Shreveport, LA, United States
Shreveport, LA, United States

Louisiana State University in Shreveport is a branch institution of the Louisiana State University System located in Shreveport, Louisiana. LSUS opened in 1967 as a two-year community college but transitioned into a four-year college five years later in 1972. LSUS enrolled 4,051 students in the Fall 2014 semester and is accredited by the Southern Association of Colleges and Schools. The school's athletic programs, nicknamed the Pilots, are members of the National Association of Intercollegiate Athletics and the Red River Athletic Conference. LSUS offers more than 70 extra-curricular organizations. LSUS operates Red River Radio, a public radio network based in Shreveport. Wikipedia.

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Davidrajuh R.,University of Stavanger | Lin B.,Louisiana State University in Shreveport
Expert Systems with Applications | Year: 2011

The first part of the paper introduces a novel tool for modeling and simulation of discrete event system. This tool called GPenSIM is a Petri net based simulator and offers significant benefits to model builders such as flexibility to include diverse libraries, ease of extending the models, and ease of programming. The second part of the paper presents a case study on modeling and optimization of airport traffic management; this study is to explore air traffic management capability of Evenes airport in Norway. The case study shows that with GPenSIM, modeling and simulation problems of large industrial discrete event systems can be done. Future research directions are discussed as well. © 2011 Elsevier Ltd. All rights reserved.

Celebi M.E.,Louisiana State University in Shreveport | Zornberg A.,Half Hollow Hills High School West
IEEE Systems Journal | Year: 2014

Dermoscopy is a noninvasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. Color information is indispensable for the clinical diagnosis malignant melanoma, the most deadly form of skin cancer. For this reason, most of the currently accepted dermoscopic scoring systems either directly or indirectly incorporate color as a diagnostic criterion. For example, both the asymmetry, border, colors, and dermoscopic (ABCD) rule of dermoscopy and the more recent color, architecture, symmetry, and homogeneity (CASH) algorithm include the number of clinically significant colors in their calculation of malignancy scores. In this paper, we present a machine learning approach to the automated quantification of clinically significant colors in dermoscopy images. Given a true-color dermoscopy image with N colors, we first reduce the number of colors in this image to a small number K, i.e., K N, using the K-means clustering algorithm incorporating a spatial term. The optimal K value for the image is estimated separately using five commonly used cluster validity criteria. We then train a symbolic regression algorithm using the estimates given by these criteria, which are calculated on a set of 617 images. Finally, the mathematical equation given by the regression algorithm is used for two-class (benign versus malignant) classification. The proposed approach yields a sensitivity of 62% and a specificity of 76% on an independent test set of 297 images. © 2014 IEEE.

Hwang S.,University of Illinois at Springfield | Celeb M.E.,Louisiana State University in Shreveport
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2010

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. One of the most important goals of WCE is the early detection of colorectal polyps. In this paper an unsupervised method for the detection of polyps in WCE videos is presented. Our method involves watershed segmentation with a novel initial marker selection method based on Gabor texture features and K-means clustering. Geometric information from the resulting segments is extracted to identify polyp candidates. Initial experiments indicate that the proposed method can detect polyps with 100% sensitivity and over 81% specificity. ©2010 IEEE.

Munker R.,University of Houston | Munker R.,Louisiana State University in Shreveport | Calin G.A.,University of Houston
Clinical Science | Year: 2011

The diagnosis of cancer has undergone major changes in the last 40 years. Once based purely on morphology, diagnosis has come to incorporate immunological, cytogenetic and molecular methods. Many cancers, especially leukaemias, are now defined by molecular markers. Gene expression profiling based on mRNA has led to further refinement of the classification and diagnosis of cancer. More recently, miRNAs (microRNAs), among other small non-coding RNA molecules, have been discovered and found to be major players in cell biology. miRNAs, having both oncogenic and tumour-suppressive functions, are dysregulated in many types of cancer. miRNAs also interfere with metastasis, apoptosis and invasiveness of cancer cells. In the present review, we discuss recent advances in miRNA profiling in human cancer. We discuss both frequent and rare tumour types and give an outlook on future developments. © The Authors Journal compilation. © 2011 Biochemical Society.

Celebi M.E.,Louisiana State University in Shreveport | Kingravi H.A.,Georgia Institute of Technology | Vela P.A.,Georgia Institute of Technology
Expert Systems with Applications | Year: 2013

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods. © 2012 Elsevier Ltd. All rights reserved.

