The 4th EAI International Conference on Mining Intelligence and Knowledge Exploration

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Deadline: July 15, 2017 | Date: November 12, 2017-November 13, 2017

Venue/Country: India

Updated: 2016-12-16 00:09:44 (GMT+9)

Call For Papers - CFP

To develop innovative and intelligent machines that are fit for futuristic use, it is necessary to take a holistic approach in order to recognize various situations and environmental issues. To do this, various types of machine learning mechanisms have been developed during past decades. Recently, deep Learning has been proposed as a new area of Machine Learning and Mining researches, which have been investigated with the objective of moving Machine Learning closer to one of its original goals. Deep learning is being tried to apply for various recognition fields where researchers feel difficulty, but can achieve very promising result such as Alphago by Google deep mind. This kind of learning mechanism can cooperate with various sensors, which are able to gather large information.

Also, the development of sensor networks, particularly in the last years, has extended their applicability in various domains, such as heritage preservation, environmental motoring and human activity recognition. Especially, to achieve highly natural interpretation of the environmental situation, various kinds of sensors should be widely employed in recognition system. 

Therefore, integration of sensor data with intelligent machine learning scheme is a natural choice and henceforth the sensor-based recognition technology is emerging as an important field of research including artificial intelligence (AI). This special issue aims to highlight the latest research results and advances on algorithms and technologies for various sensor-based recognition systems. It will include related topics and demonstrate original research work in this field of research. It will also cover the results of investigation on these topics featuring novel solutions and discuss the future trend of research in this domain. The MIKE2017 will be an interdisciplinary conference that brings together researchers and practitioners from the domains of learning algorithms, data mining, machine learning, knowledge exploration, large-scale data analytics, big data, soft computing, information systems, and so on. The selected outstanding papers from MIKE2017 will be recommended to this special issue.

Topics

Topics of interest include, but are not limited to, the following scope:

- Advances in Machine Learning

- Algorithms for Intelligent Learning

- Analogical, cognitive, and creative reasoning

- Business Intelligence

- Case Based Recommender Systems

- Collective Learning and Tagging

- Context and location aware techniques

- Cross / Multi Language content mining

- Crowdsourcing & Crowd Mining Algorithms and Systems

- Big Data Search (HANA, Terracotta, Hadoop)

- Bioinformatics - Reasoning and Learning

- Distributed and Peer-to-peer Search

- Distributed Mining of Human expertise

- Image Processing and Understanding

- IoT solutions and platforms for information mining

- Visual analytics for text mining /exploration Visual Exploration

- Real-time applications of data mining

- Learning mechanisms from wireless

 sensor systems

- Self-learning structure and framework

- Security problem in multimedia data mining and distribution

- Emotion and expression recognition algorithms for human interactive applications

- Human sensors-based applications for intelligent context recognition

- Real-time signal processing algorithms for recognition system

Important Dates

Manuscript submission deadline: 01 June 2017

Notification of acceptance: 01 July 2017

Submission of final revised paper: 15 July 2017

Submission Procedure

Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/.

A copy of the manuscript should also be emailed to the Guest Editors at the following email mipsansejong.ac.kr or bg.kimieee.org, joeljrieee.org, rajendra.prasathidi.ntnu.no.