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  • Open Access


    Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

    B. Kalpana1, S. Dhanasekaran2, T. Abirami3, Ashit Kumar Dutta4, Marwa Obayya5, Jaber S. Alzahrani6, Manar Ahmed Hamza7,*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2243-2257, 2023, DOI:10.32604/csse.2023.027129

    Abstract Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases… More >

  • Open Access


    An ISSA-RF Algorithm for Prediction Model of Drug Compound Molecules Antagonizing ERα Gene Activity

    Minxi Rong1, Yong Li1,*, Xiaoli Guo1,*, Tao Zong2, Zhiyuan Ma2, Penglei Li2

    Oncologie, Vol.24, No.2, pp. 309-327, 2022, DOI:10.32604/oncologie.2022.021256

    Abstract Objectives: The ERα biological activity prediction model is constructed by the compound molecular data of the anti-breast cancer therapeutic target ERα and its biological activity data, which improves the screening efficiency of anti-breast cancer drug candidates and saves the time and cost of drug development. Methods: In this paper, Ridge model is used to screen out molecular descriptors with a high degree of influence on the biological activity of Erα and divide datasets with different numbers of the molecular descriptors by screening results. Random Forest (RF) is trained by Root Mean Square Error (RMSE) and Coefficient of… More >

  • Open Access


    Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

    Mahmoud Ragab1,2,3,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4143-4155, 2022, DOI:10.32604/cmc.2022.027075

    Abstract Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes More >

  • Open Access


    Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

    Ali Algarni1, Mahmoud Ragab2,3,4,*, Wardah Alamri5, Samih M. Mostafa6

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 303-318, 2022, DOI:10.32604/csse.2022.022152

    Abstract The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order.… More >

  • Open Access


    Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

    Milos Mravik1, T. Vetriselvi2, K. Venkatachalam3,*, Marko Sarac1, Nebojsa Bacanin1, Sasa Adamovic1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 457-471, 2022, DOI:10.32604/cmc.2022.020687

    Abstract At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a… More >

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