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Software Engineering Research, Management and Applications
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Software Engineering Research, Management and Applications/ edited by Roger Lee.
其他作者:
Lee, Roger.
面頁冊數:
XIII, 204 p. 74 illus., 54 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Artificial Intelligence. -
電子資源:
https://doi.org/10.1007/978-3-031-09145-2
ISBN:
9783031091452
Software Engineering Research, Management and Applications
Software Engineering Research, Management and Applications
[electronic resource] /edited by Roger Lee. - 1st ed. 2022. - XIII, 204 p. 74 illus., 54 illus. in color.online resource. - Studies in Computational Intelligence,10531860-9503 ;. - Studies in Computational Intelligence,564.
Examining the Factors that Influence Customers’ Intention to Use Smartwatches in Malaysia Using UTAUT2 Model -- Generating Adversarial Robust Defensive CAPTCHA (GARD-CAPTCHA) in Convolutional Neural Networks -- A Deep Learning Approach for Lantana Camara Weed Detection and Localization in the Natural Environment -- Modeling Concretizations in Software Design -- A Practical Style Guide and Templates Repository for Writing Effective Use Cases -- Label Correction of Sound Data with Label Noise Using Self Organizing Map -- Evaluation Method of Enterprise Cybersecurity -- A Multi-model Multi-task Learning System for Hurricane Genesis Prediction -- Development of Autonomous Driving Adaptive Simulation System Using Deep Learning Process Model -- An OCL Implementation for Model-Driven Engineering of C++ -- Improving Students’ Readiness Toward the Labor Market Through Customized Learning -- Assessing Software Fault Risk with Machine Learning.
This edited book presents scientific results of the 20th IEEE/ACIS International Conference on Software Engineering Research, Management, and Applications (SERA2022) held on May 25, 2022, in Las Vegas, USA. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 12 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.
ISBN: 9783031091452
Standard No.: 10.1007/978-3-031-09145-2doiSubjects--Topical Terms:
646849
Artificial Intelligence.
LC Class. No.: Q342
Dewey Class. No.: 006.3
Software Engineering Research, Management and Applications
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Examining the Factors that Influence Customers’ Intention to Use Smartwatches in Malaysia Using UTAUT2 Model -- Generating Adversarial Robust Defensive CAPTCHA (GARD-CAPTCHA) in Convolutional Neural Networks -- A Deep Learning Approach for Lantana Camara Weed Detection and Localization in the Natural Environment -- Modeling Concretizations in Software Design -- A Practical Style Guide and Templates Repository for Writing Effective Use Cases -- Label Correction of Sound Data with Label Noise Using Self Organizing Map -- Evaluation Method of Enterprise Cybersecurity -- A Multi-model Multi-task Learning System for Hurricane Genesis Prediction -- Development of Autonomous Driving Adaptive Simulation System Using Deep Learning Process Model -- An OCL Implementation for Model-Driven Engineering of C++ -- Improving Students’ Readiness Toward the Labor Market Through Customized Learning -- Assessing Software Fault Risk with Machine Learning.
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