Discussion of the dissertation of doctoral student " Ruya Haroush Ibrahim"
The public discussion took place for the Doctoral student (Ruya Haroush Ibrahim) at Anbar University, College of Science, Department of Physics, on Wednesday 26/2/2025 in Jabir bin Hayyan Hall, for his tagged thesis
" Ground State Properties Study of Some Odd-Mass Number Nuclei Using Artificial Neural Networks (ANN) "
The discussion committee consisted of:
Prof. Dr. Saeed Nayef Turki - University of Anbar - College of Education for Pure Sciences- Chairman
Prof. Dr. Wissam Mohammed Jassim - University of Anbar - College of Computer Science and Information Technology - Member
Prof. Dr. Adel Nima Ayash - University of Anbar College of Medicine - Member
Prof. Dr. Ahmed Fadhel Mukhaiber - University of Baghdad - College of Education Ibn Al-Haitham- Member
Assist. Prof. Dr. - Ali Khalaf Obaid - University of Anbar - College of Education for Pure Sciences- Member
Prof. Dr. Akram Mohammed Ali - University of Anbar - College of Science - Member and Supervisor
Collect experimental data on different nuclei, including binding energy, dissociation energy, and nuclear radius and design an artificial neural network consisting of multiple layers (input, hidden, and output) to predict nuclear properties and apply a PSO algorithm to optimize the performance of the neural network.
After training, the performance of the model is evaluated using a test dataset to verify its accuracy in predicting nuclear properties. The results obtained from the model are analyzed, including comparing the predicted values with the experimental values. The benefit of this study was a higher accuracy: The use of AI techniques can improve the accuracy of predictions and reduce the error rate as close to zero as possible. * Saving time: These methods can accelerate the research and development process in nuclear physics. * Expanding the range of applications: The model can be used to predict the properties of new nuclei that have not yet been studied experimentally, providing new insights into nuclear properties and guiding future research.
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)