درخت حوزه‌های تخصصی

On a global scale, one of the prevalent causes of dementia is Alzheimer’s disease (AD). It will cause a steady deterioration in the individual from the mild stage to the severe stage, and thus impair their capacity to finish any tasks with no aid. The diagnosis is done with the utilization of existing methods which include medical history; neuropsychological testing as well as MRI (Magnetic Resonance Imaging), a lack of sensitivity as well as precision does affect the consistency of efficient procedures. With the deep learning network’s utilization, it is possible to create a framework for detecting specific AD characteristics from the MRI images. While automatic diagnosis is done with the application of diverse machine learning techniques, the existing ones do suffer from certain constraints with regards to accuracy. Thus, this work’s key goal is to increase the classification’s accuracy through the inclusion of a pre-processing approach prior to the deep learning model. The Alzheimer's disease Neuroimaging Initiative (ADNI) database of AD patients was used to develop a deep learning approach for AD identification. In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. The results of the experiments show that the deep learners offered are more effective than other systems already in use.

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.

Huge economic, social, political, and technological changes in recent decades have brought increasing complexity and decreasing predictability of organizations' environments. One of the solutions offered to deal with such environments is the agile approach to human resources. This research was conducted with the aim of investigating the driving factors in human resource agility based on government policies. The current research is developmental in terms of purpose and descriptive survey in terms of the method with a combined (qualitative and quantitative) approach. The research community in the qualitative part included professors, experts in the field of human resources, and senior managers of the human resources department, and in the quantitative part, the senior and middle managers of the country's tax affairs organization who had a bachelor's degree or higher. Relevant data were extracted from theoretical literature and semi-structured open interviews in the qualitative section. The tool for data collection was a researcher-made questionnaire in the quantitative part. The results of the qualitative part led to the identification of 13 main categories, which are categorized into four main clusters (The propellants of human resource agility, Human resource agility policies, operational components of human resource agility, and Results and Functions of human resource agility) and became a five-point Likert scale questionnaire. The results of confirmatory factor analysis showed that all items have a significant relationship with the structure and the proposed structural model has a good fit. The results of the path analysis showed that the operational components of human resource agility have a mediating role in the relationship between the drivers of human resource agility and the results and functions of human resource agility. Human resource agility policies have a mediating role in the relationship between the propellants of human resource agility and the results and performance of human resource agility.