Asil Oztekin

Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey

Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been widely used to evaluate alternative strategies in order to determine the best one for given business setting. This study aims at providing a quantitative basis to analytically determine the ranking of the factors in SWOT analysis via a conventional multi-criteria decision making method, Analytic Network Process (ANP). The ANP method is preferred in this study because of its capability to model potential dependencies among the SWOT factors. The study presents uniqueness in the way it incorporates inherent vagueness and uncertainty of the human decision making process by means of the fuzzy logic. The proposed SWOT fuzzy ANP methodology was implemented and tested for the Turkish airline industry. The results showed that the SWOT fuzzy ANP is a viable and highly capable methodology that provides invaluable insights for strategic management decisions in the Turkish airline industry, and can also be used as an effective tool for other complex decision making processes.

An RFID network design methodology for asset tracking in healthcare

The purpose of this research is to provide decision makers with a methodology to optimize the design of a medical-asset tracking system constrained by a limited number of RFID readers. Using an enhanced formulation of the maximal covering location problem along with a new criticality index analysis metric (derived from the severity, frequency and dwell time of the critical medical assets) the optimal placement of the limited number of RFID readers is determined. The proposed methodology is implemented in a healthcare facility where the RFID system coverage has improved by 72% compared to the currently utilized expert/heuristic-based placement strategy.

UWIS: An assessment methodology for usability of web-based information systems

A methodology for usability assessment and design of web-based information systems (UWIS) is proposed. It combines web-based service quality and usability dimensions of information systems. Checklist items with the highest and the lowest contribution to the usability performance of a web-based information system can be specified by UWIS. A case study by a student information system at Fatih University is carried out to validate the methodology. UWIS reveals a strong relationship between quality and usability which is assumed to exist by many researchers but not experimentally analyzed yet. This study depicts a strong relevance between web-based service quality and usability of web-based information systems. UWIS methodology can be used for designing more usable and higher quality web-based information systems.

An analytic approach to better understanding and management of coronary surgeries

Demand for high-quality, affordable healthcare services increasing with the aging population in the US. In order to cope with this situation, decision makers in healthcare (managerial, administrative and/or clinical) need to be increasingly more effective and efficient at what they do. Along with expertise, information and knowledge are the other key sources for better decisions. Data mining techniques are becoming a popular tool for extracting information/knowledge hidden deep into large healthcare databases. In this study, using a large, feature-rich, nationwide inpatient databases along with four popular machine learning techniques, we developed predictive models; and using an information fusion based sensitivity analysis on these models, we explained the surgical outcome of a patient undergoing a coronary artery bypass grafting. In this study, support vector machines produced the best prediction results (87.74%) followed by decision trees and neural networks. Studies like this illustrate the fact that accurate prediction and better understanding of such complex medical interventions can potentially lead to more favorable outcomes and optimal use of limited healthcare resources.

Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations

Lung transplantation has a vital role among all organ transplant procedures since it is the only accepted treatment for the end-stage pulmonary failure. There have been several research attempts to model the performance of lung transplants. Yet, these early studies either lack model predictive capability by relying on strong statistical assumptions or provide adequate predictive capability but suffer from less interpretability to the medical professionals. The proposed method described in this paper is focused on overcoming these limitations by providing a structural equation modeling-based decision tree construction procedure for lung transplant performance evaluation. Specifically, partial least squares-based path modeling algorithm is used for the structural equation modeling part. The proposed method is validated through a US nation-wide dataset obtained from United Network for Organ Sharing (UNOS). The results are promising in terms of both prediction and interpretation capabilities, and are superior to the existing techniques. Hence, we assert that a decision support system, which is based on the proposed method, can bridge the knowledge-gap between the large amount of available data and betterment of the lung transplantation procedures.

