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  • Open Access


    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this… More >

  • Open Access


    Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction

    Ahmad Almadhor1, Chitapong Wechtaisong2,*, Usman Tariq3, Natalia Kryvinska4,*, Abdullah Al Hejaili5, Uzma Ghulam Mohammad6, Mohana Alanazi7

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 199-216, 2024, DOI:10.32604/csse.2023.039546

    Abstract Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfort model that makes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs the ASHRAE RP-884 standard dataset… More >

  • Open Access


    Deep Autoencoder-Based Hybrid Network for Building Energy Consumption Forecasting

    Noman Khan1,2, Samee Ullah Khan1,2, Sung Wook Baik1,2,*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 153-173, 2024, DOI:10.32604/csse.2023.039407

    Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

  • Open Access


    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer1,*, Ahmed H. Samak2, Ahmed I. Taloba3,4, Rasha M. Abd El-Aziz3,5

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265

    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

  • Open Access


    Intrusion Detection and Prevention Model for Blockchain Based IoMT Applications

    Jameel Almalki*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 131-152, 2024, DOI:10.32604/csse.2023.038085

    Abstract The recent global pandemic has resulted in growth in the medical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare and medical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internet-based channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved… More >

  • Open Access


    A Work Review on Clinical Laboratory Data Utilizing Machine Learning Use-Case Methodology

    Uma Ramasamy*, Sundar Santhoshkumar

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 1-14, 2024, DOI:10.32604/jimh.2023.046995

    Abstract More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive APD dataset study comprising four… More >

  • Open Access


    A Four-Site Water Model for Liquid and Supercooled Water Based on Machine Learning: TIP4P-BGWT

    Jian Wang1,*, Yonggang Zheng1, Hongwu Zhang1, Hongfei Ye1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09761

    Abstract Water is the most ubiquitous fluid in nature and widely exists in the micro/nanoconfinement of leafstalks, shale, bones, etc. The complex relation of the properties and behaviours of water to the temperature, pressure and confinement size enhances the difficulty in the accurate simulation, such as the supercooled state of pure water below the freezing point. As a powerful tool, molecular dynamics simulation is adequate for investigating the relevant properties and behaviours. However, accurately calculating the physical properties of liquid and supercooled water is quite challenging by molecular simulations owing to limited model parameters. Machine learning (ML) techniques and temperature-dependent parameters… More >

  • Open Access


    A Machine Learning Framework for Isogeometric Topology Optimization

    Haobo Zhang1, Ziao Zhuang1, Chen Yu2, Zhaohui Xia1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.09091

    Abstract Topology optimization (TO) is an important and powerful tool to obtain efficient and lightweight structures in conceptional design stage and a series of representative methods are implemented [1-5]. TO are mainly based on the classical finite element analysis (FEA), resulting in an inconsistency between geometric model and analytical model. Besides, there are some drawbacks of low analysis accuracy, poor continuity between adjacent elements, and high computational cost for high-order meshes. Thus, isogeometric analysis (IGA) is proposed [6] to replace FEA in TO. Using the Non-Uniform Rational B-Splines (NURBS), IGA successfully eliminates the defects of the conventional FEA and forms a… More >

  • Open Access


    A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification

    Adil Hussain1, Amna Khatoon2,*, Ayesha Aslam2, Tariq1, Muhammad Asif Khosa1

    Journal of Cyber Security, Vol.6, pp. 1-23, 2024, DOI:10.32604/jcs.2023.046915

    Abstract The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of machine learning algorithms in the… More >

  • Open Access


    Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

    Congjun Rao1, Mengxi Li1, Tingting Huang2,*, Feiyu Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

    Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

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