درخت حوزه‌های تخصصی

Cyberbullying involves using hurtful or offensive language that goes against basic rules of respect and politeness. It harms the online environment and can negatively affect people by causing harassment, discrimination, or emotional pain. To combat this, it is crucial to develop automated methods for detecting and preventing the dissemination of such content. Deep learning, a branch of artificial intelligence, leverages neural networks to learn from data and perform complex tasks, effectively capturing semantic and grammatical nuances to differentiate between abusive and non-abusive language. This survey paper reviews current techniques and advancements in deep learning-based approaches for detecting cyberbullying content on online platforms, aiming to provide a comprehensive understanding of existing methodologies and identify potential avenues for future research to mitigate the spread and impact of such behaviors on the internet.

This research aims to design an open banking model using a grounded theory approach in bank Saderat Khorasan Razavi. The current research is exploratory in nature and mixed-in approach. This research was carried out in two stages among the research projects of the combined method. For this purpose, it collects qualitative data first. In this section, the indicators were identified through reviewing existing documents, papers, and articles and semi-structured interviews with experts, and the grounded theory method was used to select the identified indicators. Open and axial coding was done and a visual pattern was designed. In second stage was the quantitative part of the research, after the final research model was identified in the qualitative part and the components and indicators of each component were determined, the final questionnaire included all dimensions and components of the identified model based on the five-choice Likert scale and based on dimensions and the components of open banking in the country's banking industry were compiled and distributed among sample people and data were collected. The statistical population in the qualitative part of the research and the process of identifying dimensions and components of open banking in the country's banking industry included organizational experts, these people included senior managers, deputy managers, middle managers, and senior experts. The statistical population in the quantitative part included all managers and senior experts of open banking in the country's banking industry. According to the obtained results, the consequences with an average rank of 2.63 are the priority. Intervening factors with an average rating of 2.55 are the second priority. The central phenomenon with an average rank of 2.43 is the third priority. Contextual factors with an average rank of 2.40 are the fourth priority. Strategies are in the fifth priority with an average rank of 2.39. Causal factors with an average rating of 2.38 are the last priority.