Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields

Abstract: The Gaussian mixture model is a well-known classification tool that captures non-Gaussian statistics of multivariate data. However, the impractically large size of the resulting parameter space has hindered widespread adoption of Gaussian mixture models for hyperspectral imagery. To counter this parameter-space issue, dimensionality reduction targeting the preservation of multimodal structures is proposed. Specifically, locality-preserving nonnegative matrix factorization, as well as local Fisher's discriminant analysis, is deployed as preprocessing to reduce the dimensionality of data for the Gaussian-mixture-model classifier, while preserving multimodal structures within the data. In addition, the pixel-wise classification results from the Gaussian mixture model are combined with spatial-context information resulting from a Markov random field. Experimental results demonstrate that the proposed classification system significantly outperforms other approaches even under limited training data.

Dynamic programming for POMDPs using a factored state representation

Abstract: Contingent planning - constructing a plan in which action selection is contingent, on imperfect information received during plan execution - can be formalized as the problem of solving a partially observable Markov decision process (POMDP). Traditional dynamic programming algorithms for POMDPs use a flat state representation that enumerates all possible stales and state transitions. By contrast, AI planning algorithms use a factored state representation that supports state abstraction and allows problems with large state spaces to be represented and solved more efficiently. Boutilier and Poole (1996) have recently described how a factored state representation can be exploited by a dynamic programming algorithm for POMDPs. We extend their framework, describe an implementation of it, test its performance, and assess how much this approach improves the computational efficiency of dynamic programming for POMDPs.

Qualitative Research in Sport Management: Case Study as a Methodological Approach

Abstract: This paper presents qualitative research in sport management and suggests that case study is an appropriate qualitative methodology for research and practice in sport finance. The purpose of qualitative methodology is presented along with the process of a case study. The intention of this paper, for academicians working in sport management, is twofold. The first aspect is for researchers to consider using qualitative case study methodology in instances where such practice will progress the knowledge and understanding of specific situations while invoking a deeper response to research questions. The second facet of this paper focuses on the framework of case study methodology, as applied to a research project in the field of sport finance. Keywords: Qualitative, case study, sport management, finance, ticket pricing, baseball.

The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

Abstract: The lack of information to manage groundwater for irrigation is one of the biggest concerns for farmers and stakeholders in agricultural areas of Mississippi. In this study, we present a novel implementation of a nonlinear autoregressive with exogenous inputs (NARX) network to simulate daily groundwater levels at a local scale in the Mississippi River Valley Alluvial (MRVA) aquifer, located in the southeastern United States. The NARX network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, and the results were compared to identify an optimal architecture for the forecasting of daily groundwater levels over time. The training algorithms were implemented using different hidden node combinations and delays (5, 25, 50, 75, and 100) until the optimal network was found. Eight years of daily historical input time series including precipitation and groundwater levels were used to forecast groundwater levels up to three months ahead. The comparison between LM and BR showed that NARX-BR is superior in forecasting daily levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency. The results showed that BR with two hidden nodes and 100 time delays provided the most accurate prediction of groundwater levels with an error of ± 0.00119 m. This innovative study is the first of its kind and will provide significant contributions for the implementation of data-based models (DBMs) in the prediction and management of groundwater for agricultural use.