فهرست مطالب فیروزه رضوی

Alzheimer chr('39')s disease is the most common form of dementia which has caused disorder in memory. Cerebral palsy and posttraumatic stress disorder (veterans of war, warriors, armed forces) play an important role in increasing the risk of Alzheimerchr('39')s disease. The nature of the large dimensions of neural data, as well as the small number of available samples, make for an accurate computer diagnostic system. The aim of this study was to apply deep neural networks to develop an automatic disease diagnosis system.

Methods and Materials: 

In this research, studies on magnetic resonance imaging of war veterans are done by python Software. In the proposed model, in the proposed model, 10% of the images of the data base were selected for training. In the first stage, the training is from deep learning with Convolutional network to extract the features, then in the second stage, in order to classify the health status based on the learned features.

The results of the analysis are also compared with the results presented in previous Studies. The proposed method has higher detection accuracy than the existing Ones, which increases the accuracy of detection in many cases.

The results of this study showed that using intelligent methods based on deep learning can accurately diagnose the disease.

In recent years, many studies have been done to analyze brain diseases in order to identify brain diseases that have a significant role in creating diagnostic intelligent systems. among the different methods of machine learning, deep learning based methods in recent years have been a wide application of the development of intelligent systems, which resulted in the creation of powerful systems for diagnosis of disease.

In this study, the diagnosis of Alzheimer's patients with deep learning neural network is based on method of 3- D Residual  Block. the training and test procedure presented by ADNI data set  

The results showed that the output of this Method were conducted in comparison to the proposed methods, accuracy of diagnose and classification of Alzheimer's disease.

the findings of the present study showed that the machine learning with deep learning methods can diagnose Alzheimer's disease sooner than doctors.

The word paradigm has become so inflexible in the warp of various disciplines, from natural science to human sciences, that it is necessary to understand the concept and dimensions of it for each researcher and, accordingly, has become a dominant concept in the philosophy of science. There are various definitions of the paradigm, but one can imagine a paradigm as a set of fundamental beliefs and assumptions that are embodied in ontology, epistemology, and methodology. Several divisions of paradigms have been made, but in the science of organization and management, the division of paradigms into structuralism, fundamental anthropology, interpretiveism, and functionalism are more prevalent. In this paper, with a description of each paradigm, the impact of changing the philosophical paradigms on knowledge management has been discussed and it is stated that over time the paradigm of knowledge management has changed from positivism to postmodern. In addition to examining the process of changing the paradigm in theory, a study was conducted on 40 papers between 2011 and 2015 to determine the dominant paradigm of the international research space in knowledge management. The results showed that the paradigm of functionalism (positivism) overwhelms other paradigms.