University of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601Portfolio Selection by Optimizing Risk and Return Based on Complex Network Analysis (Case Study: Tehran Stock Exchange)11515031110.22034/ijiems.2021.299826.1047ENZainabolhodaHeshmatiAmirabad, North Kargar Street, Faculty of New Sciences and Technologies, University of Tehran0000-0002-5331-7488FaridehRahimnezhadAmirabad, North Kargar Street, Faculty of New Sciences and Technologies, University of TehranJournal Article20210814Nowadays, complex networks are applied for analyzing a huge body of data. Since the stock market has large data that are constantly fluctuating, it is highly difficult to analyze these data and manage the stock purchase and sale for investors. In this research, complex network is applied to select stock portfolios in order to facilitate market analysis and decision making in business relationships, and reduce the risk of inaccurate decisions. For this purpose, Tehran Stock Exchange was selected and subsequently, the latest data were collected over six consecutive years. Afterwards, a stock return correlation network was developed. According to the community detection, cohort groups were identified and then, a stock was selected from each community by designing an optimization model from the network centralities, risk and returns. Finally, for checking the accuracy of the selected portfolio, the portfolio performance in two ways with and without risk was compared with the performance of the TEPIX index. Results of this study showed that complex networks played a very effective role in selecting stock portfolios with high returns and low risk by visualizing lots of stocks in one network picture and facilitate global characteristics analysis across the network.https://www.ijiems.com/article_150311_55387fcd2fc9a1ac5dddbb36c9b4f2c9.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601Designing a Memory-Based Collaborative Filtering Group Recommender System to Confront the Cold Start Phenomenon162315031510.22034/ijiems.2022.296786.1043ENEhramSafariFaculty of Policy Research, Iran Telecommunication Research CenterMozhganKamariDepartment of Information Technology
Science and Research branch, Islamic Azad University
Tehran, IranJournal Article20210725Today, people devote more of their time on social networks. In these fields, users need to make sure of activity together and ride them as a group called group recommendation systems. The primary objective of this approach is to propose one or more entities to a group of individuals to maximize the requests and benefits of that group of individuals. Collaborative filtering approaches are widely employed in these procedures and are based on a complete initial ranking in the user-item matrix. However, in the real system, this matrix is still sparse, and the priority of users is unknown. This problem can make memory-based collaborative filtering unsuitable for group recommendation systems. Many types of research have been done to solve these systems' cold start and sparsity problems. However, unlike the developed approaches that emphasize the problem of the sparse item-user matrix in individual recommendation systems, the approach of this research is solving this problem is the group recommendation systems and tries to provide an optimal solution for the sparse matrix of the user-item. The central part of the proposed method is based on a multilayer perceptron that computes the similarity between items. It is indicated that the proposed method gives group members more satisfaction with the other five existing algorithms.https://www.ijiems.com/article_150315_a09ae8b45bb0a1b7714daec7deb59f68.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601A Firefly Algorithm for Portfolio Optimization Problem with Cardinality Constraint243315031810.22034/ijiems.2021.297820.1044ENManijeRamsheDepartment of Accounting, University of Qom., Qom, IranMohsenGharakhaniAssistant Professor &Dean, Department of Accounting & Finance, Iranian E-Institute of Higher Education, Tehran, IranAliFeyzDepartment of Financial Engineering, University of Science and Culture., Tehran, IranSeyed JafarSadjadiDepartment of Industrial Engineering, Iran University of Science and Technology, Tehran, IranJournal Article20210802The Portfolio Selection Problem is one of the most widely studied topics in the finance and economics area. Many portfolio optimization problems are formulated as a complex mathematical model where direct optimal solutions cannot be obtained in a reasonable amount of time with dependable accuracy. In this paper, the firefly algorithm, a newly introduced metaheuristic approach, has been used to solve the Markowitz portfolio optimization problem with cardinality constraints which is among the difficult mathematical problems in finance. The performance of the proposed method is then compared with some other available techniques in the literature; such as Genetic Algorithm, Tabu Search, Simulated Annealing, and Particle Swarm Optimization. The preliminary results indicated that the proposed model outperforms other methods in some cases considering error criteria for some benchmark data sets that are widely tested in the past. We illustrate with numerical examples with the statistical test that by using a well-tuned firefly algorithm we can have a better resulthttps://www.ijiems.com/article_150318_01ac6dc2d607591a64166d5149246284.