Number 3 Volume 1

Open Access Article

1 - DEA-Based Evaluation of the Oil Prices Effect on Industry: A Case Study of the Stock Exchange
Ali Taherinezhad alireza alinezhad

This paper evaluates the impact of oil prices on the industry and the total index of the Tehran Stock Exchange. In addition, the impact of oil shocks on the Tehran Stock Exchange is also studied. The analyzed data are the weekly crude oil price data in the industry More

This paper evaluates the impact of oil prices on the industry and the total index of the Tehran Stock Exchange. In addition, the impact of oil shocks on the Tehran Stock Exchange is also studied. The analyzed data are the weekly crude oil price data in the industry and the index of the Tehran Stock Exchange during the 10-year period from 2005 to 2015. For data analysis, this paper proposes an appropriate mathematical model based on the non-parametric technique of data envelopment analysis (DEA), which is a powerful mathematical tool for ranking decision-making units (DMUs). Ultimately, we used the proposed model to calculate the efficiency and ranking of 10 companies active in the food and beverage industry (except for the sugar group) and analyzed the results with the CCR model. Following that, the final ranking was done using the weighted average method. According to the results of the research, Mahram Production Group Company was ranked 1st in the weighted average method, and Shir Pegah West Azerbaijan was ranked second, and Nab Industrial Companies, Pars Livestock Feed, Georgian Biscuits, Iran Behnoosh, Pak Dairy, Behpak, Salemeen and Shir Pegah Isfahan ranked 3rd to 10th , respectively. Manuscript profile

Open Access Article

2 - Ranking the Generalized Fuzzy Numbers Based on the Center of the Area
Vahid Mohammadi Esmaeil Mehdizadeh Seyed Mojtaba Hejazi

In decision-making contexts marked by uncertainty, the application of fuzzy numbers has emerged as a crucial tool. These numbers offer a mathematical framework for representing imprecise information, enabling a more nuanced approach to decision-making. Fuzzy numbers fin More

In decision-making contexts marked by uncertainty, the application of fuzzy numbers has emerged as a crucial tool. These numbers offer a mathematical framework for representing imprecise information, enabling a more nuanced approach to decision-making. Fuzzy numbers find widespread application in quantifying the inherent uncertainty present in decision-making contexts. When incorporating fuzzy numbers into decision-making procedures, the necessity to compare these fuzzy numbers becomes an unavoidable occurrence. Ranking fuzzy numbers is a challenging topic. In this paper, we propose a new method for ranking generalized fuzzy numbers based on the center of the area concept. First, we present the concepts of the presented method. Additionally, the proposed method can rank symmetric fuzzy numbers relative to the y-axis easily. Then the advantages of the proposed method are illustrated through several numerical examples. The results demonstrate that this approach is effective for ranking generalized fuzzy numbers and overcomes the shortcomings in recent studies. Finally, we checked the result of the presented method with other existing methods. The results show that the presented method has consistent results with less computational complexity. Manuscript profile

Open Access Article

3 - Autonomous Robot Navigation in Dynamic Environments: A Temporal-Difference Learning Approach
arsalan montazeri sara Shademani Alishah vajiheh Ghasemi

In this paper, we address the critical challenges of robotic navigation in dynamic environments, increasingly relevant with rapid advancements in robotics and artificial intelligence. Traditional navigation methods, reliant on predefined paths and detailed mapping, ofte More

In this paper, we address the critical challenges of robotic navigation in dynamic environments, increasingly relevant with rapid advancements in robotics and artificial intelligence. Traditional navigation methods, reliant on predefined paths and detailed mapping, often fail in such unpredictable settings. Our research introduces a novel approach using temporal-difference learning, a form of reinforcement learning, to enhance robot navigation in these scenarios. We explore the difficulties posed by dynamic environments, such as moving obstacles and changing terrains, and demonstrate the adaptability of temporal-difference learning in overcoming these challenges. Our method, tested through rigorous experiments, shows significant improvements in adaptability, reduced collisions, and enhanced pathfinding efficiency in various simulated conditions. These results emphasize the potential of our approach in creating more resilient robotic systems for complex situations, including urban landscapes, disaster areas, or extraterrestrial environments. This paper contributes to the field of robotics by offering a promising solution to navigate dynamic settings, opening new possibilities for robotic deployment in intricate and unpredictable environments. Manuscript profile

