Richard S. Segall

Richard S. Segall

Dr. Richard S. Segall is Professor of Information Systems & Business Analytics in the Neil Griffin College of Business at Arkansas State University in Jonesboro, AR where has also taught for ten years in the Master of Engineering Management (MEM) Program in the College of Engineering & Computer Science. He is also Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. He holds a Bachelor of Science and Master of Science in Mathematics as well as a Master of Science in Operations Research and Statistics from Rensselaer Polytechnic Institute in Troy, New York. He also holds a PhD in Operations Research form University of Massachusetts at Amherst, He has served on the faculty of Texas Tech University, University of Louisville, University of New Hampshire, University of Massachusetts-Lowell, and West Virginia University. His research interests include data mining, Big Data, text mining, web mining, database management, and mathematical modeling.

Dr. Segall‘s publications have appeared in numerous journals including International Journal of Information Technology and Decision Making (IJITDM), International Journal of Information and Decision Sciences (IJIDS), Applied Mathematical Modelling (AMM), Kybernetes, Journal of the Operational Research Society (JORS), Journal of Systemics, Cybernetics and Informatics (JSCI), International Journal of Artificial Intelligence and Machine Learning (IJAIML), International Journal of Open Source Software and Processes (IJOSSP), and International Journal of Fog Computing (IJFC). He has published book chapters in Encyclopedia of Data Warehousing and Mining, Handbook of Computational Intelligence in Manufacturing and Production Management,Handbook of Research on Text and Web Mining Technologies, Encyclopedia of Information Science & Technology, and Encyclopedia of Business Analytics & Optimization.

Dr. Segall was a member of the former Arkansas Center for Plant-Powered-Production (P3), and is a member of the Center for No-Boundary Thinking (CNBT), and on the Editorial Board of theInternational Journal of Data Mining, Modelling and Management (IJDMMM)) and International Journal of Data Science (IJDS), and served as Local Arrangements Chair of the MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference that was hosted at Arkansas State University.

His research has been funded by National Research Council (NRC), U.S. Air Force (USAF), National Aeronautical and Space Administration (NASA), Arkansas Biosciences Institute (ABI), and Arkansas Science & Technology Authority (ASTA). He is recipient of several Session Best Paper awards at World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) conferences. Dr.Segall is Lead Editor of several IGI Global books of: Biomedical and Business Applications using Artificial Neural Networks and Machine Learning, Open Source Software for Statistical Analysis of Big Data, Handbook of Big Data Storage and Visualization Techniques (2 volumes), and Research and Applications in Global Supercomputing, and is co-editor of Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications. Dr. Segall is recipient of Arkansas State University, Neil Griffin College of Business Faculty Award for Excellence in Research in 2015 and 2019, and the 2020 University Award in Scholarship (Research) of Arkansas State University.

