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the incidence of this disease has increased significantly in the recent
years, expert systems and machine learning techniques to this problem have
also taken a great attention from many scholars. This study aims at diagnosing
and prognosticating breast cancer with a machine learning method based on random
forest classifier and feature selection technique. By weighting, keeping useful
features and removing redundant features in datasets, the method was obtained
to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis
Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer
Prognostic Dataset. On these datasets we obtained classification accuracy of
100% in the best case and of around 99.8% on average. This is very promising
compared to the previously reported results. This result is for Wisconsin
Breast Cancer Dataset but it states that this method can be used confidently
for other breast cancer diagnosis problems, too.
A fault management dispatcher training simulator for large-scale Distribution Automation System (TDAS) is developed to train operators in distribution control center. This simulator is composed of independent simulation server and operator consoles and can be used for network analysis, network operation, fault management and evaluation. TDAS DB is duplicated online to the simulation server keeping the data security. The system can model distribution network penetrated with distributed generations (DG) using the real data from the TDAS DB. Network fault scenarios are automatically generated by calculating fault current and generating fault indicators. Also, manual entry of cry wolf alarm is available. Moreover, operation solution for scenario of fault isolation and service restoration is generated automatically so that trainee can check their operation result. Operator actions during training session are saved and can be played back as well as displayed on one-line diagram pictures.