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In this paper, a novel classifier for classification problems, based on increment support vector data description, is proposed. The proposed method is the expand version of increment support vector data description by bring in two classes of sample. Because of the addition of two kinds of sample. This method can reflect the target sample distribution state more complete in super ball space. The results...
Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support...
In this paper we present a methodology for monitoring of human activities in home using audio recordings captured from mobile phone. Specifically, after estimating a large set of audio features, unsupervised clustering is performed in order to extract feature subspaces. Human activity sound models were trained using different combinations of these subspaces. The best performance 92.46% was achieved...
Now a day's failure of system outages in cloud application is the main drawbacks of cloud environment. It reduces the economic losses for the business environment. Anomaly detection in the cloud system will reduce the loss. Anomaly detection at the user end is difficult, particularly Rolling Upgrade operation. Due to the enormous anomaly the operations are indistinguishable. It is very difficult to...
Nonintrusive load monitoring (NILM) is a procedure for the analysis of the changes in the power (current and voltage) that goes into households and classifying the appliances used in the house according to their individual energy consumption. Utility companies use smart electric meters accompanied with NILM to examine the particular uses of electric power in households. Focus of this paper is on the...
Currently, the air quality monitoring becomes important things for knowing the value of air pollution especially in the cities. In our previous research, we built the air quality monitoring system using wireless sensor network (WSN). Each sensor nodes will transmit all of the air quality data to the base station controller (BSC). This data consists of weather condition, temperature, humidity, carbon...
Selection and use of pattern recognition algorithms is application dependent. In this work, we explored the use of several ensembles of weak classifiers to classify signals captured from a wearable sensor system to detect food intake based on chewing. Three sensor signals (Piezoelectric sensor, accelerometer, and hand to mouth gesture) were collected from 12 subjects in free-living conditions for...
Self-healing is an interesting topic in SON (Self- Organizing Networks). In this paper, we investigate cell outage detection problem, and propose an improved TCM (Transductive confidence machines) based automatic cell outage detection algorithm. By incorporating a hypothesis test with the Neyman-Pearson criterion to improve the detection accuracy, the improved TCM can effectively detect cell outage...
In this paper Levenberg-Marquardt, Conjugate gradient, Resilient back-propagation algorithms are compared for power quality monitoring. Three Networks are trained in MATLAB. Each network is trained with the single algorithm mentioned above. Data for training is generated with the help of numerical model of power quality events in MATLAB. Voltage sag and swell is taken into consideration. The networks...
Fault diagnosis has a significant role in enhancing the safety, reliability, and availability of complex systems. However, the problem of enormous condition monitoring data and multiple failure modes makes the diagnostics great challenge. The imbalance between normal and fault monitoring data will increase the false alarm rate and the false negative rate. On the other hand, discrete monitoring data...
The estimation of remaining useful life applied to industrial machinery and its components is one of the current trends in the advanced manufacturing field. In this context, this work presents a reliable methodology applied to ball bearings health monitoring. First, the proposed methodology analyses the available vibration and temperature data by means of the Spearman coefficient. This step allows...
Automatic interpretation of electrocardiography provides a non-invasive and inexpensive technique to analyze the heart activity for different cardiac conditions. The emergence of smartphones and wireless networks has made it possible to perform continuous Holter monitoring on patients or potential patients. Recently, much attention has been paid to the development of the monitoring methodologies of...
An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. IDS's are based on the belief that an intruder's behavior will be noticeably different from that of a legitimate user. Many IDS has been...
Online traffic classification has been widely used in quality of service measurements, network management and security monitoring. Currently, more and more research works tend to apply machine learning techniques to online traffic classification, and most of them are based on supervised learning and unsupervised learning techniques. Although supervised learning method has exhibited good classification...
In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have...
In the process of bridge structural health monitoring, the parameters monitored by sensors includes strain, vibration, distortion, cable tension etc.. Classification of each parameter can reflect the change of bridge structural health to some extent. According to feature of parameter data, solving methods, namely, improved single-view cooperative-training semi-supervised learning method and multi-view...
In this paper we present some preliminary results of our project concerning about developing an image recognition technique for detection of certain household objects, mainly based on the shape and the Haar-like features. Shape feature is used to find the position of certain shape household objects preliminarily. Then, Haar-like feature-based recognition may be only performed for small regions around...
This paper proposes a system of using machine learning algorithms to extract communication session information, such as conference bridge number and participant code, from users' emails or appointments. Our system can then use the retrieved information to easily setup a communication session, for example, dialing conference bridge number and participant code, as well as popping up web conference links...
Artificial Olfaction (AO) data analysts have gained long term experience on nervous system based machine learning metaphors such as Artificial Neural Networks. In this work we propose and evaluate the use of a novel tool based on an emerging, however, powerful metaphor: the Artificial Immune Systems (AIS). AIS models were developed in the `90s; ever since they have reached significant maturity, and...
In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both...
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