Ghada Atteia¹, El-Sayed M. El-kenawy^2,3, Nagwan Abdel Samee^1,*, Mona M. Jamjoom⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, Ahmad Taher Azar^8,9, Nima Khodadadi^10,11, Reham A. Ghanem¹², Mahmoud Y. Shams¹³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723 - 06 February 2023

Abstract The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose… More >

Ali Algarni¹, Mahmoud Ragab^2,3,4,*, Wardah Alamri⁵, Samih M. Mostafa⁶

Computer Systems Science and Engineering, Vol.42, No.1, pp. 303-318, 2022, DOI:10.32604/csse.2022.022152 - 02 December 2021

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 >

Uma K.V^1,*, Appavu alias Balamurugan S²

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 61-70, 2020, DOI:10.31209/2019.100000153

Abstract Real world data consists of lot of impurities. Entropy measure will help to handle impurities in a better way. Here, data selection is done by using Naïve Bayes’ theorem. The sample which has posterior probability value greater than that of the threshold value is selected. C5.0 decision tree classifier is taken as base and modified the Gain calculation function using Tsallis entropy and Association function. The proposed classifier model provides more accuracy and smaller tree for general and Medical dataset. Precision value obtained for Medical dataset is more than that of existing method. More >