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Bauyrzhan Omarov^1,*, Zhuldyz Sailaukyzy², Alfiya Bigaliyeva², Adilzhan Kereyev³, Lyazat Naizabayeva⁴, Aigul Dautbayeva⁵
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3765-3781, 2023, DOI:10.32604/cmc.2023.042469
Abstract In the face of escalating intricacy and heterogeneity within Internet of Things (IoT) network landscapes, the imperative for adept intrusion detection techniques has never been more pressing. This paper delineates a pioneering deep learning-based intrusion detection model: the One Dimensional Convolutional Neural Networks (1D-CNN) and Bidirectional Long Short-Term Memory (BiLSTM) Network (Conv-BiLSTM) augmented with an Attention Mechanism. The primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data, thereby facilitating the precise categorization of a myriad of intrusion types. Methodology: The proposed model amalgamates… More >

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Shi Qiu¹, Pengchang Zhang^1,*, Xingjia Tang², Zimu Zeng¹, Miao Zhang¹, Bingliang Hu^1,*
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3783-3800, 2023, DOI:10.32604/cmc.2023.042074
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history, science, culture, art and research. However, mainstream analytical methods are contacting and detrimental, which is unfavorable to the protection of cultural relics. This paper improves the accuracy of the extraction, location, and analysis of artifacts using hyperspectral methods. To improve the accuracy of cultural relic mining, positioning, and analysis, the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques. Firstly, region stitching algorithm based on the relative position of hyper spectrally collected data… More >

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Ahmad F Subahi^*, Areej Athama
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3801-3816, 2023, DOI:10.32604/cmc.2023.034522
（This article belongs to the Special Issue: Smart Solutions to Develop New Technologies for Healthcare)
Abstract With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In… More >

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Muhammad Aleem Raza¹, Muhammad Anwar², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Usman Ahmed Raza¹, Sadiq Ali Khan⁴, Fahad Ahmad⁵
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275
Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute (ANSI) standards and the Association… More >

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S. Kannan^1,*, S. Anusuya²
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465
Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With… More >

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Adnan Hussein¹, Abdulwahab Ali Almazroi^2,*
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3853-3866, 2023, DOI:10.32604/cmc.2023.040856
Abstract The advances in technology increase the number of internet systems usage. As a result, cybersecurity issues have become more common. Cyber threats are one of the main problems in the area of cybersecurity. However, detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its importance. This study aims to analyze Twitter data to detect cyber threats using a multiclass classification approach. The data is passed through different tasks to prepare it for the analysis. Term Frequency and Inverse Document Frequency (TFIDF) features are extracted to vectorize the cleaned data… More >

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Yuting Zhu¹, Yanfang Fu^2,*, Yang Ce³, Pan Deng¹, Jianpeng Zhu¹, Huankun Su¹
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3867-3894, 2023, DOI:10.32604/cmc.2023.046772
Abstract In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.… More >

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Abdul Haseeb¹, Muhammad Attique Khan^2,*, Majed Alhaisoni³, Ghadah Aldehim⁴, Leila Jamel⁴, Usman Tariq⁵, Taerang Kim⁶, Jae-Hyuk Cha⁶
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve… More >

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Abdulwahab Ali Almazroi^1,*, Nasir Ayub²
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3921-3942, 2023, DOI:10.32604/cmc.2023.046602
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimization methods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly-Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses… More >

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Mohammed S. Alsaqer¹, Majid H. Alsulami^2,*, Rami N. Alkhawaji³, Abdulellah A. Alaboudi²
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3943-3968, 2023, DOI:10.32604/cmc.2023.042379
Abstract Blockchain technology has revolutionized conventional trade. The success of blockchain can be attributed to its distributed ledger characteristic, which secures every record inside the ledger using cryptography rules, making it more reliable, secure, and tamper-proof. This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context. Furthermore, it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure. To realize the full potential of the accurate and efficacious use of blockchain in… More >

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174

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Naeem Ullah¹, Javed Ali Khan^2,*, Sultan Almakdi³, Mohammed S. Alshehri³, Mimonah Al Qathrady⁴, Eman Abdullah Aldakheel^5,*, Doaa Sami Khafaga⁵
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model… More >

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Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526
Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

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Mohammed Zakariah¹, Salman A. AlQahtani^2,*, Abdulaziz M. Alawwad¹, Abdullilah A. Alotaibi³
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are slowing computation. In addition, networked… More >

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Siyu Yang¹, Ge Song^1,*, Yuqiao Deng², Changyu Liu¹, Zhuoyu Ou¹
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4055-4072, 2023, DOI:10.32604/cmc.2023.042277
Abstract Deep neural networks have achieved tremendous success in various fields, and the structure of these networks is a key factor in their success. In this paper, we focus on the research of ensemble learning based on deep network structure and propose a new deep network ensemble framework (DNEF). Unlike other ensemble learning models, DNEF is an ensemble learning architecture of network structures, with serial iteration between the hidden layers, while base classifiers are trained in parallel within these hidden layers. Specifically, DNEF uses randomly sampled data as input and implements serial iteration based on the weighting strategy between hidden layers.… More >

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Xuanchen Zhou^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4073-4087, 2023, DOI:10.32604/cmc.2023.040253
Abstract With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly, most prior studies have used… More >

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