Rule-based expert systems for supporting university students

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Rule-based expert systems for supporting university students

There are more than 15 million college students in the US alone. Academic advising for courses and scholarships is typically performed by human advisors, bringing an immense managerial workload to faculty members, as well as other staff at universities. This paper reports and discusses the development of two educational expert systems at a private international university. The first expert system is a course advising system which recommends courses to undergraduate students. The second system suggests scholarships to undergraduate students based on their eligibility. While there have been reported systems for course advising, the literature does not seem to contain any references to expert systems for scholarship recommendation and eligibility checking. Therefore the scholarship recommender that we developed is first of its kind. Both systems have been implemented and tested using Oracle Policy Automation (OPA) software.

Engin, G., Aksoyer, B., Avdagic, M., Bozanlı, D., Hanay, U., Maden, M., Ertek, G. (2014) Rule-based expert systems for supporting university students. In Proceedings of 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. Procedia Computer Science 31 ( 2014 ) 22 – 31.

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Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis

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Risk Factors and Identifiers for Alzheimer’s Disease:  A Data Mining Analysis

The topic of this paper is the Alzheimer’s Disease (AD), with the goal being the analysis of risk factors and identifying tests that can help diagnose AD. While there exists multiple studies that analyze the factors that can help diagnose or predict AD, this is the first study that considers only non-image data, while using a multitude of techniques from machine learning and data mining. The applied methods include classification tree analysis, cluster analysis, data visualization, and classification analysis. All the analysis, except classification analysis, resulted in insights that eventually lead to the construction of a risk table for AD. The study contributes to the literature not only with new insights, but also by demonstrating a framework for analysis of such data. The insights obtained in this study can be used by individuals and health professionals to assess possible risks, and take preventive measures.

Ertek, G., Tokdil, B., Günaydın, İ. “Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis”. In Proceedings of Industrial Conference on Data Mining (ICDM 2014), Springer. Ed: Petra Perner (2014)

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Optimizing the electric charge station network of ESARJ

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Model solution, for scenario-model combination A4. Small circles show the districts, cross out signs (x) show unused locations (yj=0), and filled circles show selected locations (yj=1).
Model solution, for scenario-model combination A4. Small circles show the districts, cross out signs (x) show unused locations (yj=0), and filled circles show selected locations (yj=1).

 

In this study, we adopt the classic capacitated p-median location model for the solution of a network design problem, in the domain of electric charge station network design, for a leading company in Turkey. Our model encompasses the location preferences of the company managers as preference scores incorporated into the objective function. Our model also incorporates the capacity concerns of the managers through constraints on maximum number of districts and maximum population that can be served from a location. The model optimally selects the new station locations and the visualization of model results provides additional insights.

Gavranović, H., Barut, A., Ertek, E., Yüzbaşıoğlu, O.B., Pekpostalcı, O., Tombuş, O., Optimizing the electric charge station network of EŞARJ. In Proceedings of 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. Procedia Computer Science 31 ( 2014 ) 15 – 21.

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Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization

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Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization
Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization

 

This study presents a comprehensive benchmarking study of the top 100 U.S. Universities. The methodologies used to come up with insights into the domain are Data Envelopment Analysis (DEA) and information visualization. Various approaches to evaluating academic institutions have appeared in the literature, including a DEA literature dealing with the ranking of universities. Our study contributes to this literature by the extensive incorporation of information visualization and subsequently the discovery of new insights. The main purpose of the study is creating an objective basis of assessment for the candidate students to use for university preferences. Meanwhile, the actionable insights obtained for the domain can guide university managers, as well as candidate students.

Ertek G., Tokdil, B., Günaydın, İ., Göğüş, A. (2014) Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization, in I. Osman, A.L. Anouze and A. Emrouznejad (eds.) Strategic Performance Management and Measurement Using Data Envelopment Analysis, DOI: 10.4018/978-1-4666-4474-8, ISBN13: 9781466644748, IGI_Global (In Press).

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Industrial Benchmarking through Information Visualization and Data Envelopment Analysis: A New Framework

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The proposed analysis framework.
The proposed analysis framework.

 

We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.

Ertek G., Sevinç, M., Ulus, F., Köse, Ö., Şahin, G. (2014) “Industrial Benchmarking through Information Visualization and Data Envelopment Analysis: A New Framework”, in I. Osman, A.L. Anouze and A. Emrouznejad (eds.) Strategic Performance Management and Measurement Using Data Envelopment Analysis, DOI: 10.4018/978-1-4666-4474-8, ISBN13: 9781466644748, IGI_Global (In Press).

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