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In this paper a new decision support system with intelligent algorithm to screen high risk patients due to sudden cardiac death (SCD) patients for implanting cardiac defibrillator (ICD) has been developed. SCD is known as one of top killer in many developed countries. It is caused by the ventricular arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF). Thus, Implantable...
In the last decade we have witnessed a huge increase of interest in data stream learning algorithms. A stream is an ordered sequence of data records. It is characterized by properties such as the potentially infinite and rapid flow of instances. However, a property that is common to various application domains and is frequently disregarded is the very high fluctuating data rates. In domains with fluctuating...
The convergence of physical and digital worlds is creating unprecedented opportunities to enhance the travel experience for millions of people every day. A key to the success of these systems is a good understanding of driver behaviour under the influence of travel information. This paper presents the application of a new generation of driver behaviour models, based on neural agent (neugent) techniques,...
Researchers in sign language recognition customized different sensors to capture hand signs. Gloves, digital cameras, depth cameras and Kinect were used alternatively in most systems. Due to signs closeness, input accuracy is a very essential constraint to reach a high recognition accuracy. Although previous systems accomplished high recognition accuracy, they suffer from stability in realistic environment...
This paper gives a new approach for recognition of handwritten Devanagari characters. Twenty handwritten characters from 100 people resulting 2000 characters are used for the experimentation. The handwritten characters written of paper is scanned, preprocessed and on every individual characters wavelet transform is applied so as to get decomposed images of characters. Statistical parameters are computed...
Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances'...
Effective robotic grasping and manipulation requires knowledge about the surface properties of an object and the environment in which it is located. Physical contact with materials using tactile sensors can enable the retrieval of detailed information about the material, i.e. compressibility, surface texture and thermal properties. This paper describes a system used to classify a wide range of materials...
In the context-awareness problems, classification models are used to recognize the user scenes based on the sensor signal data. To improve the classification accuracy, a Dynamic Key Context Information Based scenes recognition method is proposed in this paper. Since the location information can be easily obtained by the sensors, the data set is divided into groups by their location information. An...
In this paper, a sub-band correlation-based method is proposed for the automatic detection of epilepsy and seizure. The analysis is carried out by decomposing the electroencephalogram (EEG) signals, collected from a publicly available EEG database, into the dual tree complex wavelet transform(DT-CWT) domain. An Artificial Neural Network(ANN) is employed as a classifier where the maximum cross-correlation...
Pulmonary acoustic signal analysis provides essential information on the present state of the Lungs. In this paper, we intend to distinguish between normal, airway obstruction pathology and interstitial lung disease using pulmonary acoustic signal recordings. The proposed method extracts Mel frequency cepstral coefficients (MFCC) and AR Coefficients as features from pulmonary acoustic signals. The...
Fast and robust traffic sign recognition is very important but difficult for the safety driving assist systems. This study addresses the fast and robust traffic sign recognition to enhance safety driving. We first adopt the typical Hough transform methods to implement coarse-grained locating of the candidate regions (shapes of rectangle, triangle and circle, etc.) of the traffic signs; and then propose...
This paper presents an intelligent system for the classification of ischemic stroke severity. The application of Artificial Neural Network (ANN) is proposed in this study to classify ischemic stroke severity using EEG sub bands Relative Power Ratio (RPR). There were 100 subjects from National Stroke Association of Malaysia NASAM, Petaling Jaya, Selangor, Malaysia divided into Early Group (EG), Intermediate...
This paper presents the work of acoustic analysis related to Modern Standard Arabic (MSA). The problem of classifying the consonant counterparts in MSA is tackled here. The study considers four phonemes: /dˤ, ðˤ/ and their non-emphatic counterparts /d, ð/ respectively. An accurate automatic classification for those phonemes is to be achieved. Artificial neural networks (ANNs) are used for that purpose...
Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form...
Recent years have seen significant progress in improving both the efficiency and effectiveness of time series classification. However, because the best solution is typically the Nearest Neighbor algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain elusive. Moreover, the recent explosion of interest in wearable...
Research community has recently put more attention to the Extreme Learning Machines (ELMs) algorithm in Neural Network (NN) area. The ELMs are much faster than the traditional gradient-descent-based learning algorithms due to its analytical determination of output weights with the random choice of input weights and hidden layer bias. However, since the input weights and bias are randomly assigned...
Most of the state-of-the-art methods for action recognition are very complex and variant to the geometric transformation like scaling, translation and rotation. Cuboid based method required all frames to extract the cuboid of action that's why cuboid based methods are expensive. Other methods use contour based approach for feature representation which is not robust to noise. So we require a very fast...
Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated statistical quality measures are favored in medicine, like precision, recall etc‥ Otherwise, most classification approaches in machine...
This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
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