A machine learning-based usability evaluation method for eLearning systems

The research presented in this paper proposes a new machine learning-based evaluation method for assessing the usability of eLearning systems. Three machine learning methods (support vector machines, neural networks and decision trees) along with multiple linear regression are used to develop prediction models in order to discover the underlying relationship between the overall eLearning system usability and its predictor factors. A subsequent sensitivity analysis is conducted to determine the rank-order importance of the predictors. Using both sensitivity values along with the usability scores, a metric (called severity index) is devised. By applying a Pareto-like analysis, the severity index values are ranked and the most important usability characteristics are identified. The case study results show that the proposed methodology enhances the determination of eLearning system problems by identifying the most pertinent usability factors. The proposed method could provide an invaluable guidance to the usability experts as to what measures should be improved in order to maximize the system usability for a targeted group of end-users of an eLearning system. Usability assessment of eLearning systems is a necessary and challenging problem.Machine learning techniques are effective tools for usability assessments.Pareto-like analysis can help devise severity index values.Sensitivity analysis can help rank the most important usability factors.

Universal structure modeling approach to customer satisfaction index

Purpose – Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much information that would lead to better modeling and understanding the relationship between customer satisfaction and loyalty. The purpose of this paper is to reveal potential nonlinear and interaction effects that might be embedded in antecedents of ECSI by exemplifying it in Turkish telecommunications sector.Design/methodology/approach – This papar has justified the validity and reliability of the ECSI model implementation in Turk Telekom Company. The path models are tested via conventional structural equation modeling (SEM) and using a novel method, i.e. universal structure modeling with Bayesian neural networks.Findings – The findings of this study reveal that quality has the most important impact on customer satisfact...

A decision support system for usability evaluation of web-based information systems

In this study, a decision support system (DSS) for usability assessment and design of web-based information systems (WIS) is proposed. It employs three machine learning methods (support vector machines, neural networks, and decision trees) and a statistical technique (multiple linear regression) to reveal the underlying relationships between the overall WIS usability and its determinative factors. A sensitivity analysis on the predictive models is performed and a new metric, criticality index, is devised to identify the importance ranking of the determinative factors. Checklist items with the highest and the lowest contribution to the usability performance of the WIS are specified by means of the criticality index. The most important usability problems for the WIS are determined with the help of a pseudo-Pareto analysis. A case study through a student information system at Fatih University is carried out to validate the proposed DSS. The proposed DSS can be used to decide which usability problems to focus on so as to improve the usability and quality of WIS.

Criticality index analysis based optimal RFID reader placement models for asset tracking

This study is aimed at optimising the RFID network design in the healthcare service sector for tracking medical assets. Two different optimisation models corresponding to two possible scenarios in RFID network design are developed based on the enhancement of location set covering problem (LSCP) and maximal covering location problem (MCLP). They are validated by considering a healthcare facility to optimise the real-time locating system for tracking assets. The methodology is original in that it analyses the trade-off between cost effectiveness and overall RFID system performance and hence provides possible decision guidance to optimise the RFID system. It is vital for healthcare providers to locate crucial assets in the shortest possible time, particularly in emergency situations where human lives are at risk. Hence, increasing the overall RFID system performance will definitely have a valuable effect on real-time information sharing, thereby decreasing related search time for crucial assets.

Process Performance Prediction for Chemical Mechanical Planarization (CMP) by Integration of Nonlinear Bayesian Analysis and Statistical Modeling

Chemical mechanical planarization (CMP) process has been widely used in the semiconductor manufacturing industry for realizing highly finished (Ra ~ 1 nm) and planar surfaces (WIWNU ~ 1%, thickness standard deviation (SD) ~ 3 nm) of in-process wafer polishing. The CMP process is rather complex with nonlinear and non-Gaussian process dynamics, which brings significant challenges for process monitoring and control. As an attempt to address this issue, a method is presented in this paper that integrates nonlinear Bayesian analysis and statistical modeling to estimate and predict process state variables, and therewith to predict the performance measures, such as material removal rate (MRR), surface finish, surface defects, etc. As an example of performance measure, MRR is chosen to demonstrate the performance prediction. A sequential Monte Carlo (SMC) method, namely, particle filtering (PF) method is utilized for nonlinear Bayesian analysis to predict the CMP process-state and for tackling the process nonlinearity. Vibration signals from both wired and wireless vibration sensors are adopted in the experimental study conducted using the CMP apparatus. The process states captured by the sensor signals are related to MRR using design of experiments and statistical regression analysis. A case study was conducted using actual CMP processing data by comparing the PF method with other widely used prediction approaches. This comparison demonstrates the effectiveness of the proposed approach, especially for nonlinear dynamic processes.