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University of Nebraska at Omaha, United States

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10 Papers

A New Approach for Analyzing Financial Markets using Correlation Networks and Population Analysis

Topics: Strategic Decision Support Systems; Applications of Expert Systems; Operational Research

Authors: Zahra Hatami ¹ , Hesham Ali ¹ , David Volkman ² and Prasad Chetti ³

Affiliations: ¹ College of Information Science & Technology, University of Nebraska at Omaha,Omaha, U.S.A. , ² College of Business Administration, University of Nebraska at Omaha,Omaha, U.S.A. , ³ School of Computer Science & Information Systems,Northwest Missouri State University, Maryville, U.S.A.

Digital Object Identifier: 10.5220/0011073800003179

Proceedings of ICEIS 2022, ISBN: 978-989-758-569-2

Quick Abstract

With the availability of massive data sets associated with stock markets, we now have opportunities to apply newly developed big data techniques and data-driven methodologies to analyze these complicated markets. Correlation network analysis makes it possible to structure large data in ways that facilitate finding common patterns and mine-hidden information. In this study, we developed the population analysis with utilizing a correlation network model to conduct a study on stock market data on companies for the years 2000 through 2004. We utilized companies’ parameters for behavior assessment based on the population analysis. After creating the network model, we employed graph-based community algorithms, such as GLay, to identify communities and stocks with similar features associated with their excess returns. Our analysis of the top two communities revealed that companies in the finance sector have the highest share in the market, and companies with a low amount of capitalization have a high excess return, similar to large companies. The proposed correlation network model and the associated population analysis show that investing in companies with high capitalization does not always guarantee higher rates of return on investment. Based on the proposed approach, investors could get similar returns by investing in certain small companies.

The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes

Topics: Medical Signal Acquisition, Analysis and Processing; Wearable Sensors and Systems

Authors: Rama Thelagathoti and Hesham Ali

Affiliation: College of Information Science and Technology, University of Nebraska Omaha, Omaha, NE 68182, U.S.A.

Digital Object Identifier: 10.5220/0010844500003123

Proceedings of BIOSIGNALS 2022, ISBN: 978-989-758-552-4

Quick Abstract

Depression is a serious mental health disorder affecting millions of people around the world. Traditional diagnostic approaches are subjective including self-reporting feedback from patients and observational evaluation by a trained physician. However, altered motor activity is the central feature for depressive disorder. Moreover, recent studies show that the analysis of motor activity is the best predictor in characterizing psychological disorders including depression. With the advent of wearable devices, an individual’s motor activity can be monitored naturally using body worn sensors and feasible to distinguish depressed persons from healthy individuals. In this manuscript, we hypothesis to apply a methodology that takes advantage of motor activity recorded from wearable devices and process mobility patterns for a given group of subjects. Besides, employed a population analysis approach using correlation networks that evaluates mobility parameters of the population and identify subgroups that exhibit similar motor complexity. We have analyzed the mobility data of the given group by extracting three different sets of features using hour-wise, day-wise, and hybrid mobility data. Also, a comparison study of three models is presented by constructing a correlation graph and finding a cluster of individuals exhibiting similar mobility patterns. We found that mobility data using hour-wise features provides the best results compared to the other two models.

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohn’s Disease and Healthy Gut Microbiomes

Topics: Sequence Analysis; Data Mining and Machine Learning; Pattern Recognition, Clustering and Classification; Next Generation Sequencing

Authors: Julia Warnke-Sommer ¹ and Hesham Ali ²

Affiliations: ¹ University of Nebraska Medical Center, United States , ² University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0006254201040114