Publications

Data Visualization of Big Data for Predictive and Descriptive Analytics for Stroke, COVID-19, and Diabetes
Richard S. Segall, Soichiro Takashashi. © 2023. 31 pages.
Visualization of big data is crucial for meaningful interpretations and especially for healthcare. Brief discussions are made for big data, background for healthcare, and recent...
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
Richard S. Segall, Gao Niu. © 2022. 394 pages.
During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being...
Overview of Big Data and Its Visualization
Richard S. Segall, Gao Niu. © 2022. 32 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big...
What Is Open Source Software (OSS) and What Is Big Data?
Richard S. Segall. © 2022. 42 pages.
This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open...
Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software
Richard S. Segall. © 2022. 28 pages.
This chapter first provides an overview with examples of what neural networks (NN), machine learning (ML), and artificial intelligence (AI) are and their applications in...
Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions
Richard S. Segall. © 2022. 28 pages.
The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-19 detection and analysis. Specifically, the use of neural...
Using Open-Source Software for Business, Urban, and Other Applications of Deep Neural Networks, Machine Learning, and Data Analytics Tools
Richard S. Segall, Vidhya Sankarasubbu. © 2022. 28 pages.
This article provides an overview with examples of what Neural Networks (NN), Machine Learning (ML), and Artificial Intelligence (AI) and Data Analytics are, and with their...
Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases
Richard S. Segall, Vidhya Sankarasubbu. © 2022. 30 pages.
The purpose is to illustrate how artificial intelligence (AI) technologies have been used for detection and analysis of COVID-19 and other infectious diseases such as breast...
Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)
Shen Lu, Richard S. Segall. © 2021. 22 pages.
Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.”...
A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities
Gao Niu, Richard S. Segall, Zichen Zhao, Zhijian Wu. © 2021. 20 pages.
This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most...
Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing
Richard S. Segall, Jeffrey S Cook, Gao Niu. © 2021. 42 pages.
Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical...
What Is Open Source Software (OSS) and What Is Big Data?
Richard S. Segall. © 2021. 41 pages.
This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open...
Open Source Software (OSS) for Big Data
Richard S. Segall. © 2021. 18 pages.
This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open...
Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
Richard S. Segall, Gao Niu. © 2020. 237 pages.
With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular...
What Is Open Source Software (OSS) and What Is Big Data?
Richard S. Segall. © 2020. 49 pages.
This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open...
Open Source Software (OSS) for Big Data
Richard S. Segall. © 2020. 23 pages.
This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open...
Big Data and Its Visualization With Fog Computing
Richard S. Segall, Gao Niu. © 2020. 37 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is...
Data Linkage Discovery Applications
Richard S. Segall, Shen Lu. © 2019. 13 pages.
This chapter discusses the topic of linkage discovery for data and their applications. This chapter enhances a previous study by the authors and includes additional references...
Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing
Richard S. Segall, Jeffrey S Cook, Gao Niu. © 2019. 40 pages.
Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical...
Handbook of Research on Big Data Storage and Visualization Techniques
Richard S. Segall, Jeffrey S. Cook. © 2018. 917 pages.
The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across...
Data Linkage Discovery Applications
Richard S. Segall, Shen Lu. © 2018. 11 pages.
Overview of Big Data and Its Visualization
Richard S. Segall, Gao Niu. © 2018. 32 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big...
Overview of Big-Data-Intensive Storage and Its Technologies
Richard S. Segall, Jeffrey S. Cook. © 2018. 42 pages.
This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about...
Big Data and Its Visualization With Fog Computing
Richard S. Segall, Gao Niu. © 2018. 32 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is...
Information Retrieval by Linkage Discovery
Richard S. Segall, Shen Lu. © 2015. 8 pages.
Research and Applications in Global Supercomputing
Richard S. Segall, Jeffrey S. Cook, Qingyu Zhang. © 2015. 672 pages.
Rapidly generating and processing large amounts of data, supercomputers are currently at the leading edge of computing technologies. Supercomputers are employed in many different...
Overview of Global Supercomputing
Richard S. Segall, Neha Gupta. © 2015. 32 pages.
In this chapter, a discussion is presented of what a supercomputer really is, as well as of both the top few of the world's fastest supercomputers and the overall top 500 in...
Linkage Discovery with Glossaries
Richard S. Segall, Shen Lu. © 2014. 11 pages.
Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications
Qingyu Zhang, Richard S. Segall, Mei Cao. © 2011. 362 pages.
Large volumes of data and complex problems inspire research in computing and data, text, and Web mining. However, analyzing data is not sufficient, as it has to be presented...
Comparing Four-Selected Data Mining Software
Richard S. Segall. © 2009. 9 pages.
This chapter discusses four-selected software for data mining that are not available as free open-source software. The four-selected software for data mining are SAS® Enterprise...
A Survey of Selected Software Technologies for Text Mining
Richard S. Segall. © 2009. 19 pages.
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and...
A Survey of Selected Software Technologies for Text Mining
Richard S. Segall, Qingyu Zhang. © 2009. 18 pages.
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and...
Comparing Four-Selected Data Mining Software
Richard S. Segall, Qingyu Zhang. © 2009. 10 pages.
This chapter discusses four-selected software for data mining that are not available as free opensource software. The four-selected software for data mining are SAS® Enterprise...
Using Data Mining for Forecasting Data Management Needs
Qingyu Zhang, Richard S. Segall. © 2008. 17 pages.
This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of...
Using Data Mining for Forecasting Data Management Needs
Qingyu Zhang, Richard S. Segall. © 2008. 18 pages.
This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of...
Microarray Databases for Biotechnology
Richard S. Segall. © 2005. 6 pages.
Microarray informatics is a rapidly expanding discipline in which large amounts of multi-dimensional data are compressed into small storage units. Data mining of microarrays can...