This research aims to produce a diagnosis system for breast cancer by using Neural
Network depending on Back Propagation algorithm(BPNN) and Adaptive Neuro Fuzzy
Inference System ‘ANFIS’, the both of studies was done using structural features of
b
iopsies in “Wisconson Breast Cancer “data base.
In the end a comparison was made between the two studies of malignant- benign
classification of breast masses of breast cancer which has accuracy 95,95% with BPNN
and 91.9% with ANFIS system, this results can be consider very important if they
compared with researches depending on image features that obtained of various devises
like mammography, magnetic resonance.
Evaporation is a major meteorological component of the hydrologic cycle, and it
plays an influential role in the development and management of water resources. The aim
of this study is to predict of the monthly pan evaporation in Homs meteostation
using
Artificial Neural Networks (ANNs), which based on monthly air temperature and relative
humidity data only as inputs, and monthly pan evaporation as output of the network. The
network was trained and verified using a back-propagation algorithm with different
learning methods, number of processing elements in the hidden layer(s), and the number of
hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan
evaporation with total correlation coefficient equals 96.786 % and root mean square error
equals 24.52 mm/month for the total data set. This study recommends using the artificial
neural networks approach to identify the most effective parameters to predict evaporation.
The concept of frequency reuse has been successfully implemented in modern cellular communications systems in order to increase the system capacity. Further improvement of capacity can be achieved by employing adaptive arrays at the base station. In
order to track the desired users, direction finding algorithms are used to locate the positions of mobile users as they move within or between cells.
Recently, neural networks-based direction finding algorithms have been supposed for source direction finding. The performance of neural network is evaluated by comparing their prediction, standard deviation and Mean Square Error (MSE) between their predicted and measurement values. The research depends on this context. So, it has been compared the antenna array output signals according to their amplitude, then selected the signal that has the best amplitude in the system’s final output.
