawa^3,*, Yunqi Lei¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2941-2965, 2024, DOI:10.32604/cmc.2024.046155 - 26 March 2024
Abstract Video description generates natural language sentences that describe the subject, verb, and objects of the targeted Video. The video description has been used to help visually impaired people to understand the content. It is also playing an essential role in devolving human-robot interaction. The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping. Deep learning is changing the shape of computer vision (CV) technologies and natural language processing (NLP). There are hundreds of deep learning models, datasets, and evaluations that can improve the gaps… More >

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319

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REVIEW

Usman Khan¹, Muhammad Khalid Khan¹, Muhammad Ayub Latif¹, Muhammad Naveed^1,2,*, Muhammad Mansoor Alam^2,3,4, Salman A. Khan¹, Mazliham Mohd Su’ud^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101 - 26 March 2024
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and… More >

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628

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Nannan Wu¹, Xianyi Chen^1,*, James Msughter Adeke², Junjie Zhao²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3001-3019, 2024, DOI:10.32604/cmc.2023.045311 - 26 March 2024
Abstract Recently, deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding. However, these approaches have some limitations. For example, a cover image lacks self-adaptability, information leakage, or weak concealment. To address these issues, this study proposes a universal and adaptable image-hiding method. First, a domain attention mechanism is designed by combining the Atrous convolution, which makes better use of the relationship between the secret image domain and the cover image domain. Second, to improve perceived human similarity, perceptual loss is incorporated into the training process. The experimental results are promising, with More >

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378

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Shoukun Xu¹, Lujun Zhang¹, Guangqi Jiang¹, Yining Hua², Yi Liu^1,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853 - 26 March 2024
Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

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2

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Yuejie Li^1,2,*, Shijun Li³
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3041-3070, 2024, DOI:10.32604/cmc.2023.045741 - 26 March 2024
（This article belongs to the Special Issue: Secure Blockchain Clustering for 6G Wireless Technology with Advanced Artificial Intelligence Techniques)
Abstract 6G is envisioned as the next generation of wireless communication technology, promising unprecedented data speeds, ultra-low Latency, and ubiquitous Connectivity. In tandem with these advancements, blockchain technology is leveraged to enhance computer vision applications’ security, trustworthiness, and transparency. With the widespread use of mobile devices equipped with cameras, the ability to capture and recognize Chinese characters in natural scenes has become increasingly important. Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount, such as facial recognition or personal healthcare monitoring. Users can control their visual data and grant or revoke access as needed.… More >

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ARTICLE

Chun-Ming Wu¹, Jin Lei^1,*, Wu-Kai Liu¹, Mei-Ling Ren¹, Ling-Li Ran²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062 - 26 March 2024
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed… More >

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580

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Navaneetha Krishnan Muthunambu¹, Senthil Prabakaran², Balasubramanian Prabhu Kavin³, Kishore Senthil Siruvangur⁴, Kavitha Chinnadurai¹, Jehad Ali^5,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172 - 26 March 2024
Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent… More >

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Awais Ahmad¹, Sohail Jabbar^1,*, Sheeraz Akram¹, Anand Paul², Umar Raza³, Nuha Mohammed Alshuqayran¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385 - 26 March 2024
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to… More >

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998

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344

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Chanumolu Kiran Kumar, Nandhakumar Ramachandran^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3151-3176, 2024, DOI:10.32604/cmc.2023.043534 - 26 March 2024
（This article belongs to the Special Issue: Intelligent Technologies and Applications for Future Wireless Communications)
Abstract Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions, vulnerabilities, and assaults. Complex security systems, such as Intrusion Detection Systems (IDS), are essential due to the limitations of simpler security measures, such as cryptography and firewalls. Due to their compact nature and low energy reserves, wireless networks present a significant challenge for security procedures. The features of small cells can cause threats to the network. Network Coding (NC) enabled small cells are vulnerable to various types of attacks. Avoiding attacks and performing secure “peer” to “peer” data transmission is… More >

