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  • Open Access


    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

  • Open Access


    Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

    Salah Alzghoul1,*, Othman Smadi1, Ali Al Bataineh2, Mamon Hatmal3, Ahmad Alamm4

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338

    Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or… More >

  • Open Access


    Prediction of Residential Building’s Solar Installation Energy Demand in Morocco Using Multiple Linear Regression Analysis

    Nada Yamoul1,*, Latifa Dlimi1, Baraka Achraf Chakir2

    Energy Engineering, Vol.119, No.5, pp. 2135-2148, 2022, DOI:10.32604/ee.2022.020005

    Abstract The building sector is one of the main energy-consuming sectors in Morocco. In fact, it accounts for 33% of the final consumption of energy and records a high increase in the annual consumption of energy caused by further planned large-scale projects. Indeed, the energy consumption of the building sector is experiencing a significant acceleration justified by the rapid need for the development of housing stock, wich is estimated at an average increase of 1,5% per year; furthermore, tant is an estimated increase of about 6,4%. In this sense, building constitutes an important potential source for rationalizing both energy consumption and… More >

  • Open Access


    Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

    Songlin Yang1,2, Xingjin Han1,2, Chufeng Kuang1,2, Weihua Fang3, Jianfei Zhang4, Tiantang Yu4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325

    Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J23 (on dam section ), J33 (on dam section… More >

  • Open Access


    Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

    Imran Ashraf1, Waleed S. Alnumay2, Rashid Ali3, Soojung Hur1, Ali Kashif Bashir4, Yousaf Bin Zikria1,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3009-3044, 2021, DOI:10.32604/cmc.2021.015140

    Abstract The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on,… More >

  • Open Access


    Prediction and Limitations of Noise Maps Developed for Heterogeneous Urban Road Traffic Condition: A Case Study of Surat City, India

    Dipeshkumar R. Sonaviya*, Bhaven N. Tandel

    Sound & Vibration, Vol.55, No.1, pp. 57-68, 2021, DOI:10.32604/sv.2021.010715

    Abstract Road traffic noise pollution has been recognized as a serious issue which affects human health as well as affects urban regions. Noise maps are very beneficial to identify the impact of noise pollution. A noise mapping study performed to study the propagation of noise in tier-II city along with field measurements. The noise maps are developed using a computer simulation model (SoundPLAN essential 4.0 software). The noise prediction models like U.K’s CoRTN, Germany’s RLS-90, and their modified versions, which can be used for homogenous road traffic conditions, cannot be successfully applied in heterogeneous road traffic conditions of India. In developing… More >

  • Open Access


    A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

    Mohammad Aldossary*

    Computer Systems Science and Engineering, Vol.36, No.2, pp. 353-368, 2021, DOI:10.32604/csse.2021.014974

    Abstract With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers’ revenue and customers’ payment. Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources. To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs. However, such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine (PM) but… More >

  • Open Access


    Prediction Models Generation by Machine Learning for Structural Materials Performance by Utilizing the Mi System

    Satoshi Minamoto*, Takuya Kadohira, Kaita Ito, Makoto Watanabe, Masahiko Demura

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 136-136, 2019, DOI:10.32604/icces.2019.05447

    Abstract The Materials Integration (MI) System is a domestically developed system in the “Cross-ministerial Strategic Innovation Promotion Program” to analyze structural materials performance. The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment. One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically.
    Furthermore, we developed a web application (“MIREA”: MI REgression Analyzer) that enables us to build high versatile prediction models based on machine learning techniques by using the… More >

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