S03 - MACHINE LEARNING METHODS APPLIED TO BIG DATA ANALYSIS, PROCESSING AND VISUALIZATION (MLBDAPV)

Dr. José García-Rodríguez, University of Alicante (Spain)

Dr. Enrique Domínguez, University of Alicante (Spain)

Dr. David Tomás, University of Alicante (Spain)

Dr. Jaime Oswaldo Salvador Meneses, Universidad Central del Ecuador (Ecuador)

Dr. Zoila Ruiz, Universidad Central del Ecuador (Ecuador)

The amount of data available every day is not only enormous, but growing at an exponential rate. Over the last years there has been an increasing interest in using machine learning methods to analyse and visualize massive data generated from very different sources and with many different features: social networks, surveillance systems, smart cities, medical diagnosis, business, cyberphysical systems or media digital data. This special session is designed to serve researchers and developers to publish original, innovative and state-of-the art machine learning algorithms and architectures to analyse and visualize large amounts of data.

This special session provides a platform for academics, developers, and industry related researchers belonging to the vast communities of Big Data, Machine Learning, Pattern Recognition, Visualization, Media Digital Data , and many others, to discuss, share experience and explore traditional and new areas of Data analysis, processing or visualization and machine learning combined to solve a range of problems. The objective of the workshop is to integrate the growing international community of researchers working on the application of Machine Learning applied to Big Data analysis, processing and visualization to a fruitful discussion on the evolution and the benefits of this technology to the society.

The topics of interest are those related with big data, but we are particularly interested in candidates who have conducted research in the theoretical or practical aspects of big data – algorithms, machine learning, deep learning, statistical learning methods applied to one or more domains – software engineering, media digital data, bio-informatics, health care, imaging and video, social networks, natural language processing and others.

It can be identified by, but are not limited to, the following subjects:

• Healthcare and medical diagnosis
• Social network modelling
• Financial risk assessment
• Marketing and E-commerce
• Multimedia data mining
• Visual surveillance
• Application Systems for Big Visual Data Understanding
• Education data mining
• Location big data mining
• Intelligent transportation system
• Web mining
• Text mining
• Sentiment analysis for social media
• Network security
• Smart cities
• Smart government
• Smart and cyberphysical devices
• Approximate and randomized methods for subspace learning,classification and clustering on Big Data
• Nonlinear learning techniques
• Distributed solutions for nonlinear big data processing and analysis
• Deep methods for representation learning, clustering and classification on Big Data
• Unsupervised and semi-supervised methods for Big Data
• Data-driven techniques for representation learning, clustering and classification on Big Data
• Big media data on the web and social networks
• Big multimedia data (signal, 2D/3D image, video) analysis in medicine, science and engineering
• Semantic visual analysis: human activity recognition, face/facial expression recognition, scene understanding, object detection and tracking, saliency detection
• Big Media Data applications, including media data summarization, post-processing, search and retrieval, video surveillance, robotics
• Big Media Data description, visualization and analytics
• Big cross-media analytics

Submission guidelines:

Please follow the regular submission guidelines of IWINAC 2019. Please notify the chairs of your submission by sending an email to: jgarcia@dtic.ua.es.