Proceedings of BIOINFORMATICS 2017/BIOSTEC, ISBN: 978-989-758-214-1

Quick Abstract

Horizontal gene transfer is a major driver of bacterial evolution and adaptation to niche environments. This holds true for the complex microbiome of the human gut. Crohn’s disease is a debilitating condition characterized by inflammation and gut bacteria dysbiosis. In previous research, we analyzed transposase associated antibiotic resistance genes in Crohn’s disease and healthy gut microbiome metagenomics data sets using a graph mining approach. Results demonstrated that there were significant differences in the type and bacterial distribution of transposase-associated antibiotic resistance genes in the Crohn’s and healthy data sets. In this paper, we extend the previous research by considering all gene features associated with transposase sequences in the Crohn’s disease and healthy data sets. Results demonstrate that some transposase-associated features are more prevalent in Crohn’s disease data sets than healthy data sets. This study may provide insights into the adaptation of bacteria to gut conditions such as Crohn’s disease.

Related Ontology Subjects/Areas/Topics (6)

Bioinformatics		Data Mining and Machine Learning		Pattern Recognition, Clustering and Classification
Biomedical Engineering		Next Generation Sequencing		Sequence Analysis

On the Impact of Granularity in Extracting Knowledge from Bioinformatics Data

Topics: Genomics and Proteomics; Databases and Data Management; Data Mining and Machine Learning; Systems Biology; Algorithms and Software Tools

Authors: Sean West and Hesham Ali

Affiliation: University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0005778700920103

Proceedings of BIOINFORMATICS 2016/BIOSTEC, ISBN: 978-989-758-170-0

Quick Abstract

With the rapidly increasing amount of various types of biological data currently available to researchers, the focus of the biomedical research community has been shifting from pure data generation towards the development of new methodologies for data analytics. Although many researchers continue to focus on approaches developed for analyzing single types of biological data, recent attempts have been made to utilize the availability of heterogeneous data sets that contain various types of data and try to establish tools for data integration and analysis in many bioinformatics applications. Such attempts are expected to increase significantly in this coming decade. While this can be viewed as a positive step towards advancing big data analytics in bioinformatics, it is critical that these integration methodologies are meticulously studied to ensure high quality of the knowledge extracted from the integrated data. In this work, we employ data integration methods to analyze biological data obtained from protein interaction networks and gene expression data. We conduct a study to show that potential problems can arise from integrating or fusing data obtained at different granularity levels and highlight the importance of developing advanced data fusing techniques to integrate various types of biological data for analytical purposes. Further, we explore the impact of granularity from a more formulized approach and the granularity levels significantly impact the quality of knowledge extracted from the integrated data.

Related Ontology Subjects/Areas/Topics (7)

Algorithms and Software Tools	Data Mining and Machine Learning	Genomics and Proteomics
Bioinformatics	Databases and Data Management	Systems Biology
Biomedical Engineering

A Multi-stage Graph Approach for Efficient Clustering in Self-Organized Wireless Sensor Networks

Topics: Power Management; Scheduling, Tasking and Control; Ad Hoc Networks; Hierarchical Networks; Network Performance

Authors: Abhishek Karpate and Hesham Ali

Affiliation: University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0005244700560062

Proceedings of SENSORNETS 2015, ISBN: 978-989-758-086-4

Quick Abstract

With the rapid increase in applications utilizing the current advancements of wireless sensor networks, a number of problems related to self-organization, energy-awareness and network organizations have attracted many researchers in the field. Various groups have proposed grouping the sensors into clusters and design communication routes in two levels as a way to improve communication cost and better organize networks of large sensors. In this paper, we propose a new approach to cluster wireless sensors and identify cluster heads using multi-stage graph algorithms. The approach takes advantage of the optimally associated with finding matching solutions in multi-stage graph networks. The proposed solution is designed to accommodate networks with different sizes and levels of density. We tested the algorithm using different types of networks and measure the quality of the key parameters as compared to those obtained by traditional greedy heuristics. Obtained results show that the multi-stage graph approach produces better network organization and better cluster head selection which leads to be more efficient self-organized networks.

