At the stage of formation of intermediate diagnostic hypotheses, the system will present to the user physician a hypothesis specific to the verbal and visual characteristics.
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At the same time, it is necessary to take into account the need to use fuzzy logic at the stages of the derivation of solutions. Approaches to the construction of cognitive linguistic-image models of knowledge representation for medical intelligent systems. Sci Tech Inform Process ; Sequences of Images in Intelligent Systems. J Theor Biol ; DOI: A survey on rough set theory and its applications.
The role of similarity judgment in intuitive problem solving and its modeling in a sheaf-theoretic framework. In: L. Wang, S. Halgamuge and X. Yao, eds. Similarity-based inference as evidential reasoning. Int J Approx Reason ; Doi: A modal account of similarity-based reasoning. Biomedical Decision Support Systems. How to cite. Examples of biomedical decision support systems are: Information retrieval systems that help clinicians find clinical guidelines accurately and quickly Intelligent systems that suggest diagnoses based on patient symptoms Simulation systems that calculate the outcomes of complex biochemical pathway to predict treatment outcomes Data analysis systems that summarize, interpret, and visualize hundreds of microarray assays identifying genes that are under- or over-expressed in cancer tissue.
The workshop follows the formatting guidelines for standard paper submissions to the AAAI main track. Papers can be submitted via EasyChair and will be selected based on a peer review process. Stefano Albrecht s. Subramanian Ramamoorthy s. Preferences are a central concept of decision making. As preferences are fundamental for the analysis of human choice behavior, they are becoming of increasing importance for computational fields such as artificial intelligence, databases, and human-computer interaction.
Nearly all areas of artificial intelligence deal with choice situations and can thus benefit from computational methods for handling preferences. Moreover, social choice methods are also of key importance in computational domains such as multiagent systems. This broadened scope of preferences leads to new types of preference models, new problems for applying preference structures, and new kinds of benefits.
Preferences are inherently a multidisciplinary topic, of interest to economists, computer scientists, operations researchers, mathematicians and more. The workshop on Advances in Preferences Handling promotes this broadened scope of preference handling. The workshop seeks to improve the overall understanding of the benefits of preferences for those tasks. Another important goal is to provide cross-fertilization between different fields. The main topics are preferences in Artificial Intelligence, multiagent systems, database systems, applications of preferences, preference elicitation, representation, and modeling, and properties and semantics of preferences.
The program will consist of presentations of peer-reviewed papers, panel discussions about future challenges, and an invited talk. We expect between 30 and 40 submissions and thus around 15 presentations. We therefore target a one-day workshop. The workshop authors are required to use the AAAI style files to prepare their papers.
Papers may be no longer than 6 pages and must be submitted in PDF format. Charles Ave. New Orleans, LA Cities are realizing that opening access to their many data sources and using semantic models to provide a holistic view of this heterogeneous data can unleash economic growth, optimize their operational and strategic goals while addressing computational sustainability issues. We call the cities committed to a semantic infrastructure as a way to integrate, analyze and standardize access to their open data, "Semantic Cities".
A number of cities for example, London, Chicago, Washington D.
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These technologies, principles and good practices are maturing and are becoming a perfect playfield for research-grade, scalable and robust AI techniques. This workshop aims to bring clarity and foster the communication among AI researchers, domain experts and city and local government officials. In that context, we want to: Provide a forum for sharing best-practices and pragmatic concerns among both AI researchers and domain experts.
Draw the attention of the AI community to the research challenges and opportunities in semantic cities. Foster the development of standard ontologies for city knowledge.
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Discuss the multidisciplinary and synergistic nature of the different subdomains of semantic cities for example, transportation, energy, water management, building, infrastructure, healthcare Identify the technical and pragmatic challenges needed to mature the technologies behind Semantic Cities. Elaborate a semantic data benchmark for testing AI techniques on semantic cities. We encourage submissions that show the application of AI technologies to the publication and use of city open data, and how to create a computationally sustainable, economically viable information ecosystems.
We want to include work that either discusses the advancement of foundational technologies in Semantic Cities information and knowledge management, ontologies and inference models, data integration, and others. We also encourage submissions from communities engaged in open data and corresponding standardization efforts, not necessarily within the AI community.
Semantic platforms to integrate, manage and publish government data Provenance, access control and privacy-preserving issues in open data Collaborative and evolving semantic models for cities. Challenges and lessons learned. Semantic data integration and organization in cities: social media feeds, sensor data, simulation models and Internet of things in city models. Big data and scaling out in Semantic cities. Managing big data using knowledge representation models Knowledge acquisition, evolution and maintenance of city data Challenges with managing and integrating real-time and historical city data.
Process and standards for defining, publishing and sharing open city government data Platforms and best practices for city data interoperability Foundational and applied ontologies for semantic cities. City applications involving semantic model Intelligent user interfaces and contextual user exploration of semantic data relating to cities Use cases, including, but not limited to, transportation traffic prediction, personal travel optimization, carpool and fleet scheduling , public safety suspicious activity detection, disaster management , healthcare disease diagnosis and prognosis, pandemic management , water management flood prevision, quality monitoring, fault diagnosis , food food traceability, carbon-footprint tracking , energy smart grid, carbon footprint tracking, electricity consumption forecasting and buildings energy conservation, fault detections.
The workshop continues the workshop on semantic cities at AAAI , IJCAI , whose attendees backgrounds included knowledge representation, AI planning and scheduling, multiagent systems, constraints satisfiability and search. The workshop will consist of papers, poster presentations, demonstrations, a panel, an invited talk, and discussion sessions, in a one full day schedule.
