Lidar data has proved to be a promising data source for various mapping and 3D modeling of buildings. Nevertheless, traditional manually building extraction from Lidar data is highly labor-intensive, time-consuming and very expensive. During the past decade many researchers in photogrammetry, remote sensing and computer vision communities have been trying to study and develop the automatic or s...

Motivation: In the past years, we and others have developed some basic families of algorithmic components for coping with multiple aspects of learning. It is natural to probe directly into the evolution of intelligence and learning mechanisms and into the problem of distributed intelligence such as collective learning, coordination and competition. In this project, we focus on an agent-based mo...

A complex system usually encompasses agents, as they are known from modern Artificial Intelligence. Within the system dynamics, interactions between agents are affected by perturbations (uncertainty), which often make modeling difficult. This paper introduces a modeling approach based on a function that quantifies uncertainty: the fuzzy measure of ambiguity. A model based on ambiguity minimizat...

The use of mathematical modeling and optimization in nutrition with the help artificial intelligence is indeed a trendy promising approach to data processing. With ever-increasing amount being generated field nutrition, it has become necessary develop new tools techniques process analyze these data. paper presents study on development neural-networks-based model investigate parameters related o...

Cognitive neuroscientists and psychological researches are progressively showing how emotions play a crucial role in cognitive processes. Gradually, this knowledge is being used in artificial intelligence and artificial life areas in simulation and cognitive processes modeling. However, theoretical aspects of emotions to be employed in computational systems projects are barely discussed and tou...

gence, in cooperation with Stanford University’s Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011, at Stanford University. The titles of the eight symposia were Artificial Intelligence and Health Communication, Artificial Intelligence and Sustainable Design, Artificial Intelligence for Business Agility, Computational Physiolo...

The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics biomass considering effective parameters ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, sulfur content) process heating rate. Through total 149 pyrolysis kinetics, thi...