16^th Mexican International Conference on Artificial Intelligence

Title: Challenges on Big data based Clouds Health-Care for Risk Predictions based on Ensemble Classifiers and Subjective Analysis

Abstract: Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making. The challenges in big data analytics are the high dimensionality and complexity in data representation analytics especially for on-line feature selection. Granular computing and feature selection on data streams are among the challenge to deal with big data analytics that is used for Decision making. We will discuss these challenges in this talk and provide new projection on ensemble learning for on-line health care risk prediction. In decision making most approaches are taking into account objective criteria, however the subjective correlation among different ensembles provided as preference utility is necessary to be presented to provide confidence preference additive among it reducing ambiguity and produce better utility preferences measurement for good quality predictions. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analytics due to preprocessing and normalization processes which are expensive and difficult when data sets are raw, or imbalanced. We will highlight these issues through project applied to health-care for elderly, by merging heterogeneous metrics from multi-sensing environment for providing health care predictions for active aging elderly at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams using incremental learning for modified data. Subjectivity (i.e., service personalization) would be examined based on correlations between different contextual structures that are reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations.　We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in multiclass classification and decision support systems research domains.　In this talk I will present the current state of art and focus it on health care risk analysis applications with examples from our experiments.

Bio: Hamido Fujita is professor at Iwate Prefectural University (IPU), Iwate, Japan, as a director of Intelligent Software Systems. He is the Editor-in-Chief of Knowledge-Based Systems, Elsevier of impact factor (3.325) for 2015. He received Doctor Honoris Causa from Óbuda University in 2013, and a title of Honorary Professor from Óbuda University, Budapest, Hungary in 2011. He received honorary scholar award from University of Technology Sydney, Australia on 2012. He is Adjunct professor to Stockholm University, Sweden, University of Technology Sydney, National Taiwan Ocean University and others. He has supervised PhD students jointly with University of Laval, Quebec, Canada; University of Technology, Sydney, Australia; Oregon State University (Corvallis), University of Paris 1 Pantheon-Sorbonne, France and University of Genoa, Italy. He has four international Patents in Software System and Several research projects with Japanese industry and partners. He is vice president of International Society of Applied Intelligence, and Co-Editor in Chief of Applied Intelligence Journal, Springer. He has given many keynotes in many prestigious international conferences on intelligent system and subjective intelligence. He headed a number of projects including Intelligent HCI, a project related to Mental Cloning as an intelligent user interface between human user and computers and SCOPE project on Virtual Doctor Systems for medical applications.

Title: Named Entity Recognition in Challenging Contexts

Abstract: Named entities (NE), usually proper names, are the who, the what, the where and the when of events. NE recognition refers to the task of identifying the sequences of words that correspond to NE mentions, and this task is the first step in information extraction systems. NEs are also part of the pipeline of many natural language processing systems, including machine translation, summarization, and question answering. In the late 1990's and early 2000's there were several NE recognition shared tasks and because of the high performance of these systems (reported f-measures of 88% even in cross-lingual settings), this task was considered solved. This all changed with the widespread use of social media and micro-blogging sites. The relaxed grammar, the non-standard spellings and the frequent typos, combined with the great diversity in topics covered in social media data, create new challenges for text processing tools, including NE recognition systems.

In this talk I will present our ongoing efforts to solve the NE recognition task in text from social media and micro-blogging platforms, including Reddit, YouTube, Twitter and StackExchange. I will discuss the need to model text at different levels to achieve robustness to noise, and I will motivate our need to combine a traditional machine learning approach to NE recognition with the muscle of deep learning architectures. This system is the top performing system at the recent shared task on Novel and Emerging Entity Recognition hosted by the Empirical Methods in Natural Language Processing (EMNLP) Workshop on Noisy User Generated Text 2017. After discussing our most recent results, I will conclude the talk with a discussion on the outstanding challenges in this area.

Bio: Thamar Solorio is an Associate Professor in the Department of Computer Science at the University of Houston (UH). She is the founder and director of the Research in Text Understanding and Analysis of Language (RiTUAL) group at UH. Her main research interests include stylistic modeling of text, syntactic analysis of mixed language data, language assessment, and information extraction from social media data. She has M.S. and PhD degrees in Computer Science from INAOE, Puebla, Mexico. The Department of Defense and the National Science Foundation currently fund her research program. She is the recipient of a CAREER award for her work in authorship analysis, and the 2014 Denice Denton Emerging Leaders ABIE Award. She serves as editorial board member for the Journal on Artificial Intelligence Research (JAIR) and the Computer Speech and Language Journal.

Title: Deep Learning Theory, Algorithms, and Applications in the Natural Sciences

Abstract:  The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning--essentially learning in complex systems comprised of multiple processing stages--is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. In this talk we will first address some fundamental theoretical issues about deep learning through the theory of local learning and deep learning channels. We will then describe inner and outer algorithms for designing deep recursive neural architectures to process structured, variable-size, data such as biological or natural language sequences, phylogenetic or parse trees, and small or large molecules in biochemistry. Finally we will present various applications of deep learning to problems in the natural sciences, such as the detection of exotic particles in high-energy physics, the prediction of molecular properties and reactions in chemistry, and the prediction of protein structures in biology.

Bio: Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor's Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the prediction of protein secondary and tertiary structure in biology. He has written four books and over 300 peer-reviewed articles. He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is and Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.