The machine learning approach provides a useful tool when the amount of data is very large and a model is not available to explain the generation and relation of the data set. It provides a set of practical applications for solving problems and applying various techniques in automatic data extraction and setting. This technique fills the gap between theory and practice, providing a strong reference for academicians, researchers, and practitioners. Also it covers exploratory as well as predictive modelling, deep and supervised learning, image analysis etc., which form a fruitful branch of machine learning in their own right. This extends beyond the design of non-linear algorithms to encompass also their evaluation, a critical and often neglected area of research, yet a critical stage in practical applications.
This is an ideal platform for researchers to come up with innovative ideas and approaches in the area of machine learning using these applied soft computing strategies. This gains much importance since it directly impacts the real life problem.
The issue will carry revised and substantially extended versions of selected papers presented at the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM-2018), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include, but are not limited, to the following:
- Pattern analysis
- Pattern recognition
- Biometrics
- Performance evaluation of Recognition
- Deep Learning
- Optimization for deep learning
- Supervised learning
- Unsupervised learning
- Feature representation
- Content Based Image Retrieval
- Video surveillance
- Tumour Detection
- Neuroinformatics
- Health care System
Important Dates
Manuscripts due by: 20 May, 2018
Notification to authors: 1 August, 2018
Final versions due by: 20 October, 2018
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