posted by organizer: waseemahmad || 12197 views || tracked by 10 users: [display]

MAMLAKE 2017 : Special Session on Modern Applications of Machine Learning for Actionable Knowledge Extraction

FacebookTwitterLinkedInGoogle

Link: https://aciids.pwr.edu.pl/2017/special_sessions.php
 
When Apr 3, 2017 - Apr 5, 2017
Where Kanazawa, Japan
Submission Deadline Oct 1, 2016
Notification Due Nov 1, 2016
Final Version Due Nov 15, 2016
Categories    machine learning   artificial intelligence   data mining
 

Call For Papers

CALL FOR PAPERS

Special Session on Modern Applications of Machine Learning for Actionable Knowledge Extraction
at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017), Kanazawa, Japan, April 3-5, 2017
Conference website: http://www.aciids.pwr.edu.pl/

Objectives and topics

The special session on modern applications of machine learning for actionable knowledge extraction (MAMLAKE) at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) is devoted to the modern applications of machine learning techniques (supervised, unsupervised, semi- supervised and reinforcement learning) and how these techniques are helpful in extracting actionable knowledge. The application domain includes: engineering, retail, marketing, telecommunication, banking, bio-informatics, social sciences, security, health care, education, etc.
We want to offer an opportunity for researchers and practitioners to identify and implement machine learning approaches to novel and existing real world problems and report how these approaches are helping create actionable knowledge to assist in solving problems. The scope of the MAMLAKE 2017 includes, but is not limited to the following topics:
• Theoretical framework for actionable knowledge discovery
• Domain driven data mining
• Novel machine learning applications
• Mining actionable patterns from complex datasets
• Relational and graph mining methods
• Medical informatics
• Predictive analytics
• Temporal analysis
• Data warehouse & cube mining
• Frequent pattern analysis
• Classification
• Cluster analysis
• Outlier detection
• Intrusion detection
• Text understanding (web search, anti-spam)
• Building smart robots
• Pattern visualization
• Image processing
• Mining large data streams
• Mining large scale sensor data


Important dates
Submission of papers: 1 October 2016
Notification of acceptance: 1 November 2016
Camera-ready papers: 15 November 2016
Registration & payment: 15 December 2016
Conference date: 3-5 April 2017


Special Session Organizers

Dr Waseem Ahmad
Department of Computing
Waiariki Bay of Plenty Polytechnic, New Zealand

Dr Paul Leong
Department of Business Information Systems
Auckland University of Technology, New Zealand

Dr Muhammad Usman
Department of Computer Science
Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology, Pakistan

For further inquiry regarding the special session please contact waseem.ahmad@waiariki.ac.nz

Related Resources

IEEE-MLNLP 2026   2026 IEEE 9th International Conference on Machine Learning and Natural Language Processing (MLNLP 2026)
Ei/Scopus-AI2A 2026   2026 IEEE 6th International Conference on Artificial Intelligence, Automation and Algorithms (AI2A 2026)
MODERN SYSTEMS 2026   International Conference of Modern Systems Engineering Solutions
AMLDS 2027   IEEE--2027 3rd International Conference on Advanced Machine Learning and Data Science
ICIAI--EI 2027   2027 the 11th International Conference on Innovation in Artificial Intelligence (ICIAI 2027)
Ei/Scopus-ACEPE 2026   2026 3rd IEEE Asia Conference on Advances in Electrical and Power Engineering (ACEPE 2026)
MMET 2026   2026 11th International Conference on Modern Management, Education and Social Sciences
AAIML 2027   IEEE--2027 2nd International Conference on Advances in Artificial Intelligence and Machine Learning
Learning & Optimization 2026   ASCE EMI Minisymposium on Probabilistic Learning, Stochastic Optimization, and Digital Twins
Behaviour, Learning & the Economy 2026   ERUNI ERC London Launch Workshop Behaviour, Learning & the Economy