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601A Structural Equation Model for Success Measurement of Regional Development Projects345115032110.22034/ijiems.2021.150321ENMeltem YontarAksoyDepartment of Industrial Engineering, Istanbul Technical University, İstanbul, TurkeySedaYanıkDepartment of Industrial Engineering, Istanbul Technical University, İstanbul, TurkeyJournal Article20210906Recent studies on determination of success criteria and critical success factors have gained a large share in project management research. However, there exist no “one-way” to define the project success. Meanwhile, it is also difficult to determine the success criteria and critical success factors to cover each type of project. This paper describes the development and investigation of the attributes of the success criteria and critical success factors and an analysis of the relationship between the success criteria and the success factors for regional development projects. A partial least square structural equation model (PLS-SEM) has been developed that includes variables of training, project team, project design, risk, sponsor, monitoring and project success. The model is validated using the data collected by a survey conducted for the past projects financially supported by Istanbul Development Agency. The overall results emphasize project design as the key success factor for the success of the project. Three success factors- project team, risk and monitoring- also matter as much if not more than the project design. We propose that the effect of sponsor can be displayed with an improved database due to its wide-ranging contribution to regional outcomes, the breadth of their partnership working and the long-term nature of their actions. This study has contributed to the growing literature related to success criteria and critical success factors for regional development projectshttps://www.ijiems.com/article_150321_adc6076d3c42ca9994c2d251516936f2.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601Proposing a Building Maintenance Management Framework to Increase the Useful Life of the Building526115032210.22034/ijiems.2021.289406.1039ENMiladGhanbariDepartment of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran0000-0001-9550-5711MastanehMojtahedzadeh AslM.Sc. Student, Department of Project and Construction Management, South Tehran Branch, Islamic Azad University, Tehran, IranJournal Article20210606Consideration of the global standards indicates that the life span of buildings in Iran is 25 up to 30 years and it reaches up to 100 years in developed countries. Preventive maintenance strategies should be designed to control depreciation and maintain the optimal performance of building components. This study, while reviewing maintenance-related research, aims to propose a framework of building maintenance management to increase the useful life of the building. To achieve this goal, each building needs a maintenance and implementation plan depending on its conditions. Implementing the life cycle costing (LCC) principles of any building requires inspection, repair, and recording of financial and technical data. The proposed framework of this research is based on finding the technical and economic useful life of the building by mathematical models such as LCC and comparing it with the age of the building. The existence and implementation of a maintenance checklist is the most basic way to increase the life of the building, which is divided into architectural, structural, electrical, and mechanical sections. In this research, in the end, a sample maintenance checklist is introduced by using the Delphi methodhttps://www.ijiems.com/article_150322_8b6f9b5d8a1d4fa62b0b8319b71c4d47.pdfUniversity of HormozganInternational Journal of Industrial Engineering and Management Science2409-18718120210601Provide Neural Network Prediction Model for Early Detection of Breast Cancer627015032410.22034/ijiems.2022.325437.1052ENParisaDaneshjooHead of Computer Engineering Department, Faculty of Engineering, West Tehran Branch, Islamic Azad University, Tehran, IranHelalehSeyedmirnasabMaster of Information Technology Student, majoring in Software Design and Production, Faculty of Engineering, West Tehran Branch, Islamic Azad University, Tehran, IranJournal Article20210906Breast cancer is a malignant mass in which breast tissue cells divide without any control due to genetic disorders such as mutations, chromosomal enhancement, deletion, reorganization, displacement, and recurrence. The diagnosis with breast cancer is very time-consuming. If the disease is diagnosed sooner than five years from the first cell deviation, this will increase the patient's chances of survival from 56% to more than 86%, which is very high. Data Mining is a new method for early detection and prognosis of breast cancer. The way presented in the present study will provide a model for predicting and early detection of breast cancer that will help take a step forward with the help of data mining and neural network techniques. This study examines the neural model for diagnosing breast cancer by data analysis on age, weight, age of onset of menopause, age of menopause, duration of OCP use, period of first pregnancy, family history, exercise, and some months of breastfeeding as inputs and disease variables Breast cancer as the output of the feed neural network with the Levenberg-Marquardt post-diffusion learning algorithm and the study of estimating the accuracy of the models by MSE and RMSE methods determined to the principle of multilayer neural networking algorithm has a good result. Also, according to the sensitivity analysis, it was determined that family history is more important, and less important are the variables of age, weight, age of onset of menstruation, and the number of months of breastfeeding. According to the importance of personal information in early detection of breast cancer, the efficiency of the Internet of Things in smart cities can be exploitedhttps://www.ijiems.com/article_150324_e09c86aaccabc7e26c6132e3439abd5e.pdf