درخت حوزه‌های تخصصی

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions

Purpose: This research aims to design and explain the process model of content marketing with the approach of strengthening the customers' purchase intention in the field of Keshavarzi bank services.Method: This research is applied in terms of purpose, exploratory in terms of approach, and mixed (qualitative-quantitative) in terms of data analysis. In the qualitative phase, a group of experts was considered as the statistical population including senior managers of the Keshavarzi Bank, university professors in the field of marketing, and marketing consultants familiar with the banking industry. In this phase, using a targeted non-probability sampling method, 11 experts were selected and interviewed in depth. In the quantitative phase, the customers of the Keshavarzi bank in Ahvaz were considered as the population, and out of them, 400 people were selected as a statistical sample based on the available random sampling method. The main tool of data collection in the qualitative phase was in-depth and unstructured interviews with experts. In the quantitative phase, a closed and researcher-made questionnaire consisting of 42 items was used as the main tool for data collection. In the qualitative phase, the data from the questionnaire was analyzed based on the grounded theory method (open, central, and selective coding). Content validity and reliability were obtained with Cronbach's alpha coefficient of 0.7. In the quantitative phase, SPSS and LISREL software were used to perform descriptive and inferential analyses.Findings: The findings showed that the content marketing process model included 14 categories  including causal factors (emphasis on ethics in the design of advertising message; emphasis of advertising message on competitive advantages; persuasive power of advertising message; the power of advertising message to create needs in the customer), contextual and environmental factors (adjustment of the message with the culture of the society; the general inclination of the society to use social media), the main category (attracting the customer's attention to the content of the promotional message and the services of the Keshavarzi bank), Intervening factors (use of specialized media to spread advertising messages; following the example of world-class banks in digital content production), strategic factors (stimulating customers to pass the promotional message by word of mouth; systematic and viral dissemination of the promotional message through digital social media), the consequences (increasing the intention to purchase the services of the Keshavarzi bank; strengthening the brand of the Keshavarzi bank; increasing loyalty to the Keshavarzi bank).Conclusion: The results of the research led to the design of a content marketing process model with the approach of strengthening the purchase intention of customers in the field of Keshavarzi bank services (based on the structure of the paradigm model). The hypothesized relationships of the model were tested and verified in a large community.

In this article, we will examine the financial issues related to multi-period routing and positioning and the related costs, and we will examine the related limitations. These decisions are made about location allocation, inventory and routing in a three-tier supply chain, including suppliers, warehouses and customers. We are looking for new ways to make location and routing decisions simultaneously and efficiently. In order to maximize the search space and achieve optimal results, exploratory and meta-heuristic methods have been used. The meta-heuristic technique is usually used to increase the performance of the hybrid technique. Therefore, this paper provides an overview of meta-heuristic methods and their combination to solve problems. It also examines the advantages and disadvantages of the proposed methods to solve these problems in order to provide more efficient methods.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.

The asymmetry of information between managers and shareholders significantly affects the company's investment decisions and exacerbates the problems of agency. It seems that these problems in developing markets such as Iran are more than developed markets. This research is an exploratory study, and it aims at reviewing the theory of agency in Iran. In the present study, based on the research paradigm, first, data acquisition strategy is selected based on a qualitative approach, and then qualitative study and analysis, quantitative approach, and factor analysis approach have been used to confirm the validity of the extracted model. Statistical analysis of research data shows that among the hypotheses of agency theory, risk aversion, supervision, and reward are revised dimensions in Iran like examples of developing countries.The results confirm the existence of assumptions; 1. Conflict of interests of manager and owner, 2. Information asymmetry, 3. Reverse choice, 4. Ethical risk, 5. Maximizing the expected utility, 6. Forcing and 7. Failure to confirm the assumption is the ability to predict the future and receive information. due to the existence of information asymmetry, higher investment risks of minority shareholders and transparency problems, the lack of ability to predict the future and receive information is necessarily confirmed.