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1

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ARTICLE

Bin Wu^1,2, Xianyi Chen³, Jinzhou Huang^4,*, Caicai Zhang⁵, Jing Wang⁶, Jing Yu^1,2, Zhiqiang Zhao⁷, Zhuolin Mei^1,2
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3177-3194, 2024, DOI:10.32604/cmc.2023.047147 - 26 March 2024
Abstract In a cloud environment, outsourced graph data is widely used in companies, enterprises, medical institutions, and so on. Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers. Servers on cloud platforms usually have some subjective or objective attacks, which make the outsourced graph data in an insecure state. The issue of privacy data protection has become an important obstacle to data sharing and usage. How to query outsourcing graph data safely and effectively has become the focus of research. Adjacency query is a basic… More >

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658

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273

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ARTICLE

K. B. Ajeyprasaath, P. Vetrivelan^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3195-3213, 2024, DOI:10.32604/cmc.2023.046911 - 26 March 2024
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Video streaming applications have grown considerably in recent years. As a result, this becomes one of the most significant contributors to global internet traffic. According to recent studies, the telecommunications industry loses millions of dollars due to poor video Quality of Experience (QoE) for users. Among the standard proposals for standardizing the quality of video streaming over internet service providers (ISPs) is the Mean Opinion Score (MOS). However, the accurate finding of QoE by MOS is subjective and laborious, and it varies depending on the user. A fully automated data analytics framework is required to… More >

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313

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ARTICLE

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746 - 26 March 2024
Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues,… More >

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ARTICLE

Kai Chen^1,2, Jinwei Wang³, James Msughter Adeke^1,2, Guangjie Liu^1,2,*, Yuewei Dai^1,4
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082 - 26 March 2024
Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The… More >

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588

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259

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ARTICLE

Bin Li^*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193 - 26 March 2024
Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent… More >

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634

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278

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ARTICLE

Rafi Ullah^1,*, Mohd Hilmi bin Hasan¹, Sultan Daud Khan², Mussadiq Abdul Rahim³
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3283-3301, 2024, DOI:10.32604/cmc.2024.046305 - 26 March 2024
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Medical imaging plays a key role within modern hospital management systems for diagnostic purposes. Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed, all while upholding image quality. Moreover, an increasing number of hospitals are embracing cloud computing for patient data storage, necessitating meticulous scrutiny of server security and privacy protocols. Nevertheless, considering the widespread availability of multimedia tools, the preservation of digital data integrity surpasses the significance of compression alone. In response to this concern, we propose a secure storage and transmission solution for compressed medical image sequences, such as… More >

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273

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ARTICLE

Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, Muhammad Adnan Khan⁵, Muhammad Hasnain Azam⁶, Khan Muhammad Adnan^7,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3303-3321, 2024, DOI:10.32604/cmc.2024.048035 - 26 March 2024
Abstract Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being. In… More >

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954

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303

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ARTICLE

Umer Sadiq Khan^1,2,*, Zhen Liu^1,2,*, Fang Xu^1,2, Muhib Ullah Khan^3,4, Lerui Chen⁵, Touseef Ahmed Khan^4,6, Muhammad Kashif Khattak⁷, Yuquan Zhang⁸
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3323-3348, 2024, DOI:10.32604/cmc.2024.046094 - 26 March 2024
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering… More >

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672

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312

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Zihao Song, Yan Zhou^*, Wei Cheng, Futai Liang, Chenhao Zhang
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034 - 26 March 2024
Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.… More >

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360

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ARTICLE

Umar Rashid^1,2,*, Arfan Jaffar^1,2, Muhammad Rashid³, Mohammed S. Alshuhri⁴, Sheeraz Akram^1,4,5
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3377-3390, 2024, DOI:10.32604/cmc.2024.046320 - 26 March 2024
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as More >

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323

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Zhaoliang Wu¹, Yuewei Wu^1,2, Xiaoli Feng¹, Jiajun Zou³, Fulian Yin^1,2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3391-3412, 2024, DOI:10.32604/cmc.2024.047076 - 26 March 2024
Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately More >

