مطالب

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

The aim of present study is to identify the characteristics of human resource management with a strategic thinking approach in the Organization for the Collection and Sale of Proprietary Property in Iran. In terms of aim, the present study is an exploratory study based on semi-structured interviews, and in terms of results, it is developmental. The statistical population of the study included executive experts who were fully familiar with the Organization for the Collection and Sale of Proprietary Property. The sampling method in the qualitative section was snowball sampling. A total number of 15 managers and officials of the Organization for the Collection and Sale of Proprietary Property of Iran, including members of the Board of Directors, consultants and managers, and deputies and heads of departments was selected in this section. The first step involved identifying human resource management indicators with a strategic thinking approach through interviews with experts and qualitative data analysis (thematic analysis). Also, to confirm the extracted themes and check the reliability of the interviews and reach a consensus on the extracted themes, a questionnaire was designed based on the findings of the thematic analysis and completed by experts and fuzzy Delphi analysis was performed. Based on results of research regarding the indicators for human resource management with a strategic thinking approach in the Organization for the Collection and Sale of Proprietary Property, five main indicators, including situational factors, stakeholders’ group, and selection of human resource policy, achievements and long-term results were identified.

This study examines empirically the effect of equity overvaluation on future stock price crash risk in companies with greater information asymmetry. Using the information asymmetry and crash risk indicators, the question that whether overvalued firms are more prone to future crash risk is investigated. Accordingly, the first purpose of this study is to investigate the relation between overvaluation and the future stock price crash risk. With the increase of information asymmetry between a firm and the market, managers have more abilities and opportunities to withhold bad news and accelerating the release of good news. As a result, it is expected that the information asymmetry between managers and investors increases future stock price crash risk. In so doing, we identify one main hypothesis and three subsidiary ones and the data of 111 listed companies of Tehran Stock Exchange for the period between 2009 and 2017 were analyzed and A panel data approach has been used to test of research hypotheses. According to the results, there is a positive and significant relationship between the overvaluation and the future stock price crash risk of companies which are operating in monopolistic markets and this relation intensified by the information asymmetry.

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 purpose of this study is to make investment decisions with the approach of data envelopment analysis and making decision on selection of optimal stock portfolio employing meta heuristic algorithms for multi-objective functions subject to real-life constraints. The statistical population of this research in capital decision-making and selection of the optimal capital composition is 183 of the selected companies of Tehran Stock Exchange, which were finally 42 companies as justified investment options. After measuring the risk and return of efficient companies, the real limitations of the budget, requirements and expectations of the investor, determination and composition of the investment were formulated as a multi-objective model. For optimal decision, the modified genetic meta heuristic algorithm and MATLAB software with dual operators were used. Elimination of the risk minimization function in sensitivity analysis improved the level of decision return but also led to more risk. Eliminating the maximizing return function improved decision-making risk but also reduced investment return. Elimination of investment requirements and expectations improved returns and increased investment risk, but more companies became involved in optimal investment.