Open Access Article

4 - Portfolio optimization based on return prediction using multiple parallel input CNN-LSTM
Hatef Kiabakht Mahdi Ashrafzadeh

The success of any investment portfolio always depends on the future behavior and price events of assets. Therefore, the better one can predict the future of an asset, the more profitable decisions can be made. Today, with the expansion of machine learning models and th More

The success of any investment portfolio always depends on the future behavior and price events of assets. Therefore, the better one can predict the future of an asset, the more profitable decisions can be made. Today, with the expansion of machine learning models and their advanced sub-branch i.e. deep learning, it is possible to better predict the future of assets and make decisions based on those predictions. In this article, a deep learning method called CNN-LSTM with multiple parallel inputs is introduced and is shown that it is able to provide a more accurate prediction of asset returns for the next period than other machine learning and deep learning models. Then, these forecasts will be used in two stages to build the portfolio. First, the assets that have the highest predicted return are selected, and then in the second step, Markowitz's mean-variance model will be used to obtain the optimal ratio of the selected assets for trading in the next period. The model test is performed on the assets randomly selected from different New York Stock Exchange industries based on the 11 Global Industry Classification Standard (GICS) Stock Market Sectors. Manuscript profile

Open Access Article

5 - The Impact of Organizational Structure on Organizational Performance by Applying Balance Scorecard: A Case Study
sayyed mohammadreza davoodi mansoor abedian

This paper aimed to apply a balanced scorecard as an evaluation performance tool to investigate the impact of organizational structure variables on the organizational performance of Esfahan steel industries. The standardized Robbins’s (1998) questionnaire was us More

This paper aimed to apply a balanced scorecard as an evaluation performance tool to investigate the impact of organizational structure variables on the organizational performance of Esfahan steel industries. The standardized Robbins’s (1998) questionnaire was used for data collection. Hersey and Goldsmith’s standard questionnaire was used to assess aspects of organizational performance. The sample consisted of 100 employees working in the Esfahan steel industries. The results demonstrated that there is a meaningful relationship between organizational performance and organizational structure and its components including complexity, formality, and concentration. The findings also indicated that concentrations had the most significant impact on organizational performance. The results of the Bartlett and Kaiser-Meyer tests indicated a high reliability of the research (0.89 and a confidence level of less than 0.05). Also, using correlation coefficient, regression test, and structural equations, the assumptions of the research were confirmed. The results of regression show that centralizations (With a coefficient of 0.59) had the most significant impact on organizational performance. Manuscript profile

Open Access Article

6 - Enhancing Lung Cancer Diagnosis Accuracy through Autoencoder-Based Reconstruction of Computed Tomography (CT) Lung Images
Mohammad Amin Pirian iman heidari Toktam Khatibi Mohammad Mehdi Sepehri

Lung cancer is a major global cause of cancer-related deaths, emphasizing the importance of early detection through chest imaging. Accurate reconstruction of computed tomography (CT) lung images plays a crucial role in the diagnosis and treatment planning of lung cancer More

Lung cancer is a major global cause of cancer-related deaths, emphasizing the importance of early detection through chest imaging. Accurate reconstruction of computed tomography (CT) lung images plays a crucial role in the diagnosis and treatment planning of lung cancer patients. However, noise in CT images poses a significant challenge, hindering the precise interpretation of internal tissue structures. Low-dose CT, with reduced radiation risks, has gained popularity. Nonetheless, inherent noise compromises image quality, potentially impacting diagnostic performance. Denoising autoencoder and unsupervised deep learning algorithms offer a promising solution. A dataset of CT images from patients suspected of lung cancer was categorized into four disease groups to evaluate different autoencoder models. Results showed that designed autoencoders effectively reduced noise, enhancing overall image quality. The semi-supervised autoencoder exhibited superior performance, preserving fine details and enhancing diagnostic information. This research underscores autoencoder models' potential in improving lung cancer diagnosis accuracy by reconstructing CT lung images, emphasizing the importance of noise reduction techniques in enhancing image quality and diagnostic performance. Manuscript profile

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