Related Ontology Subjects/Areas/Topics (9)

Ad Hoc Networks	Obstacles	Sensor Networks
Hierarchical Networks	Power Management	Software and Architectures
Network Performance	Scheduling, Tasking and Control	Wireless Information Networks

Identifying Aging Genes in the Aging Mouse Hypothalamus Using Gateway Node Analysis of Correlation Networks

Topics: Genomics and Proteomics; Systems Biology

Authors: Kathryn M. Cooper ¹ , Stephen Bonasera ² and Hesham Ali ¹

Affiliations: ¹ University of Nebraska at Omaha, United States , ² University of Nebraska Medical Center, United States

Digital Object Identifier: 10.5220/0005212600360043

Proceedings of BIOINFORMATICS 2015/BIOSTEC, ISBN: 978-989-758-070-3

Quick Abstract

High-throughput studies continue to produce volumes of data, providing a wealth of information that can be used to better guide biological research. However, models that can readily identify true biological signals from this data have not been developed at the same rate, due in part to a lack of well-developed algorithms that can handle the magnitude, variability and veracity of the data. One promising and effective solution to this complex issue is network modeling, due to its capabilities for representing biological elements and relationships en masse. In this research, we use correlation networks for analysis where genes are represented as nodes and indirect relationships (derived from expression patterns) are represented as edges. Here, we define “gateway” nodes as elements representing genes that change in co-expression and possibly co-regulation between states. We use the gateway node approach to identify critical genes in the aging mouse brain and perform a cursory investigation of the robustness of these gateway nodes according to network structure. Our results highlight the power of the gateway nodes approach and show how it can be used to limit search space and determine candidate genes for targeted studies. The novelty of this approach lies in application of the gateway node approach on novel mouse datasets, and the investigation into robustness of network structures.

Related Ontology Subjects/Areas/Topics (4)

Bioinformatics		Genomics and Proteomics		Systems Biology
Biomedical Engineering

On the Robustness of the Biological Correlation Network Model

Topics: Systems Biology; Computational Molecular Systems; Model Design and Evaluation

Authors: Kathryn M. Dempsey and Hesham H. Ali

Affiliation: University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0004805801860195

Proceedings of BIOINFORMATICS 2014/BIOSTEC, ISBN: 978-989-758-012-3

Quick Abstract

Recent progress in high-throughput technology has resulted in a significant data overload. Determining how to obtain valuable knowledge from such massive raw data has become one of the most challenging issues in biomedical research. As a result, bioinformatics researchers continue to look for advanced data analysis tools to analysis and mine the available data. Correlation network models obtained from various biological assays, such as those measuring gene expression levels, are a powerful method for representing correlated expression. Although correlation does not always imply causation, the correlation network has been shown to be effective in identifying elements of interest in various bioinformatics applications. While these models have found success, little to no investigation has been made into the robustness of relationships in the correlation network with regard to vulnerability of the model according to manipulation of sample values. Particularly, reservations about the correlation network model stem from a lack of testing on the reliability of the model. In this work, we probe the robustness of the model by manipulating samples to create six different expression networks and find a slight inverse relationship between sample count and network size/density. When samples are iteratively removed during model creation, the results suggest that network edges may or may not remain within the statistical parameters of the model, suggesting that there is room for improvement in the filtering of these networks. A cursory investigation into a secondary robustness threshold using these measures confirms the existence of a positive relationship between sample size and edge robustness. This work represents an important step toward better understanding of the critical noise versus signal issue in the correlation network model.

