June 23, 2013
June 23, 2013
June 26, 2013
Computing & Information Technology
23.316.1 - 23.316.17
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.
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. https://peer.asee.org/19330
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