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Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880 - 26 March 2024
Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the More >

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307

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Mahmood A. Mahmood^1,2,*, Khalaf Alsalem¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604 - 26 March 2024
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is… More >

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Sherly Alphonse^1,*, S. Abinaya¹, Sourabh Paul²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3449-3467, 2024, DOI:10.32604/cmc.2024.046403 - 26 March 2024
Abstract The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas. The radiation cost and quality factor of the antenna are influenced by the size of the antenna. Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas. Antenna parameters have recently been predicted using machine learning algorithms in existing literature. Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters. The accuracy of the prediction will be primarily dependent on the model that is used. In… More >

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358

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Sureka Sivavelu, Venkatesh Palanisamy^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407 - 26 March 2024
Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes… More >

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382

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Rashmi Sharma¹, Ashok Pal¹, Nitin Mittal², Lalit Kumar², Sreypov Van³, Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3489-3510, 2024, DOI:10.32604/cmc.2024.046606 - 26 March 2024
Abstract This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these… More >

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Talal Albalawi, P. Ganeshkumar^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396 - 26 March 2024
Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution More >

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283

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Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189 - 26 March 2024
Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing… More >

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Xianhua Li^1,2,*, Haohao Yu¹, Shuoyu Tian¹, Fengtao Lin³, Usama Masood¹
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3551-3564, 2024, DOI:10.32604/cmc.2024.047336 - 26 March 2024
（This article belongs to the Special Issue: Recognition Tasks with Transformers)
Abstract The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional (3D) method that takes into account self-occlusion, badly posedness, and a lack of depth data in the per-frame 3D posture estimation from two-dimensional (2D) mapping to 3D mapping. Firstly, by examining the relationship between the movements of different bones in the human body, four virtual skeletons are proposed to enhance the cyclic constraints of limb joints. Then, multiple parameters describing the skeleton are fused and projected into a high-dimensional space. Utilizing a multi-branch network, motion features between bones and overall motion More >

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Sameh Zarif^1,2,*, Eman Morad¹, Khalid Amin¹, Abdullah Alharbi³, Wail S. Elkilani⁴, Shouze Tang⁵
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185 - 26 March 2024
Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK… More >

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Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760 - 26 March 2024
Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal… More >

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Xiaohui Cui^1,2,#, Chao Song^1,2,#, Dongmei Li^1,2,*, Xiaolong Qu^1,2, Jiao Long^1,2, Yu Yang^1,2, Hanchao Zhang³
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321 - 26 March 2024
（This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

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Xuemin Wang, Qiaochen Li, Xuguang Bao^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3619-3643, 2024, DOI:10.32604/cmc.2024.047586 - 26 March 2024
Abstract Humans are experiencing the inclusion of artificial agents in their lives, such as unmanned vehicles, service robots, voice assistants, and intelligent medical care. If the artificial agents cannot align with social values or make ethical decisions, they may not meet the expectations of humans. Traditionally, an ethical decision-making framework is constructed by rule-based or statistical approaches. In this paper, we propose an ethical decision-making framework based on incremental ILP (Inductive Logic Programming), which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches. As the current incremental ILP makes it difficult to… More >

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Muhammad Binsawad^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3645-3663, 2024, DOI:10.32604/cmc.2024.048183 - 26 March 2024
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried… More >

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309

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Haoran Huang, Weiting Chen^*, Zheming Fan
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024
Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

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Asifa Mehmood Qureshi¹, Naif Al Mudawi², Mohammed Alonazi³, Samia Allaoua Chelloug⁴, Jeongmin Park^5,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611 - 26 March 2024
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with… More >

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ARTICLE

Xuelong Wu¹, Junsheng Wang^1,*, Zhongyao Li¹, Yisheng Miao¹, Chengpeng Xue¹, Yuling Lang², Decai Kong², Xiaoying Ma², Haibao Qiao²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027 - 26 March 2024
Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method More >

