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Recent development in machine learning, Internet of Things (IoT) and nature-inspired computing techniques has lead to advancement in a range of smart and intelligent systems. These emerging technologies such as machine learning, cloud computing, Internet of Things and pervasive computing are assisting in designing better smart and artificial intelligent system for a variety of domain such as healthcare, agriculture, banking, finance, test processing, optimization problems etc. This special issue is aiming to invite original and novel contributions and the state-of-the-art research along with exhaustive review on the challenges related to the need and design of smart and intelligent systems designed using these three technologies. The main topics of interest include, but are not limited to, the following:
Topics
•Role of emerging nature-inspired computing techniques in Healthcare, Agriculture, Banking, Mechanical and Electronics Industry.
•Design and role of IoT in designing innovative frameworks in different domains areas.
•Fuzzy Logic and Rough Set Theory
•Applications of nature-inspired computing techniques, IoT and Machine Learning
•Need and applications of energy optimization
•Applications of emerging nature-inspired computing techniques in Bioinformatics and Bio-Engineering
•Social Internet of Things and their applications
•Nature inspired computing techniques and feature selection
•Applications of Machine learning and nature-inspired computing techniques in natural language processing
•Sentiment analyzer using machine learning and natures inspired computing techniques
•Convergence of machine learning, IoT and nature inspired computing techniques in for designing Artificial Intelligence applications.
This special issue will be published in EAI Endorsed Transactions on Energy Web, an open access journal abstracted/indexed in Scopus (CiteScore 2018: 0.25; SJR 2018: 0.124; SNIP 2018: 0.344), DOAJ, DBLP, CrossRef, EBSCO, WorldCat, among others. It focuses on topics ranging from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems.
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