Total GPA: 17.54 out of 20
Graduated with honors, Graduated with honors in Bachelor of Science from Parsa Institute Of Higher Education
Thesis:Investigating cutting and mutation operators in the routing of relief agents
Final project: Investigating cutting and mutation operators in the routing of relief agents. Abstract: Routing problem of rescue agents in the earthquake disaster plays an important role in the assistance of victims. Routing of agents is done by using genetic algorithm that each agent can help victims using the shortest path. Optimal routing in the earthquake disaster help rescue agents to relief victims in a shorter time and so number of victims that rescue agents can relief are increase. In this thesis different types of crossover and mutation operators of genetic algorithm are used for finding the shortest path for rescue agents. First the routing of rescue agents are introduced, in the next section an introduction of genetic algorithm will introduce and in the last section different types of crossover and mutation operators will discuss and then compare with each other. Supervisor: Reza Jahnbin Ardebili, Instructor, Computer Engineering Department, Parsa Institute Of Higher Education, babolsar, Iran. Thesis grade: 20 out of 20.Total GPA: 17.35 out of 20
Courses GPA: 17.38 out of 20
Thesis:Analysis Brain Magnetic Resonance Images for Tumor Detection and Classification Using Feature Extraction Methods and Convolutional Neural Network
Final project: Analysis Brain Magnetic Resonance Images for Tumor Detection and Classification Using Feature Extraction Methods and Convolutional Neural Network. Abstract: In the United States, it is estimated that 23,000 new cases of brain cancer were detected in 2015. While gliomas are the most common brain tumors, they are the most common, leading to a very short life expectancy at the highest levels, and at the lowest level of life expectancy of over two years. Timely and prompt diagnosis and treatment planning lead to improved quality of life and increased life expectancy in these patients. Magnetic resonance imaging is an imaging technique that provides accurate images of the brain and is one of the most common tests used to diagnose and evaluate brain tumors. Similarly, MRI images can have a major impact on disease improvement, diagnosis, prediction, growth, and treatment planning. For this purpose, the exact diagnosis of a tumor by a doctor depends on the doctor’s skill and is of great importance. The use of deep neural networks to help classify and identify a tumor helps the physician to properly diagnose the tumor and minimize the error rate. In this thesis, the thesis describes the categorization of urinary tumor images of the brain into two groups of normal and tumor, using deep learning methods, especially the convolutional neural network. The basis of this network is the convolutional layer that works very well to find out the features in the images, and if we put some of these layers back together, they will wonderfully learn a hierarchy of nonlinear features. Supervisor: Mohammad Teshnehlab, Assistant professor, Computer Engineering Department, K.N.Toosi University of Technology, Tehran, Iran. Thesis grade: 18 out of 18Intelligent Systems Laboratory (ISLab), supervisor: Prof. Mohammad Teshnehlab. Research Topic: Deep learning for Medical Images, K. N. Toosi University of Technology, Tehran, Iran.
Member of the Young Researchers Club, Islamic Azad University. Tehran, Iran.
Graduated with honors in Bachelor of Science from Parsa Institute Of Higher Education. Babolsar, Iran
Member of the Academic Society of Computer Engineering at Parsa University. Babolsar, Iran
Siar M, Teshnehlab M. A combination of feature extraction methods and deep learning for brain tumour classification. IET Image Processing. 2021 Oct 5.
Siar M, Teshnehlab M. Age Detection from Brain MRI Images Using the Deep Learning. In2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) 2019 Oct 24 (pp. 369- 374). IEEE.
Siar M, Teshnehlab M. Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm. In2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) 2019 Oct 24 (pp. 363-368). IEEE
Halimeh Siar, Mohmmad Teshnehlab: "Diagnosing and Classification Tumors and MS Simultaneous of Magnetic Resonance Images Using Convolution Neural Network". 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Bojnord, Iran, IEEE.
Siar M, Teshnehlab M. Age and Gender Classification from Brain MRI Images Using the Convolutional Neural Network, Iranian conference on Biomedical Engineering (ICBME 2019).
Halimeh Siar, Mohmmad Teshnehlab: "Analysis Brain MRI Images for Tumor Detection Using Convolutional Neural Network". 49th Annual Iranian Mathematics conference, Iran University of Science and Technology, Tehran, Iran
Halimeh Siar, Fatemeh Siar: "Evaluating the Effecty of Deep Learning in Speech Classification". 49th Annual Iranian Mathematics conference, Iran University of Science and Technology, Tehran, Iran