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Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Because these algorithms tend to be highly effective over...
In open and distance education field, making use of data mining technologies to understand students' practical needs and usage habits about professional courses, which will greatly enhance students learning. China Open University system is Chinese largest scale organization engaging in open and distance education, and it has taken Chinese education ministry's a rural education project, called "one...
Based on the kernel method and graph theory, this paper proposes a novel Kernel Non-negative Matrix Factorization with Local and Non-local feature (LN-KNMF) approach for face recognition. We establish the objective function in kernel space which incorporates two scatter quantities, namely local scatter and non-local scatter. They are determined by the local adjacent graph matrix and non-local adjacent...
Mortality analytics is an emerging research area that discovers and communicates meaningful patterns in clinical data to reduce mortality rates. Nonetheless, intensive care unit (ICU) mortality analytics for leading causes, such as circulatory system diseases (CDS), is still complicated due to the interactions of different mortality causes. To improve analytics accuracy and quality, clustering analysis...
While research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from vehicles, we develop a time-varying road network data set where edge weights differ over time. We present our methodology and share this dataset, along with a graph manipulation tool...
Differential privacy (DP) has emerged as a popular standard for privacy protection and received great attention from the research community. However, practitioners often find DP cumbersome to implement, since it requires additional protocols (e.g., for randomized response, noise addition) and changes to existing database systems. To avoid these issues we introduce Explode, a platform for differentially...
Sentiment negation and negation scoring can be considered as major aspects of sentiment analysis. Social media sentiment analysis can be considered as an excellent source of information in today's business. But there is very minimal work has been done in sentiment negation scoring. All the existing negation scoring mechanisms are based on an adjective intensity approach. This research proposes a novel...
In many computer vision systems, one object can be described by multi-view data. Compared with individual view, multi-view data can contain complete and complementary information of the problem. But when views capture information which is uniquely but not complete enough to give an uniform learning performance, multi-view data may degrade the learning performance and it is therefore not an ideal solution...
Various players in the field of energy give a lot of importance to the evaluation of reliability of their electrical networks. Following our literature review, several limitations need to be pointed out. In this paper, we will present an accurate method of decision making based on constraint programming. Our main contributions are i) to propose a new generalized constraint based formalization of the...
Datamining is the process of extracting interesting information of patterns from large databases. One of the most important datamining task and well-researched is the association rules mining. It aims to find the interesting correlation and relations among sets of items in the transaction databases. One of the main problems related to the discovery of these associations that a decision maker faces...
The automatic detection of electrocardiogram waves presents an important step for cardiac disease diagnosis. In this work, we developed an algorithm for locating the waveform boundaries by using the empirical mode decomposition which has interesting properties concerning the pseudo periodic signals. The introduced method allows identifying an appropriate and optimum set of intrinsic mode functions...
Most of the instance selection methods seek to obtain subset of data for instance-based learning algorithms. These methods can improve classification performance, reduce memory requirements, and reduce execution time for these learning algorithms. In this paper, we introduce an instance selection algorithm (FF-IS) which is based on fuzzy frequent patterns and two thresholds. This method preserves...
Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper introduces a data driven method for alarm flood pattern matching. A modified BLAST algorithm with Levenshtein distance is proposed to discover similar alarm floods. A quadruple tank process is simulated to...
Over the recent years public transportation systems around the world have been migrating to digital ticketing solutions. This paper investigates security and privacy aspects of the one such system implemented by Riga municipality called e-talons by analysing published open data containing ride registrations.
Speech quality assessment performed by speech therapists is to some extent subjective and affected by the human factors. In order to objectify the evaluation, significant effort is invested in solutions that are non-invasive, sufficiently reliable and in compliance with diagnostic and therapeutic procedures. In this paper, the development of a system for automatic assessment of articulatory deviations...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
Individuality of handwriting is the reason why it is used as a common base element for detecting character traits of the writer. It is believed that dynamic information improve the accuracy of the analysis, but they are not contained in an offline handwritten text. In order to recover dynamic information, a novel approach for handwriting trajectory recovery is proposed in this paper. The procedure...
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...
Eye detection is a primary step in many applications such as face recognition, iris recognition, driver fatigue detection, gaze tracking etc. Occlusion by spectacles, glare and secondary image formations deteriorate its performance. In this paper, we formulate the glare/reflection removal as a classification problem and employ Low rank decomposition technique to overcome these challenges. We provide...
This paper aims at the risk early warning system developed to solve the prevention problem of hydraulic engineering migration risk. Under the support of big data produced by the relocation and resettlement, it uses the data mining technology to develop the Hydraulic Engineering Migration Risk Early Warning System, and the basic thoughts of the system design is to select the association rule data mining...
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