PSX-A-29 Late-Breaking: Use of machine learning algorithms to predict residual feed intake value and classification groups in commercial beef cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 377-378
Author(s):  
Ghader Manafiazar ◽  
Mohammad Riazi ◽  
John A Basarab ◽  
Changxi Li ◽  
Paul Stothard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Beef Cattle ◽  
Feed Intake ◽  
Phenotypic Variation ◽  
Genomic Analysis ◽  
Residual Feed Intake ◽  
Machine Learning Algorithms ◽  
Genomic Information ◽  
Data Set ◽  
Better Than

Abstract The objective of this study was to explore the potential of Machine Learning (ML) algorithms to predict residual feed intake (RFI) classification group (high or low RFI) and individual RFI using performance records and genomic information. A total of 4145 animals from research and commercial herds with RFI performance records were included in the study from which 3899 cattle had genomic information (genotyped using Illumina Bovine 50k SNP BeadChip). Different libraries based on R and Python including Lazy Predict, Scikit-learn, PyCaret, and H2O Flow were used to test various ML models. Genomic information was subjected to quality control by removing SNPs with an allele frequency less than 0.05 or with a call rate lower than 0.95. A total of 42,689 SNPs remained for further analysis and accounted for 34% of phenotypic variation (heritability of 0.34±0.07) in RFI. Different numbers of SNPs were selected based on their contribution to phenotypic variation (500 SNPs, 1K, 5K, 10K, and 15K) then were included in the ML models. The GLM Stacked Ensemble model with 15k SNPs performed better than the other models to predict RFI classification group (R2 = 0.54). Regardless of the number of SNPs included in the model, GLM Stacked Ensemble performed better than other models to predict individual RFI. This model’s performance improved with increasing SNPs (MAE=0.39 for 500 SNPs; 0.31 for 15k SNPs). In the test data set, an increasing number of SNPs did not change the performance of the model and had a MAE of 0.39). The results demonstrate the potential for ML to improve predictions for feed efficiency compare to genomic analysis in beef cattle without measuring feed intake.

PSXV-29 Late-Breaking: Investigating use of machine learning algorithms to predict days in herd for commercial beef cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 381-381
Author(s):  
Ghader Manafiazar ◽  
Mohammad Riazi ◽  
John A Basarab ◽  
Changxi Li ◽  
Paul Stothard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Phenotypic Variation ◽  
Model Performance ◽  
Genomic Analysis ◽  
Machine Learning Algorithms ◽  
Phenotypic Variance ◽  
Mean Square ◽  
Simple Linear Regression ◽  
Genomic Information

Abstract The objective of this study was to explore the potential of Machine Learning (ML) algorithms to increase the accuracy of predicting individual days in the herd (an indicator of stayability) using reproductive records and genomic information. A total of 6943 cows from 3 herds with reproductive performance were included in the study from which 696 cows had genomic information (genotyped using Illumina Bovine 50k SNP BeadChip). Different libraries based on R and Python were used to test various ML models including Lazy Predict, Scikit-learn, PyCaret, and H2O Flow. Genomic information was subjected to quality control by removing SNPs with an allele frequency less than 0.05 or with a call rate lower than 0.95. A total of 42,689 SNP remained for further analysis and accounted for 11% of phenotypic variation (heritability of 0.11±0.02) in DIH. Different numbers of SNPs (500 SNPs, 1K, 5K, 10K, and 15K) were selected based on their contribution to phenotypic variation from GWAS and were included in the models. Model performance measures, such as mean absolute error (MAE) and mean square of error (MSE), worsened with increased SNPs in the model. Bayesian Ridge algorithm using 500 top SNPs contributed to the phenotypic variance, had the best performance to predict DIH with MAE of 612.6 and R2 of 0.52 in the training population using PyCaret program. When BWT and WWT were added to the model, in addition to SNPs, little change was observed in the model’s performance. Overall, we concluded that ML models had better performance compared to the conventional modeling approach and genomic analysis; CatBoost model had 55% lower mean square of error compared to the simple linear regression (734650 vs 1637410). The results suggest that ML tools have the potential to improve the accuracy of predicting DIH compared to simple linear regression and conventional genomic analysis.

