Mehdi Ghazanfari

Bio: Mehdi Ghazanfari is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Fuzzy logic & Supply chain. The author has an hindex of 20, co-authored 83 publications receiving 1320 citations. Previous affiliations of Mehdi Ghazanfari include University of Qom & University of Montenegro.

Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS

Abstract: Evaluation of business intelligence for enterprise systems before buying and deploying them is of vital importance to create decision support environment for managers in organizations. This study aims to propose a new model to provide a simple approach to assess enterprise systems in business intelligence aspects. This approach also helps the decision-maker to select the enterprise system which has suitable intelligence to support managers' decisional tasks. Using wide literature review, 34 criteria about business intelligence specifications are determined. A model that exploits fuzzy TOPSIS technique has been proposed in this research. Fuzzy weights of the criteria and fuzzy judgments about enterprise systems as alternatives are employed to compute evaluation scores and ranking. This application is realized to illustrate the utilization of the model for the evaluation problems of enterprise systems. On this basis, organizations will be able to select, assess and purchase enterprise systems which make possible better decision support environment in their work systems.

A novel multi-objective forest optimization algorithm for wrapper feature selection

Abstract: Feature selection is one of the important techniques of dimensionality reduction in data preprocessing because datasets generally have redundant and irrelevant features that adversely affect the performance and complexity of classification models. Feature selection has two main objectives, i.e., reducing the number of features and increasing classification performance due to its inherent nature. In this paper, we propose a multi-objective feature selection algorithm based on forest optimization algorithm (FOA) using the archive, grid, and region-based selection concepts. For this purpose, two versions of the proposed algorithm are developed using continuous and binary representations. The performance of the proposed algorithms is investigated on nine UCI datasets and two microarray datasets. Next, the obtained results are compared with seven traditional single-objective and five multi-objective methods. Based on the results, both proposed algorithms have reached the same performance or even outperformed the single-objective methods. Compared with other multi-objective algorithms, MOFOA with continuous representation has managed to reduce the classification error in most cases by selecting less number of features than other methods.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014

Abstract: Two decades was systematically reviewed on fuzzy MCDM techniques from 1994 to 2014.The database for review was 403 papers from more than 150 high-ranking journals.403 scholarly papers were grouped in four different main fields.Papers were classified based on utilizing, developing and proposing research papers. MCDM is considered as a complex decision-making tool involving both quantitative and qualitative factors. In recent years, several fuzzy FMCDM tools have been suggested to choosing the optimal probably options. The purpose of this paper is to review systematically the applications and methodologies of the fuzzy multi decision-making (FMCDM) techniques. This study reviewed a total of 403 papers published from 1994 to 2014 in more than 150 peer reviewed journals (extracted from online databases such as ScienceDirect, Springer, Emerald, Wiley, ProQuest, and Taylor & Francis). According to experts' opinions, these papers were grouped into four main fields: engineering, management and business, science, and technology. Furthermore, these papers were categorized based on authors, publication date, country of origin, methods, tools, and type of research (FMCDM utilizing research, FMCDM developing research, and FMCDM proposing research). The results of this study indicated that, in 2013, scholars have published papers more than other years. In addition, hybrid fuzzy MCDM in the integrated method and fuzzy AHP in the individual section were ranked as the first and second methods in use. Additionally, Taiwan was ranked as the first country that contributed to this survey, and engineering was ranked as the first field that has applied fuzzy DM tools and techniques.