Related Ontology Subjects/Areas/Topics (5)

Bioinformatics		Computational Molecular Systems		Systems Biology
Biomedical Engineering		Model Design and Evaluation

A Stochastic Queueing Model for Multi-robot Task Allocation

Topics: Distributed Control Systems; Autonomous Agents; Planning and Scheduling; Collective and Social Robots

Authors: Angelica Muñoz-Meléndez ¹ , Pritviraj Dasgupta ² and William Lenagh ²

Affiliations: ¹ National Institute of Astrophysics, Optics, and Electronics and University of Nebraska at Omaha, Mexico , ² University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0004049102560261

Proceedings of ICINCO 2012, ISBN: 978-989-8565-21-1

Quick Abstract

A central problem in multi-robot systems is to solve the multi-robot task allocation problem. In this paper, a decentralized stochastic model based on stochastic queueing processes is applied for an application of collective detection of underground landmines where the robots are not told the distribution or number of landmines to be encountered in the environment. Repeat demands of inspection in the environment to ensure the accuracy of robot findings are necessary in this application. The proposed model is based on the estimation of a stochastic queue of pending demands that represents the alternatives of action for a robot and is used to negotiate possible conflicts with other robots. We compare and contrast this method with a decentralized greedy approach based on the distance towards the sites where inspection demands are required. Experimental results obtained using simulated robots in the Webots c environment are presented. The performance of robots is measured in terms of two metrics, completion time and distance traveled for processing a demand. Robots applying the stochastic queueing model obtained competitive results.

Related Ontology Subjects/Areas/Topics (11)

Artificial Intelligence	Formal Methods	Robotics and Automation
Autonomous Agents	Informatics in Control, Automation and Robotics	Simulation and Modeling
Collective and Social Robots	Intelligent Control Systems and Optimization	Symbolic Systems
Distributed Control Systems	Planning and Scheduling

MULTI-ROBOT COVERAGE WITH DYNAMIC COVERAGE INFORMATION COMPRESSION

Topics: Mobile Robots and Autonomous Systems; Network Robotics; Autonomous Agents

Authors: Zachary Wilson , Taylor Whipple and Prithviraj Dasgupta

Affiliation: University of Nebraska, United States

Digital Object Identifier: 10.5220/0003538202360241

Proceedings of ICINCO 2011, ISBN: 978-989-8425-75-1

Quick Abstract

We consider the problem of distributed terrain or area coverage of an initially unknown environment using a set of mobile robots. We describe a distributed algorithm that is able to solve the distributed coverage problem without having each robot exchange its complete coverage map with other robots. The central part of our technique is a compression algorithm used by a robot to approximate the regions that have been previously covered and a fitness function that calculates the degree of accuracy of the approximated coverage information. The operation of our coverage algorithm is evaluated through experiments on simulated as well as physical Corobot robots. We have quantified the extent of overhead introduced by our coverage algorithm to prevent robots from performing repeated coverage. Overall, our results show that the robots are able to cover the environment within different environment settings while significantly reducing the amount of coverage information communicated between different robots.

Related Ontology Subjects/Areas/Topics (5)

Autonomous Agents		Mobile Robots and Autonomous Systems		Robotics and Automation
Informatics in Control, Automation and Robotics		Network Robotics

Making Use Case Slices Manage Variability in Aspect-based Product Line

Authors: Satish Mahadevan Srinivasan and Mansour Zand

Affiliation: University of Nebraska at Omaha, United States

Digital Object Identifier: 10.5220/0002200301410148

Proceedings of MSVVEIS 2009/ICEIS, ISBN: 978-989-8111-90-6

Quick Abstract

Use case slice, clearly lacks composition mechanism due to which it is difficult to manage variabilities in Software Product Lines. A use case slice can only convey the design of a single member of a product line. Aspect-based modeling of use case slices look to be a promising solution but there are few issues within their composition mechanism. Aspect-based modeling of use case slices clearly lack a strong and familiar algebraic model and also fails to address precedence management issues among artifacts such as pointcuts and advices. This paper suggests a composition mechanism, for aspect-based implementation of use case slices, which would provide a familiar algebraic model and will resolve issues related to precedence management. In this paper we have discussed about a hypothetical aspect-based Expressions Product Line (EPL) and have shown how the use case slices can be used to model the variabilities in EPL.