Thesis (M.S. in Mechanical Engineering and M.S. in Information Technology Management)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Joshua Gordis, Dan Boger. Includes bibliographical references (p. 85-86). Also available online.

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains ix, 161 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 49).

Thesis (D. Min.)--Midwestern Baptist Theological Seminary, 2003. / "May 1, 2003." Includes bibliographical references (leaves 117-118).

M. Tech. Business Administration / An Enterprise Resource Planning system is complex software that ties together and automates the processes in a business. The benefits of having an Enterprise Resource Planning system included having a single database that allowed the data to be available across all business units. Small, Medium Enterprises, and Large South African companies, have introduced Enterprise Resource Planning (ERP) systems as a means of improving productivity and efficiency over the past few years. Various South African companies have experienced difficulties in the implementation of Enterprise Resource Planning systems because they are costly in terms of finance and time. This study aims to analyse the degree to which the South African company Iliad Africa has benefited from the implementation of an ERP system.

This thesis focuses on the identification of experience required for solving IT (Information Technology) problems in small to medium sized enterprises. It is aimed to utilize information retrieval and data mining techniques to automatically extract information from publicly available experience data on the internet to automatically generate a knowledgebase for dynamic IT management support. In this thesis, similarity distance measures as Jaccard Index, Cosine Similarity Measure and clustering algorithms as K-Means, EM, DBScan, CES, CES+ are employed on three different datasets to evaluate their performances. CES+ algorithm gives the highest performance results in these evaluations. Moreover, Multi Objective Genetic Algorithm (MOGA) is used and is evaluated on three different data sets to aid the usage of CES+ in real life senarios by automating the selection of necessary parameters. Results show that MOGA support is not only automating the CES+, it also provides higher performance results.

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A Decision Support System for the design and planning of soil conservation systems on a watershed scale was conceived and applied to southwestern Quebec. The system integrated Geographical Information System, distributed parameter hydrologic modeling and Expert System technologies. Maps of appropriate soil conservation practices were produced for two small rural basins representative of the study region. The effect of the selected practices on runoff and sediment was assessed using the ANSWERS model. Erosion sites were targeted using a once-in-25 year design storm. It was observed that small portions of the study basins produced large amounts of eroded sediment. The expert system was designed to select appropriate conservation practices for the 1-ha cells which had more than one ton of erosion as a result of the design storm. The results demonstrated that the selected conservation practices would reduce sediment yield and average erosion rates by 50% in each of the study basins.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 132-137). / Microfiche. / xviii, 137 leaves, bound maps (some col.) 29 cm