Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

A hybrid approach to achieve organizational agility: An empirical study of a food company

Purpose: In today’s intense global competition, agility is advocated as a fundamental characteristic for business survival and competitiveness. The purpose of this paper is to propose a practical methodology to achieve and enhance organizational agility based on strategic objectives. Design/methodology/approach: In the first step, a set of key performance indicators (KPIs) of the organization being studied are recognized and classified under the perspectives of balanced scorecard (BSC). Critical success factors are then identified by ranking the KPIs according to their importance in achieving organizational strategic objectives using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In the second step, three houses of quality (HOQs) are constructed sequentially to identify and rank the main agile attributes, agile enablers, and improvement paths. In addition, in order to translate linguistics judgments of practitioners into numerical values in building HOQs, fuzzy logic is employed. Findings: The capability of the proposed methodology is demonstrated by applying it to a case of a multi-national food company in Iran. Through the application, the company could find the most suitable improvement paths to improve its organizational agility. Research limitations/implications: A limited number of KPIs were chosen due to computational and visual constraints related to HOQs. Another limitation, similar to other agility studies, which facilitate decision making among agility metrics, was that the metrics were more industry-specific and less inclusive. Practical implications: A strong practical advantage for the application of the methodology over directly choosing agility metrics without linking them is that through the methodology, the right metrics were selected that match organization’s core values and marketing objectives. While metrics may ostensibly seem unrelated or inappropriate, they actually contributed to the right areas where there were gaps between the current and desired level of agility. It would otherwise be impossible to choose the right metrics without a structured methodology. Originality/value: This paper proposes a novel methodology for achieving organizational agility. By utilizing and linking several tools such as BSC, fuzzy TOPSIS, and quality function deployment (QFD), the proposed approach enables organizations to identify the most appropriate agile attributes, agile enablers, and subsequently agile improvement paths

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

PERFORMANCE EVALUATION ON QUALITY OF ASIAN AIRLINES WEBSITES – AN AHP PPROACH

In recent years, many people have devoted their efforts to the issue of quality of Web site. The concept of quality is consisting of many criteria: quality of service perspective, a user perspective, a content perspective or indeed a usability perspective. Because of its possible instant worldwide audience a Website’s quality and reliability are crucial. The very special nature of the web applications and websites pose unique software testing challenges. Webmasters, Web applications developers, and Website quality assurance managers need tools and methods that can match up to the new needs. This research conducts some tests to measure the quality web site of Asian flag carrier airlines via web diagnostic tools online. We propose a methodology for determining and evaluate the best airlines websites based on many criteria of website quality. The approach has been implemented using Analytical Hierarchy Process (AHP) to generate the weights for the criteria which are much better and guarantee more fairly preference of criteria. The proposed model uses the AHP pairwise comparisons and the measure scale to generate the weights for the criteria which are much better and guarantee more fairly preference of criteria. The result of this study confirmed that the airlines websites of Asian are neglecting performance and quality criteria

The Principles Of Developing A Management Decision Support System For Scientific Employees

Employees engaged in mental work have become the most valuable assets of any organization in the 21st century. The satisfaction of those involved in mental work requires the provision of objectivity and transparency in their decision-making. This, in turn, entails the development of scientifically motivated decision making mechanisms and scientific-methodological approaches to evaluate their performance based on innovative technologies.The main goal of this article is in development of the scientific and methodological framework for the establishment of a decision support system to manage the employees engaged in mental work and operating in uncertainty. In this regard, initially, the question of evaluating the activities of scientific workers is examined, its characteristic features are determined, and the fuzzy relation model is proposed as a multi-criterion issue formed in uncertainty. Taking into consideration the hierarchical structure of the criteria that allows evaluating the activities of scientific workers, a phased solution method based on an additive aggregation method is proposed. In accordance with the methodology, a functional scheme of the decision support system to manage the scientific personnel is developed. The working principle of each block and the interaction of the blocks are described. The rules for the employees\u27 management decisions are shown by referring to the knowledge production model.Based on the proposed methodological approach, the implementation phases of the decision support system for the management of the scientific workers of the Institute of Information Technology of ANAS are described. To evaluate the employees\u27 performance, the tools to collect initial information, evaluate the system of criteria, define their importance coefficients and mathematical descriptions are provided. Some results of the system software are presented. The opportunities of the system based on the proposed methodology to support enterprise mangers to make scientifically justified decisions are provided