Machine learning approaches for early prediction of adult wool growth and quality in Australian Merino sheep

2018 ◽  
Vol 148 ◽  
pp. 72-81 ◽  
Author(s):  
S. Shahinfar ◽  
L. Kahn
Keyword(s):  
Machine Learning ◽  
Early Prediction ◽  
Learning Approaches ◽  
Merino Sheep ◽  
Wool Growth ◽  
Australian Merino

scholarly journals Early prediction of diabetes using Feature Transformation and hybrid Random Forest Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 787-791
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Experimental Result ◽  
Early Prediction ◽  
Learning Approaches ◽  
Feature Transformation ◽  
Common Chronic Disease ◽  
Diabetes Prediction ◽  
Diagnosis Accuracy ◽  
Prediction Of Diabetes

Diabetes is the most common chronic disease among the world. Early prediction of these will assist the physicians to provide the improved treatment. Machine learning approaches are widely used for predicting the disease at the earlier stage. However the selecting the significant features and the suitable classifier are still reduces the diagnosis accuracy. In this paper the PCA based feature transformation and the hybrid random forest classifier is utilized for diabetes prediction. PCA attempt to identify the best subset of transformed components that greatly improves the classification result. The system is compared with priori machine learning approaches to evaluate the efficiency of this work. The experimental result shows that the present study enhances the prediction accuracy.

Across population genetic parameters for wool, growth, and reproduction traits in Australian Merino sheep. 1. Data structure and non-genetic effects

2007 ◽  
Vol 58 (2) ◽  
pp. 169 ◽  
Author(s):  
E. Safari ◽  
N. M. Fogarty ◽  
A. R. Gilmour ◽  
K. D. Atkins ◽  
S. I. Mortimer ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Age Groups ◽  
Genetic Effects ◽  
Production Traits ◽  
Merino Sheep ◽  
Wool Growth ◽  
Reproduction Traits ◽  
Fleece Weight ◽  
Growth And Reproduction ◽  
Australian Merino

Accurate estimates of adjustment factors for systematic environmental effects are required for genetic evaluation systems. This study combined data from 7 research resource flocks across Australia to estimate genetic parameters and investigate the significance of various environmental factors for production traits in Australian Merino sheep. The flocks were maintained for several generations and represented contemporary Australian Merino fine, medium, and broad wool bloodlines over the past 30 years. Over 110 000 records were available for analysis for each of the major wool traits, with over 2700 sires and 25 000 dams. Univariate linear mixed animal models were used to analyse 6 wool, 4 growth, and 4 reproduction traits. This first paper outlines the data structure and the non-genetic effects of age of the animal, age of dam, birth-rearing type, sex, flock, bloodline, and year, which were significant with few exceptions for all production traits. Age of dam was not significant for reproduction traits and fleece yield. Generally, wool, growth, and reproduction traits need to be adjusted for age, birth-rearing type, and age of dam before the estimation of breeding values for pragmatic and operational reasons. Adjustment for animal age in wool traits needs to be applied for clean fleece weight (CFW), greasy fleece weight (GFW), and fibre diameter (FD) with inclusion of 2 age groups (2 years old and >2 years old), but for reproduction traits, inclusion of all age groups is more appropriate. For GFW, CFW, and hogget weight (HWT), adjustment for only 2 dam age groups of maiden and mature ewes seems sufficient, whereas for birth (BWT), weaning (WWT), and yearling (YWT) weights, adjustments need to be applied for all dam age groups. Adjustment for birth-rearing type (single-single, multiple-single, multiple-multiple) is appropriate for wool, growth, and reproduction traits. The implications of adjustment for non-genetic effects are discussed.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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