The schedule will follow the schedule of the and editions, all grouped by topic and type invited talk, long, short and demonstration papers, panel. Papers must be formatted in AAAI two-column, camera-ready style. Regular research papers submitted and final , which present a significant contribution, may be no longer than 7 pages, where page 7 must contain only references, and no other text whatsoever.
Papers are to be submitted online at EasyChair. In the 21st century, we live in a world where data is abundant. We would like to use this data to make better decisions in many areas of life, such as industry, health care, business, and government. This opportunity has encouraged many machine learning and data mining researchers to develop tools to benefit from big data.
However, the methods developed so far have focused almost exclusively on the task of prediction. As a result, the question of how big data can leverage decision-making has remained largely untouched. This workshop is about decision-making in the era of big data. The main topic will be the complex decision-making problems, in particular the sequential ones, that arise in this context.
Examples of these problems are high-dimensional large-scale reinforcement learning and their simplified version such as various types of bandit problems. These problems can be classified into three potentially overlapping categories: 1 Very large number of data-points.
Examples: data coming from user clicks on the web and financial data. In this scenario, the most important issue is computational cost. Any algorithm that is super-linear will not be practical. Examples are found in robotic and computer vision problems. The only possible way to solve these problems is to benefit from their regularities.
Here the immediate observed variables do not have enough information for accurate decision-making, but one might extract sufficient information by considering the history of observations. If the time series is projected onto a high-dimensional representation, one ends up with problems similar to 2.
Some potential topics of interest are the following: Reinforcement learning algorithms that deal with one of the aforementioned categories; Bandit problems with high-dimensional action space Challenging real-world applications of sequential decision-making problems that can benefit from big data.
Example domains include robotics, adaptive treatment strategies for personalized health care, finance, recommendation systems, and advertising. The workshop will be a one-day meeting consisting of invited talks, oral and poster presentations from participants, and a final panel-driven discussion. We expect about 30—50 participants from invited speakers, contributed authors, and interested researchers.
We invite researchers from different fields of machine learning for example, reinforcement learning, online learning, active learning , optimization, systems distributed and parallel computing , as well as application-domain experts from for example, robotics, recommendation systems, personalized medicine, and others.
Accepted papers will be presented as posters or contributed oral presentations. Amir-massoud Farahmand decision. Amir-massoud Farahmand McGill University; amirf ualberta. These fields share many key features and often solve similar problems and tasks. Until recently, however, research in them has progressed independently with little or no interaction. The fields often use different terminology for the same concepts and, as a result, keeping-up and understanding the results in the other field is cumbersome, thus slowing down research.
Our long term goal is to change this by achieving a synergy between logical and statistical AI, and this workshop will serve as a stepping stone towards realizing this big picture view on AI. Statistical relational AI is currently provoking a lot of new research and has tremendous theoretical and practical implications.
Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late s. Practically, successful statistical relational AI tools will enable new applications in several large, complex real-world domains including those involving big data, social networks, natural language processing, bioinformatics, the web, robotics and computer vision.
Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to effectively handle the former while probability helps her effectively manage the latter. The focus of the workshop will be on general-purpose representation, reasoning and learning tools for StarAI as well as practical applications.
We intend the Statistical Relational AI workshop to be a one day session with around 50 attendees, a number of paper presentations and poster spotlights, a poster session, and invited speakers. All submitted papers will be carefully peer-reviewed by multiple reviewers and low-quality or off-topic papers will be rejected. In the tightly interconnected world of the 21st century, infectious disease pandemics remain a constant threat to global health.
At the same time, noncommunicable diseases have become the main cause of global disability and death, imposing a crushing burden on societies and economies around the world. Public Health Intelligence obtained through intelligent knowledge exchange and real-time surveillance is increasingly recognized as a critical tool for promoting health, preventing disease, and triggering timely response to critical public health events such as disease outbreaks and acts of bioterrorism.
This intelligence is created by increasingly sophisticated informatics platforms that collect and integrate data from multiple sources, and apply analytics to generate insights that will improve decision-making at individual and societal levels. Driven by omnipresent threats to public health and the potential of public health intelligence, governments and researchers now collect data from many sources, and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors.
Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease to enable early, automatic detection of emerging outbreaks and other health-relevant patterns.
themisanthropelondon.com/za-azitromicina-y.php Given the ever-increasing role of the World Wide Web as a source of data for public health surveillance, accessing, managing, and analyzing its content has brought new opportunities and challenges; particularly for nontraditional online resources such as social networks, blogs, news feed, twitter posts, and online communities due to their sheer size and dynamic structure. The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in public health.
The scope of the workshop includes, but is not limited to, the following areas:. This workshop aims to bring together a wide range of computer scientists, biomedical and health informaticians, researchers, students, industry professionals, representatives of national and international public health agencies, and NGOs interested in the theory and practice of computational models of web-based public health intelligence to highlight the latest achievements in epidemiological surveillance based on monitoring online communications and interactions on the World Wide Web.
The workshop will promote open debate and exchange of opinions among participants. We invite researchers and industrial practitioners to submit their original contributions following AAAI format through EasyChair. Three categories of contribution are sought: full-research papers up to 8 pages; short paper up to 4 pages; and posters and demos up to 2 pages.
David L. John S. Buckeridge McGill University, Canada, david.