Open Science Research Excellence

ICMLSAMS 2019 : International Conference on Machine Learning and Statistical Analysis for Materials Science

London, United Kingdom
November 18 - 19, 2019

Conference Code: 19UK11ICMLSAMS

Conference Proceedings

All submitted conference papers will be blind peer reviewed by three competent reviewers. The peer-reviewed conference proceedings are indexed in the Open Science Index, Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, and other index databases. Impact Factor Indicators.

Special Journal Issues

ICMLSAMS 2019 has teamed up with the Special Journal Issue on Machine Learning and Statistical Analysis for Materials Science. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue. The paper selection will be carried out during the peer review process as well as at the conference presentation stage. Submitted papers must not be under consideration by any other journal or publication. The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly. Selected full-text papers will be published online free of charge.

Conference Sponsor and Exhibitor Opportunities

The Conference offers the opportunity to become a conference sponsor or exhibitor. To participate as a sponsor or exhibitor, please download and complete the Conference Sponsorship Request Form.

Important Dates

Abstracts/Full-Text Paper Submission Deadline   July 1, 2019
Notification of Acceptance/Rejection   July 16, 2019
Final Paper (Camera Ready) Submission & Early Bird Registration Deadline   October 19, 2019
Conference Dates   November 18 - 19, 2019

Important Notes

Please ensure your submission meets the conference's strict guidelines for accepting scholarly papers. Downloadable versions of the check list for Full-Text Papers and Abstract Papers.

Please refer to the Paper Submission GUIDE before submitting your paper.

Selected Conference Papers

1) Consumer Load Profile Determination with Entropy-Based K-Means Algorithm
Ioannis P. Panapakidis, Marios N. Moschakis
2) Comparison of Machine Learning Models for the Prediction of System Marginal Price of Greek Energy Market
Ioannis P. Panapakidis, Marios N. Moschakis
3) Deep Learning Based Fall Detection Using Simplified Human Posture
Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif
4) Predictive Semi-Empirical NOx Model for Diesel Engine
Saurabh Sharma, Yong Sun, Bruce Vernham
5) Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults
L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead
6) Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard
7) Noise Reduction in Web Data: A Learning Approach Based on Dynamic User Interests
Julius Onyancha, Valentina Plekhanova
8) Missing Link Data Estimation with Recurrent Neural Network: An Application Using Speed Data of Daegu Metropolitan Area
JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim
9) Cognition of Driving Context for Driving Assistance
Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif
10) Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach
Rajvir Kaur, Jeewani Anupama Ginige
11) Hybrid Approach for Software Defect Prediction Using Machine Learning with Optimization Technique
C. Manjula, Lilly Florence
12) Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Walid Cherif
13) Hand Gesture Detection via EmguCV Canny Pruning
N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae
14) An Improved K-Means Algorithm for Gene Expression Data Clustering
Billel Kenidra, Mohamed Benmohammed
15) Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms
Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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