Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Finally, we propose a Bayesian method of estimating high-dimensional covariance matrices that can be decomposed into a low rank and sparse component. This covariance structure has a wide range of applications including factor analytical model and random effects model. We model the covariance matrices with the decomposition structure by representing the covariance model in the form of a factor analytic model where the number of latent factors is unknown. We introduce binary indicators for estimating the rank of the low rank component combined with a Bayesian graphical lasso method for estimating the sparse component. We further extend our method to a graphical factor analytic model where the graphical model of the residuals is of interest. We achieve sparse estimation of the inverse covariance of the residuals in the graphical factor model by employing a hyper-inverse Wishart prior method for a decomposable graph and a Bayesian graphical lasso method for an unrestricted graph.

Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo / Matlab R 2007b&rdquo / software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. This dissertation is motivated by the observation that a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data is necessary. We present a formal framework based on the relational data model that systematically specifies the construction and transformation of graphs from relational data tables. In the framework, a set of relational operators provide the basis for rich expressive power for network modeling. Powered by this relational algebraic framework, we design and implement a visual analytics system called Ploceus. Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience.

The work presented in this dissertation informs on river network modeling at large scales using geographic information systems, parallel computing and the latest advancements of atmospheric and land surface modeling. This work is motivated by the availability of a vector-based Geographic Information System dataset that describes the networks of streams and rivers in the United States, and how they are connected. A land surface model called Noah-distributed is used to provide lateral inflow to an NHDPlus river network in the Guadalupe River Basin in Texas. Challenges related to the projection of gridded hydrographic data from a coordinate system to another are investigated. The different representations of the shape of the Earth used in atmospheric science (spherical) and hydrology (spheroidal) can lead to a significant North-South shift on the order of 20 km at mid latitudes. A river network model called RAPID is developed and applied in a four-year study of the Guadalupe and San Antonio River Basins in Texas using the river network of NHDPlus. Gage measurements are used to estimate flow wave celerities in a river network and to assess the quality of RAPID flow computations. The performance of RAPID in a massively-parallel computing environment is tested and further investigation of its scalability is needed before using RAPID at the state or federal level. The replacement by RAPID of the river routing scheme used in SIM-France -- a hydro-meteorological model -- is investigated in a ten-year study of river flow in France. While the formulation of RAPID improves the functionality of SIM-France, the flow simulations are comparable in accuracy to those previously obtained by SIM-France. Sub-basin parameterization was found to improve model results. A single criterion for quantifying the quality of river flow simulations using several river gages globally in a river network is developed that normalizes the square error of modeled flow to allow equal treatment of all gaging stations regardless of the magnitude of flow. The use of this criterion as the cost function for parameter estimation in RAPID allows better results than by increasing the degree of spatial variability in model parameters. / text

This thesis presents a preliminary design of a fully articulated five-fingered anthropomorphic human hand prosthesis with particular emphasis on the controller and actuator design. The proposed controller is a modified artificial neural network PID-based controller with application to the nonlinear and highly coupled dynamics of the hand prosthesis. The new solid state actuator has been designed based on electroactive polymers, which are a type of material that exhibit electromechanical behavior and a liquid metal alloy acts as the electrode. The solid state actuators reduce the overall mechanical complexity, risk failure and required maintenance of the prosthesis. / Graduate

Current activated sludge models are deterministic in character and are constructed by basing on the fundamental biokinetics. However, calibrating these models are extremely time consuming and laborious. An easy-to-calibrate and user friendly computer model, one of the artificial intelligence techniques, Artificial Neural Networks (ANNs) were used in this study. These models can be used not only directly as a substitute for deterministic models but also can be plugged into the system as error predictors. Three systems were modeled by using ANN models. Initially, a hypothetical wastewater treatment plant constructed in Simulation of Single-Sludge Processes for Carbon Oxidation, Nitrification &amp / Denitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.

The thesis presents a survey of techniques for accurate prediction of traffic distribution in computer network systems.

This thesis deals with simulation of routing protocol OSPF in simulation software called OMNeT++. OMNeT++ is a discrete modular simulator mostly used for simulation of computer networks. This thesis includes theory needed for an understanding of the functionality of OSPFv2 and changes in OSPFv3 for IPv6, which are implemented in the model itself. Moreover, thesis contains the configuration of OSPFv3 protocol on topology created from Cisco devices following by analysis of previous source files, state of implementation and its further extension. Thesis is finished with functionality testing and evaluation of results.

This Master's thesis deals with modeling and simulation of BGP protocol within the OMNeT++ environment. The BGP protocol is described with employed data structures and the finite state machine of BGP peering. Next, the basic configuration is outlined involving the setup of the BGP protocol on Cisco devices. Further, BGP for OMNeT++ state-of-the-art is investigated together with its lack of functionality and issues. The second part of this thesis deals with design, implementation, and testing of the new functionality of BGP protocol and simulation models. The last section describes the overall achieved results.

In this research project, we introduced an agent-based banking system based on systematic trust. The features of the model and attributes of the agents are defined and analyzed precisely, and the results are explained. Some of this model's features include but are not limited to considering the savings system, insurance deposits, the impact of the Central Bank loans, and correlated regional shocks in a banking system. Different Scenarios are applied. The results indicate that by having the Central Bank loans in the model, the banking system experience dramatically fewer failures. Even if some correlated regional shocks occur, the system can be more stable than when the Central Bank does not exist. Moreover, the trust system establishes and forms during different financial periods based on the bank's clients’ point of view about the bank's performance as an intelligent system to attract more capital for the system by providing some information for the agents to join the more prestigious banks. Conclusively, in the early financial periods, banks need more financial supports to support the clients’ deposits and to make their reputation for attracting more clients; hence the Central Bank is an essential parameter to help the banks to be more stable and supports the banks in their early stages of growth. The Central Bank loans would be significantly important in panic times, such as regional correlated preference shocks. / Thesis / Master of Science (MSc)