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Use of modern technological advances in real-time biomedical analysis is very crucial. Current work focuses on glottal pathology discrimination based on non-invasive speech analysis techniques. Primary set back in developing such method is irregular performance depreciation of several state of the art acoustic features. To excuse such problems, we have used glottal to noise excitation ratio, which...
This paper aims at building a portable robotic hand for physically disabled people to perform basic hand movements. Surface Electromyography(EMG) signal is collected from muscles of human forearm to extract the subject's intentions of action, where six kinds of gestures are selected for discussion. An Artificial Neural Network(ANN) is trained and utilized to distinguish the desired movement according...
Two advanced modelling approaches, Multi-Level Models and Artificial Neural Networks are employed to model house prices. These approaches and the standard Hedonic Price Model are compared in terms of predictive accuracy, capability to capture location information, and their explanatory power. These models are applied to 2001–2013 house prices in the Greater Bristol area, using secondary data from...
Prediction stock price is considered the most challenging and important financial topic. Thus, its complexity, nonlinearity and much other characteristic, single method could not optimize a good result. Hence, this paper proposes a hybrid ensemble model based on BP neural network and EEMD to predict FTSE100 closing price. In this paper there are five hybrid prediction models, EEMD-NN, EEMD-Bagging-NN,...
In this paper, we propose a novel deep convex network method for domain adaptation in multitemporal remote sensing imagery. We fuse the capabilities of the extreme learning machine (ELM) classifier and local feature descriptor techniques to boost the classification accuracy. We use the Affine Scale Invariant Feature Transform (ASIFT) to extract the key points from the image pair, i.e. source and target...
An Artificial Neural Network (ANN) algorithm for the Soil Moisture Content (SMC) retrieval from the C-band EPS-SG SCA scatterometer, which will replace the Metop ASCAT, was implemented and tested with real data and model simulations. The main aim of this activity was in understanding the potential of VH channel, which inclusion on the mid-beam antenna of EPS-SG SCA is currently being considered, for...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
Most distributed sensing methods assume that the expected value of sensed information is same for all agents - ignoring differences in sensor capabilities due to, for example, environmental factors and sensors' quality and condition. In this paper, we present a distributed sensing framework to exploit heterogeneity in information provided about a dynamic environment using an active-passive networked...
Classification of signals acquired by condition monitoring systems for automotive application is becoming increasingly important. The work presented in this paper is motivated by a real-life classification challenge organized by Ford. Data samples from an automotive subsystem were collected. A classifier is designed to robustly isolate the different types of problems, by analyzing the acquired signals...
In developing the human-machine technology, it is essentially important to infer human mind state. A machine learning approach is promising to this need. However, the machine-learning approach essentially requires training data, ideally supervised training data, which may not be readily available. An idea is to overcome this shortcoming is to take the so-called subjective rate measure. Take the problem...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection...
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data sets. In this paper, we conduct a large empirical...
In recent years, water quality prediction has attracted many attentions of governments and researchers. The safety of water quality seriously affects the human health, fishery economy and agricultural activities. If an early prediction to the water quality with an acceptable accuracy can be achieved, the negative impacts will be minimized or even be avoided. Many researchers have applied artificial...
Two of the three most important causes of Information Technology projects failure have been related to a poor resource estimation. In average, software developers expend from 30% to 40% more effort than is estimated. Because that no single technique to estimate software development effort is best for all situations, it is important to propose new models to compare their results and then generate more...
We investigate the performance evaluation of merging (fusing) the classification capabilities of classifiers for the land use analysis. For the fusion approach, we select the parametric and non-parametric classifiers. The set used includes: Bayesian Network, Multi-layer Perceptron (MLP), Support Vector Machines (SVM) and Random Forest. These classifiers are selected based on their good over-all performance...
Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
Accurately predicting a user's rating to a service is a challenging task in the presence of malicious users that manipulate the ratings to services. Many existing service rating systems lack the ability that counter the manipulation of rating systems. This paper proposed an artificial neural network (ANN) based service rating scheme that counters the manipulation of service ratings. The scheme takes...
This paper presents a study aimed to assess applicability of artificial neural networks (ANNs) in human activity recognition from simple features derived from accelerometric signals. Secondary goal was to select the most descriptive signal features and sensor locations to be used as inputs to ANNs. Five triaxial accelerometers were attached to human body in the following places: one at back, two at...
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