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  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

    Xingzi Yang1, Wei Gao2, Xiaodu Wang1, Xiaowei Zeng1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1299-1313, 2024, DOI:10.32604/cmes.2024.049371

    Abstract The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model More >

  • Open Access

    ARTICLE

    Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

    Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398

    Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

  • Open Access

    ARTICLE

    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon1, Ye-Sol Oh1, Yeon-Ji Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239

    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

  • Open Access

    REVIEW

    A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

    Yifan Liu1, Shancang Li1,*, Xinheng Wang2, Li Xu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1233-1261, 2024, DOI:10.32604/cmes.2024.046473

    Abstract The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest were More >

  • Open Access

    CORRECTION

    Correction: Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

    Seoyun Kim1,#, Hyerim Yu2,#, Jeewoo Yoon1,3, Eunil Park1,2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 861-861, 2024, DOI:10.32604/csse.2024.053659

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Multimodal Deep Neural Networks for Digitized Document Classification

    Aigerim Baimakhanova1,*, Ainur Zhumadillayeva2, Bigul Mukhametzhanova3, Natalya Glazyrina2, Rozamgul Niyazova2, Nurseit Zhunissov1, Aizhan Sambetbayeva4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 793-811, 2024, DOI:10.32604/csse.2024.043273

    Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not… More >

  • Open Access

    ARTICLE

    A Hybrid Machine Learning Framework for Security Intrusion Detection

    Fatimah Mudhhi Alanazi*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

    Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

    Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

    Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

  • Open Access

    ARTICLE

    ABMRF: An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

    Aiman1, Muhammad Arshad1, Bilal Khan1, Khalil Khan2, Ali Mustafa Qamar3,*, Rehan Ullah Khan4

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 301-317, 2024, DOI:10.32604/iasc.2024.045402

    Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age… More >

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