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In this paper, we address interesting questions about how feng shui influences house price from a data perspective. First, is feng shui likely to influence house price? Second, how do different feng shui features, e.g., house shape, master bedroom location, and other interior room arrangements, influence the price? Third, can we automatically diagnose the feng shui problems of a house? From a dataset...
In this study, we propose a two-stage method for material segmentation in hyperspectral images. The first stage employs a Convolutional Neural Network (CNN) to predict the material label at individual pixels. The second stage further refines the segmentation by a fully-connected Conditional Random Field (CRF) framework. For the first stage, we experimented with two different network architectures...
Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud...
NB-UVB Phototherapy is one of the most common treatments administrated by dermatologists for psoriasis patients. Although in general, the treatment results in improving the condition, it also can worsen it. If a model can predict the treatment response before hand, the dermatologists can adjust the treatment accordingly. In this paper, we use data mining techniques and conduct four experiments. The...
In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
Predicting an approval rate of politicians is a popular task. While a type of prediction is using a text mining from news articles, we introduce a text augmented Gaussian process to perform the prediction with contexts. We test our model with 2017 South Korea Presidential Election in 1) a quantitative evaluation, and 2) a qualitative analysis. The performance of the model with text input is better...
Aiming at the problem of mine fault prediction, a fault prediction model based on KPCA and Pearson correlation coefficient is proposed. The model obtains the abnormal sampling data by the kernel principal component method, extracts the abnormal sampling data and draws the contribution plots, then the Pearson correlation coefficient is compared with the existing fault contribution plots. Finally, according...
Aiming at the problem that the dual-frequency ultrasonic extraction of puerarin is difficult to detect effectively and efficiently with the method of manually watching and off-line detection, a method of soft sensor modeling is proposed. In the method, the genetic algorithm and the support vector machine (GA-SVM) are combined to build the soft measurement model of the puerarin extraction. By using...
Many existing scene parsing methods adopt Convolutional Neural Networks with fixed-size receptive fields, which frequently result in inconsistent predictions of large objects and invisibility of small objects. To tackle this issue, we propose a scale-adaptive convolution to acquire flexiblesize receptive fields during scene parsing. Through adding a new scale regression layer, we can dynamically infer...
This paper presents the results of using the Least-Squares Support Vector Machines (LS-SVMs) framework for estimating CO2 levels at the Holst Center building in the Netherlands. Within the IoT framework, a Wireless Sensor Network (WSN) consisting of seven sensors is currently deployed at the third floor of the building. Each sensor node provides measures of temperature, relative humidity and CO2 levels,...
MicroRNAs (miRNAs) are a class of small noncoding RNAs which have close relations with human diseases. Herein, predicting the novel associations between human diseases and miRNAS is urgently needed. However, only use of the experimental approaches to identity such relations have many choke points such as time-consumption and high cost. In this study, we adopt a network-based inference (NBI) based...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
It is non-trivial to optimise computations of chaotic systems since slightly perturbed simulations diverge exponentially over time due to the well-known butterfly effect if bit-reproducible results are not achieved. Therefore, two model setups that show the same quality in the representation of a chaotic system will show uncorrelated behaviour if integrated long enough, hence it is challenging to...
Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
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...
The accurate prediction of crude oil output plays an important role in the deployment of oilfield development and ensuring stable production. Crude oil output forecast is the premise and the core project management system of the whole oil production, while crude oil output is a dynamic system affected by multivariate variables. To accurately predict crude oil output, this paper presents a method to...
The need for systems capable of conducting inferential analysis and predictive analytics is ubiquitous in a global information society. With the recent advances in the areas of predictive machine learning models and massive parallel computing a new set of resources is now potentially available for the computer science community in order to research and develop new truly intelligent and innovative...
In this paper, a series of spatiotemporal data is analyzed by a regression method based on the theory of support vector machine (SVM). The support vector regression (SVR) model is used to predict the remote sensing data sets effectively. Firstly, we studied how to build a SVR model for spatiotemporal series prediction, and studied the problems of the test data processing, model parameters selection...
Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may...
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