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This paper proposes a pre-processing method to enhance the accuracy of wind power forecast. Instead of using the whole dataset indifferently for training, the proposed method only uses the segments that share the same pattern. In order to search for such segments in the historical data, a k-OCCO filter and a weighted multi-resolution morphological gradient (MMG) are employed. Afterwards, the forecast...
This paper focuses on the trustworthiness of data gathered from different sources, including crowdsensing and crowdsourcing, in pervasive systems. The specific focus is on mPASS (mobile Pervasive Accessibility Social Sensing), a system devoted to support mobile users with accessibility needs in a smart city context. mPASS is in charge of collecting data about urban and architectural barriers and facilities,...
Early design analysis is essential for better design definition and efficient balancing of design effort and risk. In this paper, we introduce the concept of virtual logic netlist (VLN), a potentially incomplete yet representative hierarchical and logical netlist graph of the design. VLN enables early and rapid register transfer level (RTL) analysis using accurate backend tool engines without the...
As technology to connect people across the world is advancing, there should be corresponding advancement in taking advantage of data that is generated out of such connection. To that end, next place prediction is an important problem for mobility data. In this paper we propose several models using dynamic Bayesian network (DBN). Idea behind development of these models come from typical daily mobility...
Iris recognition is one of the most accurate biometric technologies. The uniqueness of iris, also known as iris individuality, has been widely accepted as one foundation for iris recognition. Although a few iris individuality mod- els have been proposed, they are either incomplete or less accurate. In this paper, we investigate the iris individual- ity problem using Daugman’s iriscode method. We divide...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
The objective of the present work is to design a HADOOP based parallel Marathi content retrieval system using clustering technique to get the efficient and optimized result than existing systems. The system also focuses on providing the personalized documents in Marathi language to the end user based on their interests identified from the browsing history and using time session mechanism for re ranking...
For extorting the helpful comprehension concealed in the biggest compilation of a database the data mining technology is used. There are some negative approaches occurred about the data mining technology, among which the potential privacy incursion and potential discrimination. The latter consists of irrationally considering individuals on the source of their fitting to an exact group. Data mining...
Collaborative filtering is a successful approach where data analysis and querying can be done interactively. In large systems that contain huge data or many users, collaboration is often delayed by unrealistic runtimes. In any electronic application, the recommender systems play an important role as they help in making proper decisions on the basis of the recommendations that the system provides....
Haptic feedback has two important sources of dynamics: the machine being controlled and the haptic device itself. This paper concentrates on the means of identifying the dynamics of a Phantom Omni haptic feedback device. Two models are compared: a dynamic model with parameters using results from sinusoidal steady state analysis and a data driven model that uses pseudo-random binary sequences (PRBS)...
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases Now a day's large amount of data is. generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. Classification is a supervised machine learning task which builds a model from labelled...
This paper describes the development of the framework and the algorithm for large scale automatic speech recognition systems. Technical advances include the acceleration of decoding speed by leveraging the computational power of many-core graphic processing units (GPU), in order to solve the issue of training data sparseness, improvement in the accuracy by Subspace Gaussian Mixture Models (SGMM),...
Recently, a lot of research on the use of big data is made, and this paper was aimed to perform classification experiments using CNN for the detected object collected from traffic detectors. In addition the experimental results were compared with the HOG descriptor that is commonly used in existing pedestrian and object classification and wavelet, texture and descriptor that are used in the road surface...
For improving the forecasting accuracy of bank cash flow, a combined model based on back propagation (BP) neural network and grey prediction method is put forward based on the merits and demerits of both BP neural network and grey model prediction method. The proposed method has the advantage of two methods and makes up the deficiencies of single model as well. It can efficiently reduce the influence...
This paper proposes a novel approach to select features that are jointly predictive of survival times and classification within subgroups. Both tasks are common but generally tackled independently in clinical data analysis. Here we propose an embedded feature selection to select common markers, i.e. genes, for both tasks seen as a multi-objective optimization. The Coxlogit model relies on a Cox proportional...
Social media has become an important source of near-instantaneous information about events and is increasingly also being analysed to provide predictive models, sentiment analysis and so on. One domain where social media data has value is transport and this paper looks at the exploitation of Twitter data in traffic management. A key issue is the identification and analysis of traffic-relevant content...
In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The overall purpose of oracle coaching is to...
Early detection of hypertension generally requires continuous monitoring of blood pressure levels which is not facilitated by traditional methods such as the cuff, which cannot be used in the normal environment for continuous monitoring due to the regular pressurization of certain body parts. Thus there is a need for non-invasive continuous pressure monitoring mechanism. In this paper we present a...
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,...
Although Duchenne muscular dystrophy (DMD), the most common single-gene lethal disorder, is caused by a homogeneous biochemical defect in all patients, substantial patient-patient variety in disease progression is observed. The loss of ambulation (LoA) is a functional milestone of DMD progression and the age at LoA is often used as an indication of disease severity. But as age at LoA is not always...
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