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The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a...
Effective prediction of unobservable degradation can assist to schedule preventive maintenance and reduce unexpected downtime for realistic industrial systems. In this paper, an extended time-/condition-based framework is proposed for the Probability Density Function (PDF) prediction of unobservable industrial wear. Furthering our earlier work of unobservable degradation estimation, a stage-based...
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. It will be a daunting task for system administrators to manually keep track of the execution status of a large number of virtual machines all the time. Anomaly prediction is an effective approach to enhancing availability and reliability of Cloud infrastructures...
To alleviate the loads of tracking web log file by human effort, machine learning methods are now commonly used to analyze log data and to identify the pattern of malicious activities. Traditional kernel based techniques, like the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy. However, the user of a kernel based techniques normally cannot get...
Elasticity is a key feature in cloud computing, and perhaps what distinguishes it from other computing paradigms. Despite the advantages of elasticity, realizing its full potential is hard due to multiple challenges stemming from the need to estimate workload demand. A desirable solution would require predicting system workload and allocating resources a priori, i.e., A predictive approach. Instead,...
In this paper, we study on the popularity prediction of online user-generated contents, where high quality predictions give us much more flexibility and preparing time in deploying limited resources (such as advertising budget, monitoring capacity) into more popular contents. However the high retrieval cost of data used in prediction is a big challenge due to the large amount of users and contents...
Ensuring high reliability of large-scale clusters is becoming more critical as the size of these machines continues to grow, since this increases the complexity and amount of interactions between different nodes and thus results in a high failure frequency. For this reason, predicting node failures in order to prevent errors from happening in the first place has become extremely valuable. A common...
Pressure ulcer is one of the most prevalent problems for bed-bound patients in hospitals and nursing homes. Pressure ulcers are painful for patients and costly for healthcare systems. Accurate in-bed posture analysis can significantly help in preventing pressure ulcers. Specifically, bed inclination (back angle) is a factor contributing to pressure ulcer development. In this paper, an efficient methodology...
Due to the rapid and continuous development of computer networks, more and more intrusion detection techniques are proposed to protect our systems. However, there is a weak anomaly detection problem among the existing system call based intrusion detection systems: the pattern value range of abnormal system call sequences generated by attacks always overlaps to that by normal behaviors so it is difficult...
In this paper, we propose SWIFTNET: a fast-reactive data acquisition scheme. SWIFTNET is built on the synergies between compressive sensing and prediction algorithms and limits the energy consumption in environmental monitoring and surveillance networks. We show how this approach dramatically reduces the amount of communication required to monitor the sensor readings in a deployment. We use a wildfire...
Online learning has been showing to be very useful for a large number of applications in which data arrive continuously and a timely response is required. In many online cases, the data stream can have very skewed class distributions, known as class imbalance, such as fault diagnosis of realtime control monitoring systems and intrusion detection in computer networks. Classifying imbalanced data streams...
Today's competitive business climate and the complexity of IT environments dictate efficient and cost effective service delivery and support of IT services. This is largely achieved through automating of routine maintenance procedures including problem detection, determination and resolution. System monitoring provides effective and reliable means for problem detection. Coupled with automated ticket...
Although the future mean of intracranial pressure (ICP) is of critical concern of many clinicians for timely medical treatment, the problem of forecasting the future ICP mean has not been addressed yet. In this paper, we present a nonlinear autoregressive with exogenous input artificial neural network based mean forecast algorithm (ANNNARX-MFA) to predict the ICP mean of the future windows based on...
This paper focuses on accuracy and robustness of parameters inversion in probability integral method by genetic algorithm. For this, uniform design experimental method, subsidence prediction software and genetic algorithm program are used. Result shows that parameters in probability integral method can be retrieved precisely by genetic algorithm with relative errors of the retrieved parameters are...
Some mentally impaired but otherwise physically healthy individuals may have a tendency of wandering or difficulties using public transportation. Intelligent assistive technologies which are able to learn the individual's travel behavior and prompt anomalous events such as when the person deviates from expected destinations would greatly enhance the independence and safety of the individual and also...
Nowadays the overlay network has greatly improved the performance of the Internet. The overlay network flexibly selects its communication paths and targets and thus can benefit from estimation of end-to-end network performances. For an overlay network with n end hosts, most of the existing systems have to send O (n2) probes into the network and then they calculate the performances of all links. Although...
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...
In order to monitor continuously moving phenomena such as wile fire and hazardous bio-chemical material in wireless sensor network, boundary tracking approach has been widely used by reason of its huge scale and extensive diffusion property. With the boundary tracking scheme, the energy efficiency is expected to improve if only sensor nodes near the boundary of continuous object actively participate...
Due to the severe resource constraints of sensor hardware, energy efficiency is one of critical factors for monitoring the movement of the large-scale phenomena such as wild fire and hazardous bio-chemical material, denoted by continuous objects. In order to save energy, most of existing research on tracking the continuous objects focuses on finding the ways to minimize the communication cost through...
One of the major problems in managing large-scale distributed systems is the prediction of the application performance. The complexity of the systems and the availability of monitored data have motivated the applicability of machine learning and other statistical techniques to induce performance models and forecast performance degradation problems. However, there is a stringent need for additional...
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