Search results for "Intelligence"
showing 10 items of 6959 documents
Three-dimensional cardiac computational modelling: methods, features and applications
2015
[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty …
Visual Behaviour Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics
2012
For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehe…
A multi-agent system reinforcement learning based optimal power flow for islanded microgrids
2016
In this paper, a distributed intelligence algorithm is used to manage the optimal power flow problem in islanded microgrids. The methodology provides a suboptimal solution although the error is limited to a few percent as compared to a centralized approach. The solution algorithm is multi-agent based. According to the method, couples of agents communicate with each other only if the buses where they are located are electrically connected. The overall prizing system required for learning uses a feedback from an approximated model of the network. Based on the latter, a distributed reiforcement learning algorithm is implemented to minimize the joule losses while meeting operational constraints…
An expert system hybrid architecture to support experiment management
2014
Specific expert systems are used for supporting, speeding-up and adding precision to in silico experimentation in many domains. In particular, many experimentalists exhibit a growing interest in workflow management systems for making a pipeline of experiments. Unfortunately, these type of systems does not integrate a systematic approach or a support component for the workflow composition/reuse. For this reason, in this paper we propose a knowledge-based hybrid architecture for designing expert systems that are able to support experiment management. This architecture defines a reference cognitive space and a proper ontology that describe the state of a problem by means of three different per…
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
2017
There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II fro…
Factors Affecting Entrepreneurship and Business Sustainability
2018
Abstract: Sustainability is becoming increasingly important for society, and the creation of business ventures is one area where sustainability is critical. We examined the factors affecting actions that are designed to foster business sustainability. These factors are related to the environment, behavior, human relations, and business activity. Based on questionnaire responses from experts, the Analytic Hierarchy Process (AHP) method was used to rank sustainable business criteria according to their importance for entrepreneurs starting sustainable businesses. The results indicate that the most important drivers of sustainable entrepreneurship are behavioral factors and business factors. Et…
The Relationship Between Entrepreneurship and Digitalization - Spotlight on the EU Countries
2020
Abstract In the current context of the modern world, economies depend on a dynamic innovative environment, the innovation actually representing one of the main facilitators of change. The economic growth has certainly led to an exponential increase with respect to the interaction between innovation and entrepreneurship. Undoubtedly, innovation depends on the entrepreneurial context and mostly on the cooperation between the market process’ level actors. In order to implement the innovation in the entrepreneurial activities, there is a sine qua non condition of having the abilities to contribute as a promoter on spreading and adoption of innovation. The current innovations are, in certain sit…
Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
2021
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contamin…
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.
2020
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total …
DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection
2020
Abstract Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regula…