The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts...
Unattended falls can lead to serious medical issues among the elderly, especially when motor functions may become inactive. Motion sensors like accelerometer can aid in automatic characterization and classification of human motion. Un-Classified motion can be accounted for anomaly that when reported to the online knowledge builder can correct the existing model or estimate additional classes into...
Hadoop architecture provides one level of fault tolerance, in a way of rescheduling the job on the faulty nodes to other nodes in the network. But, this approach is inefficient when a fault occurs after most of the job is executed. Thus, it's necessary to predict the fault at the node at quite an early stage so that the rescheduling of the job is not costly in terms of time and efficiency. Prediction...
Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions...
The number of vehicles that exists on public roads have increased drastically over the years. This have caused several problems, where one of the most common problem is traffic jam. There have been several studies that have tried to solve this problem, such as by using real time videos with computer vision, wireless sensor networks, and traffic data predictions. In this study, we proposed a modification...
Wireless sensor networks are gaining more and more attention these days. They gave us the chance of collecting data from noisy environment. So it becomes possible to obtain precise and continuous monitoring of different phenomenons. However wireless Sensor Network (WSN) is affected by many anomalies that occur due to software or hardware problems. So various protocols are developed in order to detect...
In patient monitoring systems the user-specific evaluation is essential to obtaining realistic result. This criterion poses a difficult task for the experts, because the large number of input factors and the complex interactions between them is hard to manage. In this paper some possibilities are presented, which can effectively be used to handle this task by different threshold definitions. The basic...
The timely detection of abnormal energy usage is one of the major ad-hoc techniques to optimize energy efficiency. Typically an alarm is triggered either by a significant drift from the baseline consumption level or by a period of large variations. In this paper we propose a statistical predictive method for detecting anomalies both in mean and in variation. The criterion behind is based on the prediction...
In this paper, a modified partial least-squares (PLS) regression modeling method is proposed. The proposed method can build a modified regression model to extract the useful information in residual subspace, which is helpful to predict the output variables. With this method, more accurate quality variables are predicted. In simulation experiment, penicillin fermentation process is used to test the...
Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers'...
A decision support system for operators monitoring complex systems is proposed. It consists in a fault isolation method based on pattern matching using binary information, in this case event lists. A training set composed of faults is used to create fault templates. Event lists generated by unknown faults are classified by comparing them with the fault templates using an original weighted distance...
In most recent Intelligent Video Surveillance systems, mechanisms to support human decisions are integrated in cognitive artificial processes. These algorithms mainly address the problem of extraction and modelling of relevant information from a sensor network. In crowd monitoring the main problem is to individuate specific events as for example different behaviours among interacting entities. A bio-inspired...
Learning metacognitive listening strategies and specializing in the implementation of such strategies enable not only a more efficient use of time but also easier and more sustained listening. This research examines first-year university students' awareness level of metacognitive listening comprehension strategies (MLCS) on learning English as a Foreign Language (EFL). We used Vandergrift et al.'s...
By using the health management technology the actuators can be timely responded to the fault situation so that it should be used the right approach to solve and prevent the occurrence of the fault and improve the system reliability. In this paper a method of the actuator health management is proposed by using neural network pattern recognition technology in the flight control system. Firstly, a particular...
A primary difficulty in physiological monitoring is detecting changes of health status for patients. In order to address this difficulty, we propose a new framework in patient-specific physiological monitoring by defining a density ratio using the training density and testing density to denote the changes of patient status, such as health, sub-health and abnormalities. We use a Least Square-based...
Personalized information retrieval and recommendation systems have been proposed to deliver the right information to users with different interests. However, most of previous systems are using keyword frequencies as the main factor for personalization, and as a result, they could not analyze semantic relations between words. Also, previous methods often fail to provide the documents that are related...
This paper analyzes the traditional mathematical models about monitoring TWCC. Combining with the catalysis exothermic reaction, BP ANN is introduced to develop the TWCC efficiency monitoring model to evaluate the performance of TWCC, which is based on the nonlinear relationship between TWCC efficiency and some input parameters that can be measured on the rebuilt engine test bench. The BP ANN model's...
Our contribution in this paper is two fold. First we provide preliminary investigation results establishing program based anomaly detection is effective if short system call sequences are modeled along with their occurrence frequency. Second as a consequence of this, built normal program model can tolerate some level of contamination in the training dataset. We describe an experimental system Sequencegram,...
The monitoring and management of the high density crowd in large scale public place is an important factor of city disaster reduction and mitigation. Automatic short term prediction of crowd density is a key problem. This paper introduces a prediction algorithm using v-support vector regression (v-SVR), which can control the accuracy of fitness and prediction error by adjusting the parameter v. An...
Telephony over IP is exposed to multiple security threats. Conventional protection mechanisms do not fit into the highly dynamic, open and large-scale settings of VoIP infrastructures, and may significantly impact on the performance of such a critical service. We propose in this paper a runtime risk management strategy based on anomaly detection techniques for continuously adapting the VoIP service...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.