Celebi M.E.,Louisiana State University in Shreveport
Image and Vision Computing | Year: 2011

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer. © 2010 Elsevier B.V. All rights reserved.

Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 152.69K | Year: 2010

This NSF MRI Award funds the acquisition of a high performance liquid chromatography (HPLC) system to significantly improve infrastructure for research, research training, and education in the biological sciences in the Department of Biological Sciences at Louisiana State University in Shreveport (LSUS). The HPLC system is used to support a range of research projects, including: abscisic acid (ABA) concentrations and antioxidant concentrations in salt-stressed plants; agricultural chemical residues in constructed wetlands; ecological chemistry and carotenoids; molecular genetics of yeast; ecological chemistry and metabolites; and evolutionary and systematic botany. In addition to providing modern HPLC capabilities for research, the new system allows the Department to advance education and training objectives by teaching students to use and apply the latest HPLC procedures in a laboratory and classroom environment. LSUS, an institution that serves significant numbers of students from under-represented groups, has a long history of involving undergraduates in research projects within the Department of Biological Sciences. Research training and education supported by the new HPLC system will contribute to development of a skilled workforce in Louisiana (an EPSCoR state) and the nation. The results of the research and teaching efforts will be broadly disseminated through abstracts and peer reviewed publications, as well as by active participation of students and faculty at professional meetings.

Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 197.80K | Year: 2013

With this award from the Chemistry Major Research Instrumentation Program, Professor William Yu from Lousiana State University - Shreveport and colleagues Kui Che, M. Cran Lucas and Thomas Ticich will acquire a high resolution electron microscope (EM) with TEM (transmission), SEM (scanning), STEM and ED (electron difraction) functions. The proposal is aimed at enhancing research and education at all levels, especially in areas such as (a) glycosaminoglycans and podocyte behavior, (b) study of anti-cancer agents based on fusarochromanone (FC101a), (c) magnetic nanocrystals for cancer detection and treatment, (d) inhibiting lymphatogenous tumor metastasis by nanoshell-mediated hyperthermia, (e) TEM analysis of carbon nanostructures synthesized in an ethanol burner, (f) porous stainless steel supported iron oxide nanoparticle membranes for arsenic removal from water, and (g) disruption of crystalline cellulose for efficient enzymatic adsorption and hydrolysis.

An electron microscope uses high energy electrons in the characterization of materials. In the transmission mode, a beam of electrons penetrates a thin layer of sample resulting in an image of the sample while in the scanning mode the electrons are used to probe the material. The electron microscope can provide higher resolution and magnification than a microscope using light to probe the material. It can provide useful details of the material down to near atomic size levels. This instrumentation will provide microscopy training and research opportunities to large numbers of students across many fields including chemistry, biology, physics, materials and engineering.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ROBUST INTELLIGENCE | Award Amount: 155.90K | Year: 2011

Clustering is a crucial component of exploratory data analysis. This project aims to develop novel algorithms that address various shortcomings of k-means, the most widely used clustering algorithm. Specific objectives include (a) the development of initialization methods to address the sensitivity of k-means to the initial cluster centers; (b) the investigation of alternative distance measures to address the sensitivity of k-means to outliers; and (c) the development of practical acceleration methods. Innovations developed during this project should be readily applicable to a wide range of clustering algorithms. For example, initialization is crucial for most clustering algorithms. Furthermore, an effective initialization method can be used independently of k-means as a standalone clustering algorithm. K-means is often used as a subroutine in other learning algorithms. Therefore, development of acceleration methods for k-means is of great practical interest.

The project is expected to make broader impacts on several fronts. At the international level, we intend to make significant contributions to the data mining literature by publishing in top-ranked journals. At the national level, we aim to enhance the competitiveness of the US by seeding the next generation of scientists. At the regional level, we hope to improve the quality of education in an EPSCoR state and contribute to the development of a diverse and skilled workforce. At the institutional level, we intend to improve the research environment and the curriculum of our Computer Science program. Finally, at the individual level, we hope to increase the participation of students from underrepresented groups in research and equip them with valuable skills including self-confidence, independent thinking/problem solving, and effective communication.

Celebi M.E.,Louisiana State University in Shreveport
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2010

Reduced ordering based nonlinear vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics. ©2010 IEEE.

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