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Discrete-event simulation (DES) has been used since the late 1950s. In contrast, agent-based simulation (ABS) is much newer but has been the “hottest” topic in simulation since 2005, despite a lack of agreement on what is an agent or ABS. We carefully define DES and ABS, and discuss their similarities and differences. We argue that emergence is not a fundamental tenet of ABS, as is often suggested...
One of the most important factors in human-computer interaction is the user emotional reaction. Interactive environments including serious games that are responsive to user emotions improve their effectiveness and user acceptances. Testing and training for user emotional competence is meaningful in health care, which has motivated us to analyze immersive affective games using feedback. In this article,...
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical...
People detection is essential in a lot of different systems. Many applications nowadays tend to require people detection to achieve certain tasks. These applications come under many disciplines, such as robotics, ergonomics, biomechanics, gaming and automotive industries. This wide range of applications makes human body detection an active area of research. With the release of depth sensors or RGB-D...
This paper addresses an investigation regarding the suitability of two different techniques, Active Basis Model (ABM) and Gabor based Convolutional Neural Network (CNN or G-ConvNets) in the mechanism for recognition of biological movement (mammalian visual system model). This method inspired by ventral streams which provide the form information. Both of these approaches contain information of the...
Accurate prediction of electricity demand is essential for planning, policy making and resource allocation in national level. In this manuscript, we applied a number of artificial intelligence methods to predict macro-scale electricity consumption rates in Iran. To this end, three socio-economic and three environmental factors were considered as inputs to the prediction models. We used data for the...
Lexicon plays a key role in Medical Language Processing (MLP) technology. Construction of semantic lexicon has become the prerequisite of MLP study in China where there are limited clinical terminology resources available. In this study, an iterative machine learning algorithm based on Conditional Random Field (CRF) was proposed aiming to automatically build a symptom lexicon from clinical corpus...
This paper presents a new method based both on Active Shape Model (ASM) and spatial distance model to segment brain structures. It combines two types of a priori knowledge: the structure shapes and the distances between them. This knowledge consists of shape and distance variability which are estimated during a training step. Then, the obtained models are used to guide simultaneously the evolution...
In Human Activity Recognition (HAR) supervised and semi-supervised training are important tools for devising parametric activity models. For the best modelling performance, typically large amounts of annotated sample data are required. Annotating often represents the bottleneck in the overall modelling process as it usually involves retrospective analysis of experimental ground truth, like video footage...
One of the major concerns in Wastewater Treatment Plant (WWTP) operation is that of satisfying the legal requirements that impose maximum allowable concentration levels for effluent pollutants. Not meeting these requirements may generate economic punishment in terms of fines in addition, of course, to the environmental consequences. The effluent limit violations is usually measured as a side performance...
Growing streams of the information and changes in all spheres of living demand the search of new ways of the person effective functioning in a modern information field. One of such ways is the use of information technologies in education system, in particular, in foreign language training of the future specialists. In this paper principles of development of the special professional educational environment,...
Medical subject headings (MeSH) is a controlled hierarchical vocabulary used by the National Library of Medicine (NLM) to index biomedical articles. In the 2014 version of MeSH terminology there are a total of 27,149 terms. Librarians at the NLM tag each biomedical article to be indexed for the Pub Med search system with terms from MeSH. This means the human indexers look at each article's full text...
Detection of complex human events in videos and images is a challenging problem of computer vision. The difficulty lies in constructing effective connection between human activities and specific events. In this paper we focus on dangerous human events, especially when people with handheld weapons are presented in images. By introducing Human-Object Interaction model, we are able to establish methods...
The problems of predicting the Protein-Protein Interactions (PPIs) are characterized by probabilistic constraints using the artificial neural network techniques. In the literature, no specific rules are proposed for determining whether two proteins interact, but various approaches have been proposed to collect the information about the interaction between the proteins. The need and importance of PPIs,...
In the area of malware analysis, static binary analysis techniques are becoming increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In these dynamic analysis systems, the malware samples are executed and monitored in a controlled environment...
The plight of ethnic medical technology, education, and industry in ethnic minorities was examined, and the combined “industry-education-research” experience of Jiangxi University of Traditional Chinese Medicine was analyzed. Facing key scientific issues and technological demands, the development of an innovative collaborative model for ethnic medical technology, education, and industry was examined,...
Noise is a prominent challenge found in many bioinformatics datasets and it refers to erroneous or missing data. The presence of noise in gene expression datasets has adverse effects on machine-learning techniques, such as supervised classification algorithms and feature selection techniques. Additionally, the identification of noise and its quantification are challenging tasks that require a proper...
Class imbalance is a significant challenge that practitioners in the field of bioinformatics are faced with on a daily basis. It is a phenomenon that occurs when number of instances of one class is much greater than number of instances of the other class(es) and it has adverse effects on the performance of classification models built on this skewed data. Random Forest as a robust classifier has been...
Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing...
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