جستجوی مقالات مرتبط با کلیدواژه "یادگیری جمعی" در نشریات گروه "مدیریت"

This study seeks to explore and compare ensemble learning models for more accurate predictions of students’ national ranks in Iran’s nationwide university entrance examination, commonly known as the Konkur. The primary aim is to identify optimal models capable of predicting students’ ranks with the highest precision by analyzing data from preparatory and simulated exams conducted before the Konkur. These identified models can then empower academic counselors to offer data-driven recommendations, assisting students in making informed decisions about their educational paths and academic planning.

Initially, Octoparse software was utilized to collect data related to the preparatory tests conducted by the Kanoon Farhangi Amoozesh (Cultural Center of Education, also known as Ghalamchi) to facilitate a precise analysis of students' performance. The dataset contained key information such as the average test scores, students' national and regional ranks in the exams, as well as the academic disciplines and universities where the students were admitted. Additionally, four advanced ensemble learning models—XGBoost, LightGBM, CatBoost, and Random Forest—were selected for comparison and evaluation based on their performance in predicting students' national ranks. To assess the accuracy and efficiency of these models, various metrics were used, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R²), and training and prediction times. Afterward, The collected data were split into training and testing sets to ensure optimal model training and evaluation. To achieve the best possible performance, the grid search method was applied, enabling precise tuning of the model parameters to obtain optimal results.

The study findings underscored the exceptional performance of the XGBoost and LightGBM models in predicting students' national ranks. XGBoost emerged as the most accurate model, delivering predictions that are closely aligned with the actual values and exhibiting the lowest error rates, positioning it as the top performer in this research. Similarly, LightGBM, with results closely mirroring those of XGBoost, was also recognized as a strong contender for predicting national ranks. Its blend of speed and precision made it a highly effective tool in this context. The Random Forest model also demonstrated superior accuracy compared to CatBoost, albeit requiring more time for training and prediction. In contrast, CatBoost was identified as the weakest option, displaying lower accuracy and higher error rates than the other models, despite its faster prediction times. This suggests that CatBoost may require further refinement to compete with the more successful models.

The research findings highlight the effectiveness of XGBoost and LightGBM models in ensemble learning, showcasing their exceptional performance in predicting students' ranks in nationwide examinations. With their high accuracy and optimal performance, these models can serve as valuable tools within educational systems, contributing to the improvement of learning processes. They have the potential to identify students who may encounter challenges in their educational journeys and aid in the development of more effective learning programs. Furthermore, the outcomes of this study can assist school leaders and educational policymakers in making well-informed decisions to promote educational equity and ensure equal learning opportunities for all students. Looking ahead, leveraging more advanced deep learning models and integrating additional data, such as social and economic factors and school types, could lead to enhanced prediction accuracy. Additionally, the integration of diverse machine learning models to create hybrid systems may enhance precision and minimize prediction errors. Overall, this research marks a significant milestone in the advancement of predictive and decision-making systems within the education sector, laying the groundwork for substantial progress in this field.

The healthcare insurance industry faces a significant challenge predicting individuals' insurance costs, which are based on complex parameters such as age and physical characteristics. Insurance companies categorize policyholders into high-risk and low-risk groups to manage risks and avoid potential losses. However, the accurate estimation of costs for each individual can be a daunting task. By leveraging data science and machine learning techniques, insurance companies can improve their cost estimation accuracy and better manage risks. This approach can help insurance companies to provide more accurate insurance coverage and pricing for individuals leading to higher customer satisfaction and lower financial losses. To address this challenge, a data science and machine learning-based approach that uses ensemble learning to predict high-risk and low-risk individuals is used. The method involves several steps including data preprocessing, feature engineering, and cross-validation to evaluate the model's performance. The first step involves preprocessing the data by cleaning it, handling missing values, and encoding categorical variables. The second step generates new features using feature engineering techniques such as scaling, normalization, and dimensionality reduction. Next, ensemble learning is used to combine multiple regression methods such as logistic regression, neural networks, support vector machines, random forests, LightGBM, and XGBoost. By combining these methods, the aim is to leverage their strengths and minimize their weaknesses to achieve better prediction accuracy. Finally, the model's performance is evaluated using cross-validation techniques such as k-fold cross-validation. These techniques help to validate the model's accuracy and prevent overfitting. The proposed approach achieves an AUC of 0.73 demonstrating its effectiveness in predicting high-risk and low-risk individuals. In conclusion, the healthcare insurance industry can benefit greatly from data science and machine learning-based approaches. By accurately predicting high-risk and low-risk individuals, insurance companies can better manage risks and provide more accurate coverage and pricing for their customers. This can lead to the improvement of  customer satisfaction and the reduction of financial losses for insurance companies.

The main objective of this research is to identify the impact of Learning Organization on job satisfaction and individual performance of staff in the Roads and Urban Development Mazandaran province. The study consisted of all managers and staff are Roads and Urban Development Mazandaran province using simple random sampling method, using Morgan, 118 samples were selected. The questionnaires are measuring devices that its validity was confirmed by the supervisor and several experts. After reviewing the validity and reliability, information and data are collected and analyzed using descriptive and inferential statistics were used. Method of analyzing the data, descriptive statistics (including frequency, percentage relative frequency) and inferential statistics (including Kolmogorov-Smirnov test, regression) was performed using spss software. The results indicate that the learning organization and its components (dimensions) positive impact on job satisfaction and can place significant and effective role in increasing individual performance of employees in the organization.

Accurate forecasting of stock market behavior is invaluable for traders. Forecasting financial time series is among the important and challenging problems and researchers try to extract hidden patterns to predict the future behavior of the stock market. The purpose of this paper is to provide an intelligent model to predict stock market behavior. This paper employs ensemble learning (EL) algorithm model using neural network base learners to increase the accuracy. In order to consider the direction of price change in the stock price forecasting, a two-stage structure was used. In the first stage, the next direction of the stock price (increase or decrease) was predicted and thenit was employed to forecast the price. The most important challenges of the proposed models in the stock market were the accuracy of the results and how to increase the forecasting efficiently. Research in this field has paid little attention to the prediction of the direction of the next movement of stock price, while it is very important regarding the profitability. The use of artificial intelligence-based models has shown that the stock market is predictable despite its uncertain and unstable nature. The evaluation of results in stock market dataset shows that the proposed model suggests higher accuracy compared to other models in the literature. In addition, it can overcome the market fluctuations and can be used as a reliable and applicable model in the stock markets.

The purpose of this paper is to examine the role of information and communication technologies in each step of Nonaka’s knowledge creation model and also the effects of knowledge sharing on innovation and collective learning among organization's staffs. To do so, we propose a framework based on the Nonaka’s model of knowledge creation to analyze the relationships between information and communication technologies, knowledge management and process innovation and product innovation. After that, we tested this framework in Hacoupian Corporation which has a wide use of information and communication technologies and also is an innovative firm. In this paper, qualitative research method was used to collect data from Semi-structured interviews with a number of staff. The results show that different types of information and communication technologies are used in Hacoupian Corporation and the combinations of these technologies has a positive effects on socialization, externalization, combination, and internalization of employees and leads to product innovations and process innovation.