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Guojun Chen^*, Danguo Cheng, Wei Chen, Xue Yang, Tiezheng Guo
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3721-3741, 2024, DOI:10.32604/cmc.2024.047325 - 26 March 2024
Abstract With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to… More >

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Mengqi Zhang¹, Jiazhao Chai², Jianxiang Cao³, Jialing Ji³, Tong Yi^4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486 - 26 March 2024
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them More >

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Shu-Chuan Chu¹, Xiaomeng Yang¹, Li Zhang², Václav Snášel³, Jeng-Shyang Pan^1,4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3763-3786, 2024, DOI:10.32604/cmc.2024.048594 - 26 March 2024
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance; however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. More >

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Zhijian Liu^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998 - 26 March 2024
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction… More >

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486

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Shaha Al-Otaibi¹, Rahim Khan^2,*, Hashim Ali², Aftab Ahmed Khan², Amir Saeed³, Jehad Ali^4,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3805-3823, 2024, DOI:10.32604/cmc.2024.049017 - 26 March 2024
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract The Internet of Things (IoT) is a smart networking infrastructure of physical devices, i.e., things, that are embedded with sensors, actuators, software, and other technologies, to connect and share data with the respective server module. Although IoTs are cornerstones in different application domains, the device’s authenticity, i.e., of server(s) and ordinary devices, is the most crucial issue and must be resolved on a priority basis. Therefore, various field-proven methodologies were presented to streamline the verification process of the communicating devices; however, location-aware authentication has not been reported as per our knowledge, which is a crucial… More >

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REVIEW

Pengjun Li¹, Qixin Zhao¹, Yingmin Liu¹, Chao Zhong¹, Jinlong Wang^1,*, Zhihan Lyu²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3825-3865, 2024, DOI:10.32604/cmc.2024.046851 - 26 March 2024
Abstract Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the… More >

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Dahyeon Kim¹, Namgi Kim², Junho Ahn^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3867-3889, 2024, DOI:10.32604/cmc.2024.046871 - 26 March 2024
Abstract This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities, generating applicable guidelines based on real-world software. The existing analysis of software security vulnerabilities often focuses on specific features or modules. This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software. The key novelty lies in overcoming the constraints of partial approaches. The proposed framework utilizes data from various sources to create a comprehensive functionality profile, facilitating the derivation of real-world security guidelines.… More >

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250

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Teng Wang, Yao Zhang, Jiangguo Liang, Shuai Wang, Shuanggen Liu^*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3891-3907, 2024, DOI:10.32604/cmc.2024.048115 - 26 March 2024
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract With the widespread data collection and processing, privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals. Support vector machine (SVM) is one of the most elementary learning models of machine learning. Privacy issues surrounding SVM classifier training have attracted increasing attention. In this paper, we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction, called Fed_DPDR-DPML, which greatly improves data utility while providing strong privacy guarantees. Considering in distributed learning scenarios, multiple participants usually hold unbalanced or small amounts of data. Therefore, Fed_DPDR-DPML enables multiple participants to collaboratively learn a global… More >

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Yilei Wang¹, Xin Sun¹, Guiping Zheng^2,3, Ahmar Rashid⁴, Sami Ullah⁵, Hisham Alasmary⁶, Muhammad Waqas^7,8,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3909-3927, 2024, DOI:10.32604/cmc.2024.047767 - 26 March 2024
Abstract The application of Intelligent Internet of Things (IIoT) in constructing distribution station areas strongly supports platform transformation, upgrade, and intelligent integration. The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer, with the former using intelligent fusion terminals for real-time data collection and processing. However, the influx of multiple low-voltage in the smart grid raises higher demands for the performance, energy efficiency, and response speed of the substation fusion terminals. Simultaneously, it brings significant security risks to the entire distribution substation, posing a major challenge to the smart grid. In… More >

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Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

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Kechen Song^1,2,*, Wenqi Cui², Han Yu¹, Xingjie Li¹, Yunhui Yan^2,*
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451 - 26 March 2024
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel More >

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