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Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Ebrahem A. Algehyne, Muhammad Lawan Jibril, Naseh A. Algehainy, Osama Abdulaziz Alamri, Abdullah K. Alzahrani
2022 Big Data and Cognitive Computing  
In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed  ...  An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of  ...  Acknowledgments: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia and University of Tabuk, Tabuk71491, Saudi Arabia as well.  ... 
doi:10.3390/bdcc6010013 fatcat:7xqy3q4bb5huxbdljzj5dgnpw4

An IoT-based Intelligent Wound Monitoring System

Hina Sattar, Imran Sarwar Bajwa
2019 IEEE Access  
We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree.  ...  Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status  ...  There are many available techniques in machine learning for analysis of data to predict outcome e.g. SVM, Neural Network, KNN, Random Forest, Decision Tree.  ... 
doi:10.1109/access.2019.2940622 fatcat:yoixccls2fhujiwz5dnv4sybri

CFE-CMStatistics 2017 PROGRAMME AND ABSTRACTS 11th International Conference on Computational and Methodological Statistics (CMStatistics 2017) CMStatistics 2017 Co-chairs: CMStatistics 2017 Programme Committee

Ana Colubi, Erricos Kontoghiorghes, Marc Levene, Bernard Rachet, Herman Van, Dijk, Veronika Czellar, Hashem Pesaran, Mike Pitt, Stefan Sperlich, Knut Aastveit, Alessandra Amendola (+81 others)
E1092: Towards an efficient early warning system for extreme wind speed detection Presenter: Daniela Castro, King Abdullah University of Science and Technology, Saudi Arabia Co-authors: Raphael Huser,  ...  We apply BANS to identify integrative networks for key signaling pathways in kidney cancer and dynamic signaling networks using longitudinal protein data from a breast cancer cell line.  ...  The aim is to assess the extent to which enhanced design is used in combination with quick response in the fashion system, following previous results.  ...