مطالب

This article was conducted with the aim of providing a model of acceptance of virtual banking in the banking industry of Iran. The method of conducting the research was based on the pluralism of the ground theory in the data, which according to conducting the research at first using library studies, identifying the constituent elements of participation after in-depth interviews with 20 professional and scientific experts, in order to extract the elements and Thematic analysis method was used to group them. The output of this stage was the identification of 111 indicators in the form of 5 main dimensions and 12 sub-dimensions. Convergent and divergent validity were used to determine the validity, and the results were at the desired level, and the results showed that in order to accept virtual banking in the banking industry of Iran, considering the current situation and the complexities of banking in Iran, a strategic plan regarding virtual banking should be prepared and compiled, which the attention of the authorities And those involved in this field are essential to the capacities of the private sector in banking activities and the expansion of banking facilities. But in order to solve the problems, pay attention to the organizational strategies that include the formulation of a strategic plan for the participation of the private sector and the use of control and evaluation tools to ensure the effectiveness of teamwork and partnership in banking, along with creating a working environment with trust and honesty among customers to improve communication. Personal and interpersonal among the participants can provide a sustainable basis for the adoption of virtual banking.

In our research, we provide a statistical syntax parsing method experimented on Kannada texts, which is an official language of Karnataka, India. The dataset is downloaded from TDIL website. Using the Cocke-Younger-Kasami (CYK) parsing technique, we generated Kannada Treebank dataset from 1000 annotated sentences in the first stage. The Treebank generated in this stage contains 1000 syntactically structured sentences and it is used as input to train the syntax parser model in the second stage. We have adopted Probabilistic Context Free Grammar (PCFG) while training the parser model and extracting the Chmosky Normal Form (CNF) grammar from a Treebank dataset. The developed syntax parser model is tested on 150 raw Kannada sentences. It outputs with the most likely parse tree for each sentence and this is verified with golden Treebank. The syntax parser model generated 74.2% precision, 79.4% recall, and 75.3% F1-score respectively. The similar technique may be adopted for other low resource languages.