SPECIAL ISSUE ON: New Innovations in Machine Learning and Software Science (NEMLSS)
摘要截稿:
全文截稿: 2019-03-15
影响因子: 5.921
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 计算机:人工智能 - 1区
Overview
Knowledge Engineering aims at turning the process of constructing Knowledge Based Systems (KBSs) from theory into intelligent application. This requires the analysis of actual problems and the development of appropriate methods, languages, and tools specialized for developing KBSs. Uncertainty is one aspect in decision making has been tackled in different research and project but still has not provided robust solutions that can be used in catastrophic decision making systems for example, or another systems needs more precise factorization to the uncertain requirement. Intelligence is related to how to apply knowledge indifferent spatial space models.
Current Artificial Intelligence involved in developing secure and robust software technologies for many aspects. However, recently data streams in multi-sensing and data in motion still considered as obstacles to in providing consistent decision making for real problems. This has created inherent needs to establish new innovations on integrating machine leaning into software systems making it more robust in handling real situations in decision making.
This special issue will expands best selected articles from the SOMET2018 (the 17th round of the International Conference on Intelligent Software Systems) held in Granada on September 2018) and also new and original contributions not submitted previously are welcome. The selected articles are based on novelty, and high quality technical contributions. The papers (extended revision) to be contributed in the NEMLESS special issue must have more than 70% new material compared with the conference version. Also, submitted articles should be original and not submitted elsewhere. Moreover, we encourage submittingOriginal Software Publicationtype articles.
The topics of the special issue:
- End-user programming environment,
- Semi-supervised learning, transfer learning and domain adaptation.
- Deep learning technologies in advanced software systems