-------- Forwarded Message -------- Subject: [computational.science] CFP: Int. Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML 2019) Date: Thu, 31 Jan 2019 12:46:53 +0000 From: Deep ML deepml.info@gmail.com To: computational.science@lists.iccsa.org
------------------------ Call for Papers -----------------------------
The International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML 2019)
26-28 August 2019, Istanbul, Turkey
http://www.ficloud.org/deep-ml-2019/
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Deep learning and machine learning are the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionize industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, security, manufacturing, education, games, and various other industrial applications. Google, for example, exploits the techniques of deep learning in voice and image recognition applications, while Amazon uses such techniques in helping customers in their online purchase decisions. The International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) provides a leading forum for researchers, developers, practitioners, and professional from public sectors and industries in order to meet and share latest solutions and ideas in solving cutting edge problems in modern information society and economy.
The conference comprises a set of tracks that focus on specific challenges in deep learning and machine learning and their applications in emerging areas. Topics of interest include, but are not limited to, the following:
1) Deep and Machine Learning Models and Techniques:
Novel machine and deep learning Active learning; Incremental learning and online learning Agent-based learning; Manifold learning Multi-task learning Bayesian networks and applications Case-based reasoning methods Statistical models and learning Computational learning; Evolutionary algorithms and learning Evolutionary neural networks Fuzzy logic-based learning Genetic optimization Clustering, classification and regression Neural network models and learning Parallel and distributed learning Reinforcement learning Supervised, semi-supervised and unsupervised learning Tensor Learning
2) Deep and Machine Learning for Big Data Analytics:
Deep/Machine learning based theoretical and computational models Novel techniques for big data storage and processing Data analysis, insights and hidden pattern Data analysis and decision making Data wrangling, munching and cleaning Data integration and fusion Data visualization Data and information quality, efficiency and scalability Security threat detection using big data analytics Visualizing security threats Enhancing privacy and trust Data analytics in complex applications – finance, business, healthcare, engineering, medicine, law, transportation, and telecommunication 3) Deep and Machine Learning for Data Mining and Knowledge:
Data mining in the web and online systems Multimedia; images and video data mining Feature extraction and classification Information retrieval and extraction Distributed and P2P data search Sentiment analysis Mining high velocity data streams Anomaly detection in streaming data Mining social media and social networks Mining sensor and computer networks data Mining spatial and temporal datasets Data classification, clustering, and association Knowledge acquisition and learning Knowledge representation and reasoning Knowledge discovery in large datasets
4) Deep and Machine Learning Application Areas:
Bioinformatics and biomedical informatics Finance, business and retail Intelligent transportation Healthcare, medicine and clinical decision support Computer vision Human activity recognition Information retrieval and web search Cybersecurity Natural language processing Recommender systems Social media and networks
5) Deep and Machine Learning for Computing and Network Platforms:
Network and communication systems Software defined networks Wireless and sensor networks Internet of Things (IoT) Cloud Computing Edge and Fog Computing
Paper Submission:
Full papers must be in English and should be between 12 to 14 pages. Short papers should be limited to 8 pages. Papers must be formatted in Springer's LNCS format. Submitted research papers may not overlap with papers that have already been published or that are simultaneously submitted to a journal or a conference with proceedings.
ORGANISING COMMITTEE
General Co-Chairs:
Joao Gama, University of Porto, Portugal Edwin Lughofer, Johannes Kepler University Linz, Austria
Program Co-Chairs:
Irfan Awan, University of Bradford, UK Hadi Larijani, Glasgow Caledonian University, UK
Local Organising Co-Chairs:
Perin Ünal, Teknopar, Turkey Sezer Gören, Ugurdag Yeditepe University, Turkey Tacha Serif, Yeditepe University, Turkey
Publication Chair:
Muhammad Younas, Oxford Brookes University, UK
Journal Special Issue Coordinator:
Lin Guan, Loughborough University, UK
Workshop Coordinator:
Filipe Portela, University of Minho, Portugal
Publicity Chair:
Esra N. Yolaçan, Osman Gazi University, Turkey _______________________________________________ computational.science mailing list computational.science@lists.iccsa.org https://lists.iccsa.org/mailman/listinfo/computational.science
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