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Emotions have a direct influence on an individual's physical and cognitive behaviour, as well as their performance, a student with a positive emotional state will learn and perform better. This paper presents an agent framework that addresses the relationship between user's state of emotion during learning and the modification of learning pace and feedback-type in a virtual environment for learning...
Reinforcement learning (RL) can be a tool for designing policies and controllers for robotic systems. However, the cost of real-world samples remains prohibitive as many RL algorithms require a large number of samples before learning useful policies. Simulators are one way to decrease the number of required real-world samples, but imperfect models make deciding when and how to trust samples from a...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling...
In this paper, we present the encoding produces technique for texture recognition. This produces are developed with the purpose to replaced the previous method. Firstly, we encode image informations by VTT (Volume Trace Transform), and trace functionals are chosen by RL (Reinforce Learning). The number of VTT results are extracted by the amount of trace functionals. Secondly, we present P-DFT (Plus-Discriminant...
The execution of an agent's complex activities, comprising sequences of simpler actions, sometimes leads to the clash of conflicting functions that must be optimized. These functions represent satisfaction, short-term as well as long-term objectives, costs and individual preferences. The way that these functions are weighted is usually unknown even to the decision maker. But if we were able to understand...
Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
The structure of a protein is closely correlated to its function. Feature dimension reduction method is one of most famous machine learning tools. Some researchers have begun to explore feature dimension reduction method for computer vision problems. Few such attempts have been made for classification of high-dimensional protein data sets. In this paper, feature dimension reduction method is employed...
The multicollinearity exists in the interpretive variable of regression model , it often brings inconvenience to social post-evaluation. The ridge regression has advantages than LS method. The support vector machines (SVM) is a novel machine learning tool in data mining. It is based on the structural risk minimization (SRM) principle, which has been shown to be more superior than the traditional empirical...
SVM is a novel type of statistical learning method that has been successfully used in speaker recognition. However, training SVM consumes long computing time and large storage space with all training examples. This paper proposes an improved sparse least-squares support vector machine (LS-SVM) for speaker identification. Firstly KPCA is exploited to reduce the dimension of input vectors and to denoise...
Discrimination of benign and malignant mammographic masses based on supervised and unsupervised learning methods help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram. For predicting the outcomes of breast biopsies, we propose Rotation Forest with twelve decision trees algorithms as base classifiers and Principal Component Analysis (PCA) as filter...
Sudden Cardiac Death (SCD) is an unexpected death caused by loss of heart function when the electrical impulses fired from the ventricles become irregular. Most common SCDs are caused by cardiac arrhythmias and coronary heart disease. They are mainly due to Acute Myocardial Infarction (AMI), myocardial ischaemia and cardiac arrhythmia. This paper aims at automating the recognition of ST-segment deviations...
Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone which is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location based services, marketing...
RFID localization is a promising new field of work that is eagerly awaited for many different types of applications. For use in a medical context, special requirements and limitations must be taken into account, especially regarding accuracy, reliability and operating range. In this paper we present an experimental setup for a medical navigation system based on RFID. For this we applied a machine...
In this article, a new Multi Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) is introduced. It is an improvement of a single OSELM (single agent) by combined multiple OSELMs (multi agents) with a final decision making module (parent agent). Here, the development of the parent agent is motivated by the Bayesian Formalism...
Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine...
Web automation programs offer a means for users to enhance the usability of the web. These programs can be published on a wiki or other repository, thereby making them available for use by other users. However, in addition to programs of broad usefulness to the community at large, these repositories also contain many programs that are unreliable or highly specialized to the needs of very small sub-...
Falls among the elderly is an important health issue. Fall detection and movement tracking are therefore instrumental in addressing this issue. This paper responds to the challenge of classifying different movements as a part of a system designed to fulfill the need for a wearable device to collect data for fall and near-fall analysis. Four different fall trajectories (forward, backward, left and...
In recent years, one mode of data dissemination has become extremely popular, which is the deep web. A key characteristics of deep web data sources is that data can only be accessed through the limited query interface they support. This paper develops a methodology for mining the deep web. Because these data sources cannot be accessed directly, thus, data mining must be performed based on sampling...
In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the...
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