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  • Open Access


    Explainable Artificial Intelligence Solution for Online Retail

    Kumail Javaid1, Ayesha Siddiqa2, Syed Abbas Zilqurnain Naqvi2, Allah Ditta3, Muhammad Ahsan2, M. A. Khan4, Tariq Mahmood5, Muhammad Adnan Khan6,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4425-4442, 2022, DOI:10.32604/cmc.2022.022984

    Abstract Artificial intelligence (AI) and machine learning (ML) help in making predictions and businesses to make key decisions that are beneficial for them. In the case of the online shopping business, it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business. In this research, a dataset of 12,330 records of customers has been analyzed who visited an online shopping website over a period of one year. The main objective of this research is to find features that are relevant in terms of correctly predicting… More >

  • Open Access


    Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

    R. Saravana Ram1,*, M. Vinoth Kumar2, N. Krishnamoorthy3, A. Baseera4, D. Mansoor Hussain5, N. Susila6

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 545-460, 2022, DOI:10.32604/csse.2022.021681

    Abstract Major fields such as military applications, medical fields, weather forecasting, and environmental applications use wireless sensor networks for major computing processes. Sensors play a vital role in emerging technologies of the 20th century. Localization of sensors in needed locations is a very serious problem. The environment is home to every living being in the world. The growth of industries after the industrial revolution increased pollution across the environment. Owing to recent uncontrolled growth and development, sensors to measure pollution levels across industries and surroundings are needed. An interesting and challenging task is choosing the place… More >

  • Open Access


    Robust Length of Stay Prediction Model for Indoor Patients

    Ayesha Siddiqa1, Syed Abbas Zilqurnain Naqvi1, Muhammad Ahsan1, Allah Ditta2, Hani Alquhayz3, M. A. Khan4, Muhammad Adnan Khan5,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5519-5536, 2022, DOI:10.32604/cmc.2022.021666

    Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration… More >

  • Open Access


    Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression

    Fuyun Zhu, Guoqing Wu*

    Energy Engineering, Vol.118, No.6, pp. 1703-1712, 2021, DOI:10.32604/EE.2021.015602

    Abstract Accurate power load forecasting plays an important role in the power dispatching and security of grid. In this paper, a mathematical model for power load forecasting based on the random forest regression (RFR) was established. The input parameters of RFR model were determined by means of the grid search algorithm. The prediction results for this model were compared with those for several other common machine learning methods. It was found that the coefficient of determination (R2) of test set based on the RFR model was the highest, reaching 0.514 while the corresponding mean absolute error (MAE) More >

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