Faculty & Research

January 2019

Dr. Margaret Plaza's recent article entitled "Decision system supporting optimization of machining strategy"  was published in Computers & Industrial Engineering, external link, the top ranked journal for 2017, external link. 

Abstract

The study offers a decision model that allows to optimize the machining strategy in a virtual environment. The objective of optimization is to balance product quality with process efficiency and to assure the desired level of cost. The decision model allows the managers to explore the possibility of using less expensive grades for challenging applications. Those grades were previously considered unsuitable because of their inconsistent machinability. 

The study was conducted as a collaborative effort between Ryerson University and the Faculty of Mechanical Engineering at Cracow University of Technology.

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December 2018

Read Dr. Aziz Guergachi's recent article entitled "Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques"  published in IEEE Access, external link.

Abstract

The objective of this inductive research was to investigate: (1) the relationship between diabetes mellitus and individual risk factors of Metabolic Syndrome (MetS), in a non-conservative setting; (2) the prediction of future onset of diabetes using relevant risk factors of MetS and; (3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate balanced training sets. The dataset used in this research contains 667907 records for a period ranging from 2003 to 2013. To quantify the contribution of individual risk factors of MetS in the development of diabetes in a non-conservative setting logistic regression analysis was performed. Our analyses contradicts the view that diabetes is commonly associated with low levels of High Density Lipoprotein (HDL). Instead, our results demonstrate that increased levels of HDL are positively correlated with diabetes onset, particularly in women. We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced datasets. The results demonstrated the supremacy of Naïve Bayes with K-medoids under-sampling technique as compared to random under-sampling, oversampling and no sampling. It achieved on average 79% ROC performance with increased true positive rate. The results of this study suggest further research to clarify the pathophysiological significance of HDL and pathways in the development of diabetes.

Read more, external link

December 2018

Read Dr. Aziz Guergachi's recent article entitled "Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques"  published in IEEE Access, external link.

Abstract

The objective of this inductive research was to investigate: (1) the relationship between diabetes mellitus and individual risk factors of Metabolic Syndrome (MetS), in a non-conservative setting; (2) the prediction of future onset of diabetes using relevant risk factors of MetS and; (3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate balanced training sets. The dataset used in this research contains 667907 records for a period ranging from 2003 to 2013. To quantify the contribution of individual risk factors of MetS in the development of diabetes in a non-conservative setting logistic regression analysis was performed. Our analyses contradicts the view that diabetes is commonly associated with low levels of High Density Lipoprotein (HDL). Instead, our results demonstrate that increased levels of HDL are positively correlated with diabetes onset, particularly in women. We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced datasets. The results demonstrated the supremacy of Naïve Bayes with K-medoids under-sampling technique as compared to random under-sampling, oversampling and no sampling. It achieved on average 79% ROC performance with increased true positive rate. The results of this study suggest further research to clarify the pathophysiological significance of HDL and pathways in the development of diabetes.

Read more, external link

November 13, 2018

Hot off the press! This recent article published by Dr. Ana-Maria Herman in the International Journal of Heritage Studies, external link exposes the experimental aspects of (repeat) museum displays - and considers the implications for practitioners.

Abstract

In this paper, I present a case for understanding exhibitionary practices as always experimental. I discuss here a study conducted on the McCord Museum’s MTL Urban Museum App, a digital display that was (re-)made based on the Museum of London’s Streetmuseum App. Drawing on the notion of ‘remediation’ and actor-network theory, I consider the display as formed through the refashioning of an ‘actor-network’, or what I refer to in this paper as an experimental assemblage. This allows me to trace the processes of transformation that brought heterogeneous actors together and into novel arrangements in re-making the App and how such processes resulted in the generation of novel experiences, practices and knowledge. Thus, this study shows that even ‘repeat’ mainstream displays involve experimental processes, or ‘exhibition experiments’. The implication for practitioners in museums, galleries, libraries and other cultural heritage institutions is that decision-making processes must always account for the experimentality of alldisplay practices, ‘new’ or ‘old’.

The findings described in this article are part of a larger study that will be published in a book by Dr. Herman.

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