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Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
It is widely acknowledged that the value of a house is the mixture of a large number of characteristics. House price prediction thus presents a unique set of challenges in practice. While a large body of works are dedicated to this task, their performance and applications have been limited by the shortage of long time span of transaction data, the absence of real-world settings and the insufficiency...
This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as ‘Bad,’ ‘Normal,’ and ‘Good.’ These rules are used to interpret the three sleep statuses. To show the...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
In typical applications, chromatic indices are calculated as linear combinations of the normalized r-, g- and b-channels and used as features for a later classification based on chromatic appearance. But the variety of indices used in the literature is very limited. Furthermore is the choice of which index to use justified either empirically, based on false mathematical assumptions or not justified...
According to the noise and overlapping characteristics of agricultural irrigation water quality monitoring data for the comprehensive evaluation may bring about the boundary fuzzy problem. This paper proposes an improved Genetic Algorithm (GA) to avoid premature convergence, the global optimal solution of the function of the Projection Pursuit (PP) function is used as the comprehensive evaluation...
Circuit breakers are important equipment to control the energy distribution and fault isolation in power grid. With the development of the power grid in China, the evaluation of health status of circuit breaker in operation becomes an important part of the operation management. A number of studies have been made on the evaluation of circuit breaker health status, but are mostly limited to interpretation...
For the deficiency of the incidence model, the incidence model based on the included angle of vectors is presented and is extended to the panel data. In order to describe the incidence between the sequences, which is projected to n-dimensional vector. The included angle of vectors reacts the difference of the sequences in similarity. The incidence model based on the included angle of vectors is defined,...
This article presents a survey of the factors that indicate evasion in distance education, as well as the data mining techniques that are being used in the detection of evasion. As a methodology, we have used the systematic review, analyzing the works published in the last five years. The result indicated that there are multiple factors that influence evasion, which were systematized in four dimensions,...
Fault detection method using k nearest neighbor rule has shown its advantages in dealing with nonlinear, multi-mode, and nonGaussian distributed data. Once a fault is detected in industrial processes, recognizing fault variables becomes the crucial task subsequently. Recently, the method of fault variables recognition using k nearest neighbor reconstruction (FVR-kNN) has been reported. However, the...
For the risk management problem of key enterprises in the area, we propose a time-series based risk monitoring and assessment model which could manage and predict enterprises' dynamic risk levels. Our model takes a systematic method to monitor and evaluate enterprises' various types of risk indicators: by collecting real-time categorical data through pre-designed data-collection portal, the model...
An important field in exploratory sensory data analysis is the segmentation of time-series data to identify activities of interest. In this work, we analyse the performance of univariate and multi-sensor Bayesian change detection algorithms in segmenting accelerometer data. In particular, we provide theoretical analysis and also performance evaluation on synthetic data and real-world data. The results...
Traditional machine learning algorithms often require computations on centralized data, but modern datasets are collected and stored in a distributed way. In addition to the cost of moving data to centralized locations, increasing concerns about privacy and security warrant distributed approaches. We propose keybin, a distributed key-based binning clustering algorithm for high-dimensional spaces....
This study attempts to make use of traffic behaviour on the aggregate level to estimate congestion on urban arterial and sub-arterial roads of a city exhibiting heterogeneous traffic conditions by breaking the route into independent segments and approximating the traffic flow behaviour of the segments. The expected travel time in making a trip is modelled against sectional traffic characteristics...
In order to solve the classification prediction of dust pollution at different altitudes, the least square support vector machine(LS-SVM) and BP neural network is used to construct the distribution model. Built by LS-SVM, the accuracy of the model was verified by BP neural network with the realtime dust pollution data of different high monitored by Unmanned aerial vehicles. The data analysis shows...
There are so many works that have been performed to achieve semantic search, but few are integrated with enterprise applications such as documents management systems (DMS). While major search engines such as Google, Yahoo and Bing always introduce novel methods to enhance search experience, users of enterprise systems generally have to deal with form-based search infrastructures to access their data...
Examination timetable (ETT) is a complex administrative task at educational institutions that must fulfill various constraints to generate the ETT to schedule exam sessions within a precise period. The ETT problem could be modeled as Constraint Satisfaction Problems (CSPs). In addition, it could be particularly investigated by Constraint Logic Programming (CLP) approach. This paper uses a real examination...
This paper proposes a real-time identification method for auto-regressive with exogenous inputs and state-dependent parameters (ARX-SDP). This model is always non-linear. We conveniently adapt Young's off-line approach to an real-time approach with reduced computational cost. Young's approach focuses on discovering state-parameter dependence. This implies to unveil the nonlinear structure of the system...
Climate and land use land cover (LULC) are two important factors for changes in hydrological droughts in the Yellow River Basin (YRB). Quantifying their impacts on hydro-logical droughts is of great importance for drought prevention and mitigation. To better characterize hydrological droughts, we proposed a new drought index named Standard Surface water and Groundwater Index (SSGI) that considers...
Public safety has been discussed for many years, but how to use and understand crime data is still difficult. Limitations of previous research were mainly restricted by technology. The significant technological advance since last century provided a tool for researchers to compute large amount of data and complicate models while shrinking process time too. This research tried to combine results from...
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