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In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
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...
The dysregulations of long intergenic non-coding RNAs (lincRNAs) have shown to be linked with a wide variety of human diseases over the past few years. However, there are only a few lincRNA-disease association inference tools available with most of them relying on very specific type of prior knowledge about the lincRNAs and the diseases. They fall short in generalized association predictions when...
Word prediction is an applicable task for medical purposes and it can be done by analyzing brain's activities. Functional Magnetic Resonance Imaging (fMRI) is a technique for obtaining 3D images, related to the neural activity of brain through time. By subtracting fMRI images, which are captured consecutively, brain's operation can be detected. In this paper, a novel approach, based on machine learning...
A hybrid least square support vector machine (LSSVM) is proposed to predict the boiler combustion efficiency. In this approach, a principal component analysis (PCA) is employed to reconstruct new variables as the input of the predictive model. Then, a particle swarm optimization (PSO) algorithm optimized LSSVM is proposed. The parameters of LSSVM are optimized dynamically by PSO and the output value...
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...
The Escherichia coli (E. coli, ATCC 25922), Staphylococcus aureus (S. aureus, ATCC 29213), and Salmonella (SE, ATCC 14028) are three common bacterial pathogens of BSIs (Bloodstream infection). Accurately identifying these three bacterial pathogens will greatly help doctors to reduce the number of days to cure the patients. In this study, the identification models for bloodstream infection are studied...
Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations...
As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel...
Peach is one of the most important commodities in the global fresh product market. With the development of people's living standard, consumers pay more attention to the internal quality of fruits than the appearance quality of fruits. The requirement of nondestructive analysis could be satisfied by near infrared (NIR) spectroscopy with appropriate data analysis methods. In this paper, we measured...
Anaerobic ammonium oxidation (anammox) process has been recognized as efficient biological nitrogen removal process, which has the advantages of cost-effective and low energy compared to the conventional nitrification and denitrification processes. However, the efficient operation and control is limited due to the complexity of nonlinear and biochemical phenomena involved. This paper proposes an appropriate...
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...
A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction...
According to the characteristics of car ownership prediction influenced by multi-factor and non-linear, a combination forecasting model was proposed based on principal component analysis (PCA) and BP neural network for the purpose of car ownership prediction. Take the national car ownership as an example, the principal component analysis is carried out on the factors affecting the car ownership, and...
A rapid method for identification of plastics based on Raman spectroscopy with the combination of support vector machine (SVM) is presented in this paper. Plastics studied consist of polyethylene, polyethylene terephthalate, polymethyl methacrylate, polyacetal, polypropylene, polystyrene and polyvinyl chloride. With spectral preprocessing and principal component analysis (PCA), support vector machine...
Short-term prediction of water demand provides basic guarantee of water supply system operation and management. In this study, an effective model for daily water demand forecasting is proposed. Firstly, principle component analysis (PCA) is utilized to simplify the complexity and reduce the correlation between influence variables, and the score values of selected principle components (PCs) turn into...
Video blogs (vlogs) are a popular media form for people to present themselves. In case a vlogger would be a job candidate, vlog content can be useful for automatically assessing the candidates traits, as well as potential interviewability. Using a dataset from the CVPR ChaLearn competition, we build a model predicting Big Five personality trait scores and interviewability of vloggers, explicitly targeting...
It is important to cut down the erection time and the operation guidance by studying the shield machine tool failure. In this paper, an ACO-BP algorithm based tool failure prediction model is established by utilizing the nonlinear mapping characteristics of neural network and mining data characteristics from the subway. According to the practical problems, the dependent variables and the independent...
Incipient fault detection (FD) and prediction are crucial for the safe operation of in-orbit satellite's attitude control system (ACS). In this paper, a locally linear embedding (LLE) model combined with exponentially weighted moving average (EWMA) technique is proposed in FD for ACS, which is more suitable when the magnitude of the fault is small. After that, fault trend prediction with multi variables...
DNA methylation (DNAm) is an epigenetic mechanism used by cells to control gene expression, and identification of DNAm biomarkers can assist in early diagnosis of cancer. Identification of these biomarkers can be done using CpG (Cytosine-phosphate guanine) sites, or particular regions in DNA. Previous machine learning methods known as MS-SPCA and EVORA have been used to link DNAm biomarkers to specific...
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