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 Internet of Things empowers citizens to interconnect their devices, such as smart phones, into large-scale participatory decentralized networks, which they can use to make real-time collective measurements as public good, for instance, crowd-sourcing the monitoring of traffic in a city. This approach is an alternative to big data analytics systems that are often expensive to access, privacy-intrusive...
Knowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments...
Repeated exposures to psychological stress can lead to or worsen diseases of slow accumulation such as heart diseases and cancer. The main challenge in addressing the growing epidemic of stress is a lack of robust methods to measure a person's exposure to stress in the natural environment. Periodic self-reports collect only subjective aspects, often miss stress episodes, and impose significant burden...
We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our...
Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. In this paper we establish a framework for predicting true sequences from NGS data. We formulate this task as a classification problem. We define several features, such as log likelihood ratio of estimated true counts, error probability...
Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named UDEED is proposed...
Handling changes over time in supervised learning (concept drift) lately has received a great deal of attention, a number of adaptive learning strategies have been developed. Most of them make an optimistic assumption that the new labels become available immediately. In real sequential classification tasks it is often unrealistic due to task specific delayed labeling or associated labeling costs....
Regression is one of the effective techniques for data analysis in a WSN. Besides distributed data, the limited power supply and bandwidth capacity of nodes makes doing regression difficult in WSNs. Conventional methods, which employ some numerical optimization techniques such as Nelder-Mead simplex and gradient descent, generally work in a pre-established Hamiltonian path among the nodes. Low estimation...
Health care information systems tend to capture data for their research in order to promote the level of people's health status and the satisfaction of individuals in societies. To this aim, many approaches have been taken and examined. Data mining is one of the newest analytical methods that have been used to serve medical science research and has been shown to be a valid, sensitive, and reliable...
The performance of a kernel-based method is usually sensitive to a choice of the values of the hyper parameters of a kernel function. In this paper, we present a novel framework of using wavelet kernels in the kernel principal component analysis (KPCA) in order to better explain the nonlinear relationships among original multivariate data. We propose to introduce dilation and translation factors into...
The accuracy and fairness of the assessment results to a large extent limited by the choice of methods of assessment data processing. In the current practice of the equipment support core competencies (ESCC) assessment, there are two methods of assessment data processing mainly used, which are named the simple arithmetic average method and the weighted arithmetic average method. In this paper, an...
Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis...
In large-scale compute cloud systems, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs are becoming an increasingly important concern to system designers and administrators. When a system fails to function properly, health-related data are valuable for troubleshooting. However, it is challenging to effectively...
With based-data of DOM and vector data in land change survey and the research object of emphasis land class, we can establish an accurate, rapid and objective data validity evaluation system based on remote sensing to evaluate the validity of the updated data quantitatively in land change survey to second land survey in the future. This paper researched two main aspects, just as follows: the principle...
Spatial data quality (data accuracy, precision, consistency and so on) is a key issue in Geographic Information System. Geographical boundary inconsistency will directly affect the correctness and efficiency of analysis in GIS application. This paper describes a framework for checking and correcting geographical boundary inconsistency. Two kinds of inconsistency are identified: geometric inconsistency...
The life cycle of GIS attribute data is a complex system, which consists of collecting data, extracting digital information, integrating information, and so on. Attribute uncertainty can directly affect the quality of GIS- based decision making. A number of theories and methods to deal with attribute uncertainty and the propagation of the attribute uncertainty are discussed. A propagation model of...
Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis...
Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different...
The non-equal-interval direct Verhulst new information GM(1,1) model with two times fitting was built which extended equal interval to non-equal-interval and suited for general data modeling and estimating parameters of direct Verhulst GM(1,1). The new model chooses the nth component of X(0) as the starting conditions of the grey differential model. The new model need not pre-process the primitive...
Result Validation is an important phase of guidance simulation VV&A process, which main purpose is testing the consistency of simulation data and flight data or other standard data. Result Validation mainly involves the data analysis and calculate activities, therefore, developing the result validation tool (RVT), make use of computer's computing instead of manual calculation, can consumedly increase...
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.