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  • Open Access

    ARTICLE

    New Trends in the Modeling of Diseases Through Computational Techniques

    Nesreen Althobaiti1, Ali Raza2,*, Arooj Nasir3,4, Jan Awrejcewicz5, Muhammad Rafiq6, Nauman Ahmed7, Witold Pawłowski8, Muhammad Jawaz7, Emad E. Mahmoud1

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2935-2951, 2023, DOI:10.32604/csse.2023.033935 - 21 December 2022

    Abstract The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil, China, Ethiopia,… 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 - 01 August 2022

    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 >

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