Modeling with Gaussian mixture regression for lactationmilk yield in Anatolian buffaloes

2016 ◽  
Author(s):  
Abdullah Yesilova ◽  
Ayhan Yilmaz ◽  
Gazel Ser ◽  
Baris Kaki
Keyword(s):  
Milk Yield ◽  
Environmental Effects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Lactation Period ◽  
Multivariate Statistical ◽  
Yield Data ◽  
Data Set ◽  
Mixture Regression ◽  
Lactation Duration

The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p less than 0.01). In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

GMM with parameters initialization based on SVD for network threat detection

2021 ◽  
Vol 40 (1) ◽  
pp. 477-490
Author(s):  
Yanping Xu ◽  
Tingcong Ye ◽  
Xin Wang ◽  
Yuping Lai ◽  
Jian Qiu ◽  
...  
Keyword(s):  
Gaussian Mixture Models ◽  
Singular Values ◽  
Gaussian Mixture ◽  
Singular Value ◽  
Optimal Parameters ◽  
Clustering Methods ◽  
Data Set ◽  
Detection Model ◽  
Clustering Model ◽  
Threat Behavior

In the field of security, the data labels are unknown or the labels are too expensive to label, so that clustering methods are used to detect the threat behavior contained in the big data. The most widely used probabilistic clustering model is Gaussian Mixture Models(GMM), which is flexible and powerful to apply prior knowledge for modelling the uncertainty of the data. Therefore, in this paper, we use GMM to build the threat behavior detection model. Commonly, Expectation Maximization (EM) and Variational Inference (VI) are used to estimate the optimal parameters of GMM. However, both EM and VI are quite sensitive to the initial values of the parameters. Therefore, we propose to use Singular Value Decomposition (SVD) to initialize the parameters. Firstly, SVD is used to factorize the data set matrix to get the singular value matrix and singular matrices. Then we calculate the number of the components of GMM by the first two singular values in the singular value matrix and the dimension of the data. Next, other parameters of GMM, such as the mixing coefficients, the mean and the covariance, are calculated based on the number of the components. After that, the initialization values of the parameters are input into EM and VI to estimate the optimal parameters of GMM. The experiment results indicate that our proposed method performs well on the parameters initialization of GMM clustering using EM and VI for estimating parameters.

The impact of controlled breeding on milk production in pastoral goats in northern Kenya: an application of polynomial growth curve fitting

2003 ◽  
Vol 140 (3) ◽  
pp. 357-371 ◽  
Author(s):  
I. HARY
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Growth Curve ◽  
Rainy Season ◽  
Polynomial Growth ◽  
Linear Mixed Model ◽  
Lactation Period ◽  
Yield Data ◽  
Northern Kenya ◽  
The Impact

The paper investigates the effect of controlled seasonal breeding on milk production in a herd of Small East African (SEA) goats. Polynomial growth curve models were fitted to both daily and cumulative milk yield data obtained from an experiment conducted over a period of 4 years (1984–88) under simulated pastoral herd management in Isiolo District, northern Kenya. The experimental treatment consisted of six different mating seasons per year, which were replicated three times over the course of the experiment.Milk yields in the first 2 weeks of lactation were negatively affected (<400 g/day) when kidding took place between June and September, whereas maximum initial yields of about 450 to 550 g/day were achieved at the onset and during the long rainy season. Multiple peaks in milk yield curves were observed when a rainy season occurred after about the first half of the lactation period. In terms of total amount of milk produced until 28 weeks of lactation, the production system could benefit from the introduction of a restricted breeding management allowing does to be bred in the period from June to November, with total milk yields being estimated at approximately 60 kg of milk. Maximum milk production until weaning can be expected to be achieved by does mated between October and January (between 46 and 48 kg of milk). The present experiment has revealed that mating just prior to or during the long rainy season leads to low milk yields until weaning and significantly increases the incidence of early kid deaths.It is concluded that evaluating milk production in goat herds exposed to strong seasonal changes in forage supply is perhaps best carried out in terms of cumulative milk yields, instead of average daily yields, which are subject to large fluctuations. Furthermore, under these conditions fitting polynomial growth curves to longitudinal milk yield data using the general linear mixed model appears to be more appropriate than the estimation of non-linear algebraic lactation curves.

scholarly journals The Information Bottleneck and Geometric Clustering

2019 ◽  
Vol 31 (3) ◽  
pp. 596-612 ◽  
Author(s):  
DJ Strouse ◽  
David J. Schwab
Keyword(s):  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Selection Procedure ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Data Set ◽  
Information Bottleneck ◽  
Geometric Clustering ◽  
Data Points ◽  
Intuitive Method

The information bottleneck (IB) approach to clustering takes a joint distribution [Formula: see text] and maps the data [Formula: see text] to cluster labels [Formula: see text], which retain maximal information about [Formula: see text] (Tishby, Pereira, & Bialek, 1999 ). This objective results in an algorithm that clusters data points based on the similarity of their conditional distributions [Formula: see text]. This is in contrast to classic geometric clustering algorithms such as [Formula: see text]-means and gaussian mixture models (GMMs), which take a set of observed data points [Formula: see text] and cluster them based on their geometric (typically Euclidean) distance from one another. Here, we show how to use the deterministic information bottleneck (DIB) (Strouse & Schwab, 2017 ), a variant of IB, to perform geometric clustering by choosing cluster labels that preserve information about data point location on a smoothed data set. We also introduce a novel intuitive method to choose the number of clusters via kinks in the information curve. We apply this approach to a variety of simple clustering problems, showing that DIB with our model selection procedure recovers the generative cluster labels. We also show that, in particular limits of our model parameters, clustering with DIB and IB is equivalent to [Formula: see text]-means and EM fitting of a GMM with hard and soft assignments, respectively. Thus, clustering with (D)IB generalizes and provides an information-theoretic perspective on these classic algorithms.

Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

2017 ◽  
Vol 5 (1) ◽  
pp. 109 ◽  
Author(s):  
Abrham Debasu Mengistu ◽  
Dagnachew Melesew Alemayehu
Keyword(s):  
Speech Processing ◽  
Speaker Identification ◽  
Hybrid Approach ◽  
Gaussian Mixture Models ◽  
Back Propagation ◽  
Gaussian Mixture ◽  
Back Propagation Neural Network ◽  
Identification System ◽  
Noisy Environments ◽  
Data Set

<p>In Ethiopia, the largest ethnic and linguistic groups are the Oromos, Amharas and Tigrayans. This paper presents the performance analysis of text-independent speaker identification system for the Amharic language in noisy environments. VQ (Vector Quantization), GMM (Gaussian Mixture Models), BPNN (Back propagation neural network), MFCC (Mel-frequency cepstrum coefficients), GFCC (Gammatone Frequency Cepstral Coefficients), and a hybrid approach had been use as techniques for identifying speakers of Amharic language in noisy environments. For the identification process, speech signals are collected from different speakers including both sexes; for our data set, a total of 90 speakers’ speech samples were collected, and each speech have 10 seconds duration from each individual. From these speakers, 59.2%, 70.9% and 84.7% accuracy are achieved when VQ, GMM and BPNN are used on the combined feature vector of MFCC and GFCC. </p>

scholarly journals Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ning Wang ◽  
Yang Xu ◽  
Hongbin Ma ◽  
Xiaofeng Liu
Keyword(s):  
Muscle Fatigue ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Robot Learning ◽  
Movement Trajectory ◽  
Mixture Regression ◽  
Learning Tasks ◽  
Training Stage ◽  
Short Time ◽  
Semg Signals

To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.

scholarly journals Segmentation of Bone with Region Based Active Contour Model in PD Weighted MR Images of Shoulder

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Aysun Sezer ◽  
Hasan Basri Sezer ◽  
Songul Albayrak
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Gaussian Mixture Models ◽  
Original Data ◽  
Gaussian Mixture ◽  
Initial Contour ◽  
Proton Density ◽  
Mr Images ◽  
Data Set

Proton density (PD) weighted MR images present inhomogeneity problem, low signal to noise ratio (SNR) and cannot define bone borders clearly. Segmentation of PD weighted images is hampered with these properties of PD weighted images which even limit the visual inspection. The purpose of this study is to determine the effectiveness of segmentation of humeral head from axial PD MR images with active contour without edge (ACWE) model. We included 219 images from our original data set. We extended the use of speckle reducing anisotropic diffusion (SRAD) in PD MR images by estimation of standard deviation of noise (SDN) from ROI. To overcome the problem of initialization of the initial contour of these region based methods, the location of the initial contour was automatically determined with use of circular Hough transform. For comparison, signed pressure force (SPF), fuzzy C-means, and Gaussian mixture models were applied and segmentation results of all four methods were also compared with the manual segmentation results of an expert. Experimental results on our own database show promising results. This is the first study in the literature to segment normal and pathological humeral heads from PD weighted MR images.

scholarly journals Ellipsoidal mixture models for clustering: an application to web-based education data

2016 ◽  
Vol 28 (1) ◽  
pp. 1-7
Author(s):  
Saujanna Jafreen ◽  
Taslim S Mallick ◽  
Jafar A Khan
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Real Data ◽  
Gaussian Mixture ◽  
Information Criteria ◽  
Data Set ◽  
Web Based ◽  
Education Data ◽  
Misclassification Rates ◽  
Web Based Education

This study considers the Gaussian mixture models for clustering. Since spherical and diagonal models occur very rarely in practice and analysis can be simplified when these models are implemented, we focus on the ellipsoidal models. EM algorithm is used to fit these models to a real data set related to an adaptive educational electronic course. Misclassification rates and Bayesian Information Criteria (BIC) values are used for comparison.Bangladesh J. Sci. Res. 28(1): 1-7, June-2015

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

Engagement Through the Diaspora Channel

2017 ◽  
Author(s):  
Michael S. Danielson
Keyword(s):  
Civil Society ◽  
Statistical Models ◽  
State Level ◽  
The State ◽  
Multivariate Statistical ◽  
Political Factors ◽  
Data Set ◽  
Level Data ◽  
Municipal Level

The first empirical task is to identify the characteristics of municipalities which US-based migrants have come together to support financially. Using a nationwide, municipal-level data set compiled by the author, the chapter estimates several multivariate statistical models to compare municipalities that did not benefit from the 3x1 Program for Migrants with those that did, and seeks to explain variation in the number and value of 3x1 projects. The analysis shows that migrants are more likely to contribute where migrant civil society has become more deeply institutionalized at the state level and in places with longer histories as migrant-sending places. Furthermore, the results suggest that political factors are at play, as projects have disproportionately benefited states and municipalities where the PAN had a stronger presence, with fewer occurring elsewhere.

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