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  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

    Okba Taouali1,*, Sawcen Bacha2, Khaoula Ben Abdellafou1, Ahamed Aljuhani1, Kamel Zidi3, Rehab Alanazi1, Mohamed Faouzi Harkat4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1593-1609, 2023, DOI:10.32604/csse.2023.039984

    Abstract Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to… More >

  • Open Access

    ARTICLE

    A Data Driven Security Correction Method for Power Systems with UPFC

    Qun Li, Ningyu Zhang*, Jianhua Zhou, Xinyao Zhu, Peng Li

    Energy Engineering, Vol.120, No.6, pp. 1485-1502, 2023, DOI:10.32604/ee.2023.022856

    Abstract The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training… More >

  • Open Access

    ARTICLE

    Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

    Fawad Nasim1,2,*, Muhammad Adnan Yousaf1, Sohail Masood1,2, Arfan Jaffar1,2, Muhammad Rashid3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2865-2877, 2023, DOI:10.32604/iasc.2023.034258

    Abstract Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success. A good batsman not only scores run but also provides stability to the team’s innings. The most important factor in selecting a batsman is their ability to score runs. It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record. This hypothesis is based on the fact that a player’s batting average is generally considered to be a good indicator of their future performance. We… More >

  • Open Access

    ARTICLE

    Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

    Yuhan Liu, Yuqiao Zheng*, Zhuang Ma, Cang Wu

    Energy Engineering, Vol.120, No.5, pp. 1223-1237, 2023, DOI:10.32604/ee.2023.026329

    Abstract An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition (SCADA) normal data drive. Firstly, the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted. Then the spindle speed is employed as the output parameter, and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the pre-processed data, with the evaluation indexes selected as the optimal model. Finally, calculating the spindle operation status index according to the sliding window principle, ascertaining the threshold value More >

  • Open Access

    ARTICLE

    Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

    Nebras M. Sobahi1,*, Ahteshamul Haque2, V S Bharath Kurukuru2, Md. Mottahir Alam1, Asif Irshad Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5757-5776, 2023, DOI:10.32604/cmc.2022.028340

    Abstract Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the… More >

  • Open Access

    ARTICLE

    An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

    Jie Ren1,2, Chuqiao Xu3, Junliang Wang2,4, Jie Zhang2,*, Xinhua Mao4, Wei Shen4

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 599-618, 2023, DOI:10.32604/cmes.2022.022415

    Abstract The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing… More >

  • Open Access

    ARTICLE

    Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

    Mehdi Jamei1, Nadjem Bailek2,*, Kada Bouchouicha3, Muhammed A. Hassan4, Ahmed Elbeltagi5, Alban Kuriqi6, Nadhir Al-Ansar7, Javier Almorox8, El-Sayed M. El-kenawy9,10

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406

    Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF),… More >

  • Open Access

    ARTICLE

    A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

    Hongfei Ma1,*, Wenqi Zhao2, Yurong Zhao1, Yu He1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498

    Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by More >

  • Open Access

    ARTICLE

    Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

    Aishah Alrashidi, Ali Mustafa Qamar*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1973-1988, 2023, DOI:10.32604/csse.2023.024633

    Abstract Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for… More >

  • Open Access

    ARTICLE

    Prediction of Low-Permeability Reservoirs Performances Using Long and Short-Term Memory Machine Learning

    Guowei Zhu*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1521-1528, 2022, DOI:10.32604/fdmp.2022.020942

    Abstract In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. More >

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