Browsing by Subject "Artificial neural networks"
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Item A landscape approach to reserving farm ponds for wintering bird refuges in Taoyuan, Taiwan(Texas A&M University, 2006-08-16) Fang, Wei-TaMan-made farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing and bring with it the loss of this refuge function. It is ecologically significant because one fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon-Wiener diversity index (H??). Results indicate that ponds with regular shape or the ones with larger size possess a strong positive correlation with H??. Farm ponds adjacent to farmland benefited waterside bird diversity. On the other hand, urban development was shown to cause the reduction of farmland and pond numbers, which in turn reduced waterside bird diversity. By running the ANN model with four neurons, the resulting H?? index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28). Analysis of historical pond occurrence to the present showed that ponds with larger size and a long perimeter were less likely to disappear. Smaller (< 0.1 ha) and more curvilinear ponds had a more drastic rate of disappearance. Based on this finding, a logistic regression was constructed to predict pond-loss likelihood in the future and to help identify ponds that should be protected. Overlaying results from ANN and form logistic regression enabled the creation of pond-diversity maps for these simulated scenarios of development intensities with respective to pond-loss trends and the corresponding dynamics of bird diversity.Item Forecasting of sick leave usage among nurses via artificial neural networks(2010-12) Tondukulam Seeth, Srikanth; Hasenbein, John J.; Popova, ElmiraThis report examines the trends in sick leave usage among nurses in a hospital and aims at creating a forecasting model to predict sick leave usage on a weekly basis using the concept of artificial neural networks (ANN). The data used for the research includes the absenteeism (sick leave) reports for 3 years at a hospital. The analysis shows that there are certain factors that lead to a rise or fall in the weekly sick leave usage. The ANN model tries to capture the effect of these factors and forecasts the sick leave usage for a 1 year horizon based on what it has learned from the behavior of the historical data from the previous 2 years. The various parameters of the model are determined and the model is constructed and tested for its forecasting ability.Item The uses of supramolecular chemistry in synthetic methodology development(2009-05) Shabbir, Shagufta Hasnain; Anslyn, Eric V., 1960-Enantioselective indicator displacement assays (eIDAs), was transitioned to a high-throughput screening protocols, for the rapid determination of concentration and enantioselectivity (ee) of chiral diols and α-hydroxycarboxylic acid. To improve the design of our previously established receptor based on o-(N,N-dialkylaminomethyl)arylboronate scaffolds for eIDAs. The rigidity of the receptor, which pertinent from the formation of an intramolecular N-B dative bond was investigated. o-(Pyrrolidinylmethyl)phenylboronic acid its complexes with bifunctional substrates such as catechol, [alpha]-hydroxyisobutyric acid, and hydrobenzoin was studied in detail by x-ray crystallography and ¹¹B NMR. Our structural study predicts that the formation of an N-B dative bond, and/or solvolysis to afford a tetrahedral boronate anion, depends on the solvent and the complexing substrate present. To simplify the operation of eIDAs, we introduced an analytical method, which utilize a dual-chamber quartz cuvette, which reduces the number of spectroscopic measurements from two to one and introduced artificial neural networks (ANNs) which simplifies data analysis. In a second example a high-throughtput screening protocol for hydrobenzoin was developed. The method involves the sequential utilization of what we define herein as screening, training, and analysis plates. Several enantioselective boronic-acid based receptors were screened using 96-well plates, both for their ability to discriminate the enantiomers of hydrobenzoin and to find their optimal pairing with indicators resulting in the largest optical responses. The best receptor/indicator combination was then used to train an ANN to determine concentration and ee. To prove the practicality of the developed protocol, analysis plates were created containing true unknown samples of hydrobenzoin generated by established Sharpless asymmetric dihydroxylation reactions, and the best ligand was correctly identified. The system was extended to pattern recognition for the rapid determination of identity, concentration, and ee of chiral vicinal diols. A diverse enantioselective sensor array was generated with three chiral boronic acid receptors and pH indicators. The optical response produced by the sensor array, was analyzed by two pattern recognition algorithms: principal component analysis (PCA) and ANNs. The PCA plot demonstrated good chemoselective and enantioselective separation of the analytes, and ANNs was used to accurately determine the concentration and ee of five unknown samples.Item Utilizing symmetry in evolutionary design(2010-08) Valsalam, Vinod K.; Miikkulainen, RistoCan symmetry be utilized as a design principle to constrain evolutionary search, making it more effective? This dissertation aims to show that this is indeed the case, in two ways. First, an approach called ENSO is developed to evolve modular neural network controllers for simulated multilegged robots. Inspired by how symmetric organisms have evolved in nature, ENSO utilizes group theory to break symmetry systematically, constraining evolution to explore promising regions of the search space. As a result, it evolves effective controllers even when the appropriate symmetry constraints are difficult to design by hand. The controllers perform equally well when transferred from simulation to a physical robot. Second, the same principle is used to evolve minimal-size sorting networks. In this different domain, a different instantiation of the same principle is effective: building the desired symmetry step-by-step. This approach is more scalable than previous methods and finds smaller networks, thereby demonstrating that the principle is general. Thus, evolutionary search that utilizes symmetry constraints is shown to be effective in a range of challenging applications.