Search results for "Intelligence"
showing 10 items of 6959 documents
The Rise of Distributed Artificial Intelligence Through Shared Data and Cloud Solutions
2021
Decision-makers of present times encounter influence by an ever-increasing emotional condition created by social media, market trends, experience, and historical facts. The concept of mixed human and artificial intelligence is one of the most underrated business drivers today, and conspiracy theories, fixed mindset, and legacy systems are slowing down collective evolution. This paper intends to contribute to the everyday awareness of data sharing through cloud solutions and services. It opens a wide range of possibilities for new solutions and insights that endorse a collaborative culture where a growth mindset paired with transparency and ethics reduces time-to-value in businesses, governm…
Diagnóstico de Enfermedades Card´ıacas con los algoritmos supervisados Naives Bayesian
2020
Las enfermedades cardíacas son la principal causa de muerte en la actualidad. Este paper contrasta la performance de los diferentes algoritmos supervisados de Machine Learning, que tienen aplicaciones en el a´rea de la medicina, con los algoritmos supervisados Naives Bayes para ayudar a clasificar pacientes propensos a sufrir enfermedades cardíacas. Como fuente de datos se usan 303 instancias de pacientes con diferentes características que fueron analizados al procesar los datos con los respectivos algoritmos. Los resultados con el algoritmo de Naives Bayes son pro- metedores, obteniendo una precisio´n del 86,81 %, usando la fuente de datos mencionada. Esta familia de algoritmos tiene un me…
Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption
2019
In machine learning applications in the energy sector, it is often necessary to have both highly accurate predictions and information about the probabilities of certain scenarios to occur. We address this challenge by integrating and combining long short-term memory networks (LSTMs) and online density estimation into a real-time data streaming architecture of an energy trader. The online density estimation is done in the MiDEO framework, which estimates joint densities of data streams based on ensembles of chains of Hoeffding trees. One attractive feature of the solution is that queries can be sent to the here-called forecast-based point density estimators (FPDE) to derive information from …
Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data
2018
Density estimation of streaming data is a relevant task in numerous domains. In this paper, a novel non-parametric density estimator called FRONT (forest of normalized trees) is introduced. It uses a structure of multiple normalized trees, segments the feature space of the data stream through a periodically updated linear transformation and is able to adapt to ever evolving data streams. FRONT provides accurate density estimation and performs favorably compared to existing online density estimators in terms of the average log score on multiple standard data sets. Its low complexity, linear runtime as well as constant memory usage, makes FRONT by design suitable for large data streams. Final…
Prototype-based learning on concept-drifting data streams
2014
Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To ide…
Online Density Estimation of Heterogeneous Data Streams in Higher Dimensions
2016
The joint density of a data stream is suitable for performing data mining tasks without having access to the original data. However, the methods proposed so far only target a small to medium number of variables, since their estimates rely on representing all the interdependencies between the variables of the data. High-dimensional data streams, which are becoming more and more frequent due to increasing numbers of interconnected devices, are, therefore, pushing these methods to their limits. To mitigate these limitations, we present an approach that projects the original data stream into a vector space and uses a set of representatives to provide an estimate. Due to the structure of the est…
Guiding the modeller: organizing and selecting experimental data for single cell models using the CoCoDat database
2003
Collating, organizing and selecting quantitative experimental data are time-consuming tasks necessary for building and constraining biophysically realistic neuronal models. The CoCoDat (Collation of Cortical Data) database has been designed as an advanced environment for storing, organizing and retrieving detailed, uninterpreted quantitative data on morphology, electrophysiology and connectivity from the published literature according to neurophysiological concepts. All experimental data are linked to exact bibliographical references and detailed records of procedures used in the experiments that produced the data. We demonstrate the usefulness of CoCoDat for implementation of an example mo…
The HisClima database: historical weather logs for automatic transcription and information extraction
2021
Knowing the weather and atmospheric conditions from the past can help weather researchers to generate models like the ones used to predict how weather conditions are likely to change as global temperatures continue to rise. Many historical weather records are available from the past registered on a systemic basis. Historical weather logs were registered in ships, when they were on the high seas, recording daily weather conditions such as: wind speed, temperature, coordinates, etc. These historical documents represent an important source of knowledge with valuable information to extract climatic information of several centuries ago. This paper presents a database for researching about the ca…
Reusability and modularity in transactional workflows
1997
Abstract Workflow management techniques have become an intensive area of research in information systems. In large scale workflow systems modularity and reusability of existing task structures with context dependent (parameterized) task execution are essential components of a successful application. In this paper we study the issues related to management of modular transactional workflows, i.e., workflows that reuse component tasks and thus avoid redundancy in design. The notion of parameterized transactional properties of workflow tasks is introduced and analyzed, and the underlying architectural issues are discussed.
Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis
2006
Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…