Iowa Technology Governance Board Annual Report, January 10, 2011

This Technology Governance Board Annual Report provides information on the FY08 – FY12 Information Technology Personnel Spending; FY08 – FY12 Technology Equipment and Services Spending; and FY08 – FY12 Internal IT Expenditures with the Iowa Communications Network and Department of Administrative Services - Information Technology Enterprise. The report also contains a projection of technology cost savings. This report was produced in compliance with Iowa Code §8A.204(3a) and was submitted to the Governor, the Department of Management, and the General Assembly on January 10, 2011.

Annual Report of the Iowa Highway Research Board Research and Development Activities FY 2010

The Highway Division of the Iowa Department of Transportation (Iowa DOT) engages in research and development for two reasons: first, to find workable solutions to the many problems that require more than ordinary, routine investigation; and second, to identify and implement improved engineering and management practices. This report, entitled ―Iowa Highway Research Board Research and Development Activities FY2009‖ is submitted in compliance with Sections 310.36 and 312.3A, Code of Iowa, which direct the submission of a report of the Secondary Road Research Fund and the Street Research Fund, respectively.

Audit report on the Iowa Corn Promotion Board for the years ended August 31, 2010 and 2009

Audit report on the Iowa Corn Promotion Board for the years ended August 31, 2010 and 2009

Reliure de : Le Diverse et artificiose machine del capitano Agostino Ramelli dal ponte della Tresia... Nelle quali si contengono varii et industriosi movimenti... composte in lingua italiana et francese

Numérisation partielle de reliure

Annual Report of the Hawk-i Board to the Governor, 2007

Annual Report of the hawk-i Board to the Governor

Annual Report of the Hawk-i Board to the Governor, 2008

Annual Report of the hawk-i Board to the Governor

Annual Report of the Hawk-i Board to the Governor, 2010

Annual Report of the hawk-i Board to the Governor

Annual Report of the Hawk-i Board to the Governor, 2009

Annual Report of the hawk-i Board to the Governor

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2010

Audit report of the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2010

Report of recommendations to the Public Employment Relations Board for the year ending, June 30, 2010

Report of recommendations to the Public Employment Relations Board for the year ending, June 30, 2010

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Report of Recommendations to the Board of Regents for the year ended June 30, 2010

Report on the Board of Regents for the year ended June 30, 2010