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PAML 2015 : Journal of Neural Computing and Applications (Springer), Special Issue on Predictive Analytics Using Machine Learning

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Link: http://goo.gl/8rDiIj
 
When N/A
Where N/A
Submission Deadline Mar 7, 2015
Notification Due May 30, 2015
Final Version Due Jul 30, 2015
Categories    machine learning   data mining   artificial intelligence
 

Call For Papers

Predictive analytics is concerned with the prediction of future trends and outcomes. The approaches used to conduct
predictive analytics can be classified into machine learning techniques and regression techniques. Machine learning
techniques have become increasingly popular in conducting predictive analytics due to their outstanding
performance in handling large scale datasets with uniform characteristics and noisy data. Innovative predictive
models have been applied successfully in several domains such as health care, cyber security, education, credit card
fraud detection, social media, cloud computing, software measurement, quality and defect prediction, cost and effort
estimation and software reuse.

The aim of this special issue is to invite authors of selected papers in the 2014 Workshop on Machine Learning for
Predictive Models (http://www.icmla-conference.org/icmla14/w03.pdf) to extend their work and submit to this
special issue. Moreover, this special issue is open for external authors to submit their original work in this area. It is
envisioned to obtain a good perspective into the current state of practice of Machine Learning techniques to address
various predictive problems. Topics relevant to this special issue include, but are not limited to:
Clustering and Classification
Decision Support
Support Vector Machine
Time Series
Decision Trees
Fuzzy Logic & Systems
Probabilistic Reasoning
Lazy Learning
Recommender Systems
Expert Systems
Artificial Neural Networks
Evolutionary Algorithms
Ranking Algorithms
Cognitive Processes
Evolutionary Computing
Swarm Intelligence
Artificial Immune Systems
Instance–Based Learning
Chaos Theory
Multi-Valued Logic
Ensemble Techniques
Hybrid Intelligent Models
Reasoning Models

Applied to:


Health Care Applications, Education, Cyber Security, Credit Card Fraud Detection,
Software Process and Performance, Software Cost Estimation, Software Reliability,
Software Risk Management, Software Quality and Testing, Cloud Computing, Social Media,
Business Applications,Sale Forecasting , Stock Market Forecasting, Weather Predictions
Bio-Medical Applications, Big data Applications, Intelligent Traffic Management,
Image Processing, E-Government , E-Business, E-Commerce, Networking

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