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Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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Conference Article |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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cidis @ cidis @ |
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154 |
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Author |
Monica Villavicencio; Alain Abran |
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Title |
Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs |
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Conference Article |
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Year |
2011 |
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Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement |
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239-244 |
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measurement; software engineering; higher education |
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In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement. |
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IEEE |
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English |
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gtsi @ user @ |
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68 |
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