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Context-Aware Recommendation Systems has gained lots of attention in both industry and academic research. Factorization Machines (FM) based recommendation has been successfully used in sparse industrial datasets for user personalized video recommendations. FM is a collaborative filtering technique for predicting a target such as user rating, given observations of interaction between some users and...
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...
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using a newly defined metric – area...
It is found that the GARCH (1,1) model has a good fitting effect on the time series by the statistical analysis of the logarithmic yield of the closing price of the Shanghai Composite Index. Therefore, this paper first uses the model GARCH (1,1) predicts the daily closing price of the Shanghai Composite Index, and then uses the Fourier series to correct the predicted residuals to obtain the final...
The mass monitoring data collected by the on-line monitoring of the substation is stored in the Hadoop Distributed File System (HDFS), and the index table structure of the online monitoring data is optimized and stored in the distributed structured database (HBase) Quick access to monitoring data. Based on Hadoop 's online monitoring data processing experiment platform, a fast fault identification...
Significant increases in computational resources have enabled the development of more complex and spatially better resolved weather and climate models. As a result the amount of output generated by data assimilation systems and by weather and climate simulations is rapidly increasing e.g. due to higher spatial resolution, more realisations and higher frequency data. However, while compute performance...
The intersectoral model MEMMAS was developed as a tool for describing and forecasting the economy in the context of substantial changes in the external and internal conditions of its development. MEMMAS combines a multi-agent description of the economy functioning with the interbranch input-output balance methodology. This model helped to conduct the forecasting studies of the impact of Urals oil...
We show how model based prediction can be employed in the construction of a speech codec which operates entirely in the frequency domain of a Modified Discrete Cosine Transform (MDCT). The codec tools described in this paper are part of the Dolby AC-4 system standardized by ETSI and included in ATSC 3.0.
Broadcast television is one of the most important telecommunication systems in Brazil. Such relevance requires that an assessment of the coverage and quality of the Digital TV signal must be extensively performed in the pursuit of technical excellence. The study of coverage prediction models would allow broadcasters to project their stations in a way to distribute their respective signals harmoniously...
In Japan, public transport such as bus services play an essential role in providing the growing aging population with mobility. However, both public and private Japanese bus services are in a precarious position with many of them running at a deficit due to the existence of convenient alternative means such as private cars. Therefore, this research proposes a new model to predict bus user demand based...
Stock market prediction has attracted a lot of attention from both business and academia. In this paper, we implement a model based on Recurrent Neural Networks (RNN) with Gated Recurrent Units (GRU) to predict the stock volatility in the Chinese stock market. We also propose many price related features which are used as inputs for our model. Apart from that, we carefully select official accounts...
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...
In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the...
Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
Financial market dynamics forecasting has long been a focus of economic research. A hybridizing functional link artificial neural network (FLANN) and improved particle warm optimization (PSO) based on wavelet mutation (WM), named as IWM-PSO-FLANN, for forecasting the CSI 300 index is proposed in this paper. In the training model, it expands a wider mutation range while apply wavelet theory to the...
Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However, one needs to encode a syntax to indicate the chosen...
This paper compares two torque control methodologies, namely Field Oriented and Model Predictive ones, for monitoring Interior Permanent Magnet Motor (IPMM) over a wide speed range. IPMM magnetic saturation and cross coupling effects are taken into consideration, increasing significantly the precision of the developed controllers. Maximum Torque per Ampere (MTPA) and Field Weakening (FW) operating...
Remaining Useful Life (RUL) prediction of bearings is one of the crucial conditions for timely maintenance. In this paper, a fuzzy multimodal extreme learning regression is proposed for the RUL estimation. In this method, fuzzy fusion, ensemble empirical mode decomposition (EEMD), and extreme learning machine (ELM) are integrated. The fuzzy fusion is first used to fuse original features for establishing...
As a fundamental cloud service for modern Web applications, the cloud object storage system stores and retrieves millions or even billions of read-heavy data objects. Serving for a massive amount of requests each day makes the response latency be a vital component of user experiences. Due to the lack of suitable understanding on the response latency distribution, current practice is to use overprovision...
For a developing country such as India, to have the best usage of resources, public planning requires good forecasts of future trends. India's Index of Industrial Production (IIIP) is an index which conveys the status of production in the industrial sector of the economy. In this study, an artificial neural network (ANN) was applied to forecast IIIP. Accordingly, the inputs to the ANN consisted of...
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