Colleges and Department Research
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Research, creative, and scholarly works created by the university community organized by college.
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Browsing Colleges and Department Research by Department "Business Administration"
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Item A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations(Multidisciplinary Digital Publishing Institute, 2021-02-19) Li, Yue; Mendez-Mediavilla, Francis A.; Temponi, Cecilia; Kim, Junwoo; Jimenez, JesusMost large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.Item Deep Vision for Breast Cancer Classification and Segmentation(Multidisciplinary Digital Publishing Institute, 2021-10-27) Fulton, Lawrence V.; McLeod, Alexander; Dolezel, Diane; Bastian, Nathaniel; Fulton, Christopher P.(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.Item Measurement Invariance Tests of Revisions to Archaically Worded Items in the Mach IV Scale(Public Library of Science, 2019-10) Miller, Brian K.; Miller, Brian K.; Nicols, Kay M.; Nicols, Kay M.; Konopaske, Robert; Konopaske, RobertThe Machiavellian IV [1] instrument, developed almost 50 years ago to measure trait Machiavellianism and still in wide use in personality research, uses item wording that is not gender-neutral, makes use of idiomatic expressions, and includes archaic references. In this two-sample study, exploratory factor analysis (EFA) was conducted on one sample to examine the structure of responses to the Mach IV. In an independent second sample the resulting EFA structure was analyzed using confirmatory factor analysis-based measurement equivalence/invariance (ME/I) tests in a control group with the original archaic items and a treatment group with eight items rewritten in a more modern vernacular. Specific model testing steps [2] and statistical tests [3] were applied in a bottom-up approach [4] to ME/I tests on these two versions of the Mach IV. The two versions were found to have equal form, equal factor loadings, but unequal indicator error variances. Subsequent item-by-item tests of error invariance resulted in substantial decrements to fit for three revised items suggesting that the error associated with these items was not equal across the two versions.Item The Impact of Firm Characteristics and IT Governance on IT Material Weaknesses(IGI Global, 2018-04) Zhang, Peiqin; Zhao, Kexin; Kumar, Ram L.Accurate and timely reporting of organizational performance is becoming increasingly important and highly regulated. However, organizations face a variety of challenges in seeking to provide accurate and reliable information due to the existence of IT control problems. Hence it is important for end users including auditors and managers to understand how to manage IT material weaknesses ITMWs. While there is extensive accounting research on general material weaknesses MWs, ITMWs are under researched. This article identifies key firm characteristics that appear to be related to ITMWs. In addition, the authors suggest that IT governance may help firms mitigate such problems. To gain a deeper understanding of IT governance effects, this article proposes a model which includes an innovative construct, ITGOV, operationalized using secondary data. The authors empirically validate the proposed model based on a data set of 1,112 firms. Their study illustrates the differences between ITMWs and general MWs. These results can also help end users computing by offering insights into better management of ITMWs.