Computational Intelligence Course in Undergraduate Computer Science and Engineering Curricula

Afsaneh Minaie; Paymon Sanati-Mehrizy; Ali Sanati-Mehrizy; Reza Sanati-Mehrizy

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Conference: 2013 ASEE Annual Conference & Exposition
Location: Atlanta, Georgia
Publication Date: June 23, 2013
Start Date: June 23, 2013
End Date: June 26, 2013
ISSN: 2153-5965
Conference Session: POTPOURRI
Tagged Division: Computing & Information Technology
Page Count: 17
Page Numbers: 23.316.1 - 23.316.17
DOI: 10.18260/1-2--19330
Permanent URL: https://peer.asee.org/19330
Download Count: 973

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Paper Authors

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Afsaneh Minaie Utah Valley University

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Afsaneh Minaie is a professor of Computer Engineering at Utah Valley University. Her research interests include gender issues in the academic sciences and engineering ﬁelds, Embedded Systems Design, Mobile Computing, Wireless Sensor Networks, and Databases.

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Paymon Sanati-Mehrizy Icahn School of Medicine at Mount Sinai

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Paymon is currently a medical student at the Icahn School of Medicine at Mount Sinai. He completed his undergraduate studies in Biology from the University of Pennsylvania in May 2012. Currently, his research interests consist of higher education curricula, both with universities and medical schools.

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Ali Sanati-Mehrizy

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Dr. Ali Sanati-Mehrizy is a graduate of the Milton S. Hershey Pennsylvania State University College of Medicine. He completed his undergraduate studies in Biology from the University of Utah. In July 2013, he will begin a Pediatrics residency at the UMDNJ-Newark University Hospital. His research interests involve pediatric hematology and oncology as well as higher education curricula, both with universities and medical schools.

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Reza Sanati-Mehrizy Utah Valley University

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REZA SANATI MEHRIZY is a professor of Computing Sciences Department at Utah Valley University, Orem, Utah. He received his MS and PhD in Computer Science from University of Oklahoma, Norman, Oklahoma. His research focuses on diverse areas such as: Database Design, Data Structures, Artiﬁcial Intelligence, Robotics, Computer Integrated Manufacturing, Data Mining, Data Warehousing and Machine
Learning.

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Abstract

Artificial Neural Network Course in Undergraduate Computer Science and Engineering Curriculums Abstract: An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. The study of neural and adaptive systems is a unique and growing interdisciplinary field that considers adaptive, distributed, and mostly nonlinear systems. Neural and adaptive systems are used in many important engineering applications, such as signal filtering, data segmentation, data mining, text mining, classification of input patterns, system identification, prediction, and control. They are used in many commercial products such as modems, image‐processing systems, speech recognition, front‐end signal processors, and biomedical instrumentation. Nowadays, neural networks offer improved performance over conventional technologies in areas such as machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, artificial life, adaptive control, and optimization and many more. The principal characteristic of neural systems is their adaptivity, which brings a totally new system design technique. It is believed that neural and adaptive systems should be considered as another tool in the scientists and engineer’s toolbox. Educational excellence requires exposing students to the current edge of research. To ensure that student projects are along the same trajectory that the industry is moving, educators must continually introduce emerging techniques, practices, and applications into the curriculum. The field of artificial neural networks is growing rapidly. It is crucial that the emerging field of neural networks be integrated into the computer science and engineering curriculums. This paper will study different approaches that are used by different institutions of higher education around the world to integrate neural networks in their curriculum.

Citation
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Minaie, A., & Sanati-Mehrizy, P., & Sanati-Mehrizy, A., & Sanati-Mehrizy, R. (2013, June), Computational Intelligence Course in Undergraduate Computer Science and Engineering Curricula Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19330

TY  - CPAPER
AB  -     Artificial Neural Network Course in Undergraduate Computer Science and                              Engineering Curriculums                                                        Abstract: An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system.  Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem.  The study of neural and adaptive systems is a unique and growing interdisciplinary field that considers adaptive, distributed, and mostly nonlinear systems.   Neural and adaptive systems are used in many important engineering applications, such as signal filtering, data segmentation, data mining, text mining, classification of input patterns, system identification, prediction, and control.  They are used in many commercial products such as modems, image‐processing systems, speech recognition, front‐end signal processors, and biomedical instrumentation.  Nowadays, neural networks offer improved performance over conventional technologies in areas such as machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, artificial life, adaptive control, and optimization and many more.  The principal characteristic of neural systems is their adaptivity, which brings a totally new system design technique.   It is believed that neural and adaptive systems should be considered as another tool in the scientists and engineer’s toolbox.   Educational excellence requires exposing students to the current edge of research.  To ensure that student projects are along the same trajectory that the industry is moving, educators must continually introduce emerging techniques, practices, and applications into the curriculum.  The field of artificial neural networks is growing rapidly.  It is crucial that the emerging field of neural networks be integrated into the computer science and engineering curriculums.  This paper will study different approaches that are used by different institutions of higher education around the world to integrate neural networks in their curriculum.   
AU  - Afsaneh Minaie
AU  - Paymon Sanati-Mehrizy
AU  - Ali Sanati-Mehrizy
AU  - Reza Sanati-Mehrizy
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - Computational Intelligence Course in Undergraduate Computer Science and Engineering Curricula
UR  - https://peer.asee.org/19330
DO  - 10.18260/1-2--19330
ER  -