Decision-Making and Data Mining for Sustainable Computing, 2nd Edition

Topic Information

Dear Colleagues,

The statistical and machine learning (ML) approaches of artificial intelligence (AI) have been successfully implemented in the predictive applications of several domains of Science and Engineering in recent years. The ML algorithms of AI are vital components for the development of an automated, accurate, and robust prediction system after analysis of the data for the specific application. The improved accuracy of the statistical and ML approaches of AI is crucial in each of the applications of predictive modeling in many domains including healthcare, agriculture, and space. The development of advanced ML approaches and their implementation in the analysis of experimental and simulated data is posing a challenge to researchers at present. With this objective, this Topic invites researchers and academicians to submit their novel and unpublished research outcomes related to the current development of statistical and ML approaches to AI in predictive modeling applications of Engineering and Sciences. This is the second edition of a Topic on decision-making and data mining for sustainable computing, covering a broad range of topics related to applications of ML approaches in the analysis of data.

Prof. Dr. Sunil Jha
Dr. Malgorzata Rataj
Prof. Dr. Xiaorui Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Data data	2.0	5.0	2016	25 Days	CHF 1600	Submit
Encyclopedia encyclopedia	-	-	2021	26.8 Days	CHF 1200	Submit
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600	Submit
Sci sci	-	5.2	2019	26.7 Days	CHF 1400	Submit
Symmetry symmetry	2.2	5.3	2009	15.8 Days	CHF 2400	Submit

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