Machine-Learning Models for Combinatorial Catalyst Discovery

2003 ◽  
Vol 804 ◽  
Author(s):  
Gregory A. Landrum ◽  
Julie Penzotti ◽  
Santosh Putta
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Homogeneous Catalyst ◽  
Validation Data ◽  
Data Set ◽  
Molecular Weights ◽  
Equivalent Models ◽  
Average Accuracy ◽  
Better Than

ABSTRACTStandard machine-learning algorithms were used to build models capable of predicting the molecular weights of polymers generated by a homogeneous catalyst. Using descriptors calculated from only the two-dimensional structures of the ligands, the average accuracy of the models on an external validation data set was approximately 70%. Because the models show no bias and perform significantly better than equivalent models built using randomized data, we conclude that they learned useful rules and did not overfit the data.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

scholarly journals RAPID COMMUNICATION: Residual feed intake in beef cattle is associated with differences in protein turnover and nutrient transporters in ruminal epithelium

2019 ◽  
Vol 97 (5) ◽  
pp. 2181-2187
Author(s):  
Ahmed A Elolimy ◽  
Emad Abdel-Hamied ◽  
Liangyu Hu ◽  
Joshua C McCann ◽  
Daniel W Shike ◽  
...  
Keyword(s):  
Amino Acid ◽  
Beef Cattle ◽  
Protein Degradation ◽  
Insulin Signaling ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Protein Turnover ◽  
Residual Feed Intake ◽  
Ubiquitin Proteasome ◽  
Ruminal Epithelium

Abstract Residual feed intake (RFI) is a widely used measure of feed efficiency in cattle. Although the precise biologic mechanisms associated with improved feed efficiency are not well-known, most-efficient steers (i.e., with low RFI coefficient) downregulate abundance of proteins controlling protein degradation in skeletal muscle. Whether cellular mechanisms controlling protein turnover in ruminal tissue differ by RFI classification is unknown. The aim was to investigate associations between RFI and signaling through the mechanistic target of rapamycin (MTOR) and ubiquitin-proteasome pathways in ruminal epithelium. One hundred and forty-nine Red Angus cattle were allocated to 3 contemporary groups according to sex and herd origin. Animals were offered a finishing diet for 70 d to calculate the RFI coefficient for each. Within each group, the 2 most-efficient (n = 6) and least-efficient animals (n = 6) were selected. Compared with least-efficient animals, the most-efficient animals consumed less feed (P < 0.05; 18.36 vs. 23.39 kg/d DMI). At day 70, plasma samples were collected for insulin concentration analysis. Ruminal epithelium was collected immediately after slaughter to determine abundance and phosphorylation status of 29 proteins associated with MTOR, ubiquitin-proteasome, insulin signaling, and glucose and amino acid transport. Among the proteins involved in cellular protein synthesis, most-efficient animals had lower (P ≤ 0.05) abundance of MTOR, p-MTOR, RPS6KB1, EIF2A, EEF2K, AKT1, and RPS6KB1, whereas MAPK3 tended (P = 0.07) to be lower. In contrast, abundance of p-EEF2K, p-EEF2K:EEF2K, and p-EIF2A:EIF2A in most-efficient animals was greater (P ≤ 0.05). Among proteins catalyzing steps required for protein degradation, the abundance of UBA1, NEDD4, and STUB1 was lower (P ≤ 0.05) and MDM2 tended (P = 0.06) to be lower in most-efficient cattle. Plasma insulin and ruminal epithelium insulin signaling proteins did not differ (P > 0.05) between RFI groups. However, abundance of the insulin-responsive glucose transporter SLC2A4 and the amino acid transporters SLC1A3 and SLC1A5 also was lower (P ≤ 0.05) in most-efficient cattle. Overall, the data indicate that differences in signaling mechanisms controlling protein turnover and nutrient transport in ruminal epithelium are components of feed efficiency in beef cattle.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

scholarly journals 127 Predicting Dry Matter Intake of Gestating and Lactating Beef Cows

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 58-58
Author(s):  
Megan A Gross ◽  
Claire Andresen ◽  
Amanda Holder ◽  
Alexi Moehlenpah ◽  
Carla Goad ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Milk Yield ◽  
Feed Intake ◽  
Beef Cows ◽  
Multiple Regression Equation ◽  
Validation Data ◽  
Data Set ◽  
Lactating Cows ◽  
Downward Bias ◽  
Protein Supply

Abstract In 1996, the NASEM beef cattle committee developed and published an equation to estimate cow feed intake using results from studies conducted or published between 1979 and 1993 (Nutrient Requirements of Beef Cattle). The same equation was recommended for use in the most recent version of this publication (2016). The equation is sensitive to cow weight, diet digestibility and milk yield. Our objective was to validate the accuracy of this equation using more recent published and unpublished data. Criteria for inclusion in the validation data set included projects conducted or published within the last ten years, direct measurement of forage intake, adequate protein supply, and pen feeding (no tie stall or metabolism crate data). The validation data set included 29 treatment means for gestating cows and 26 treatment means for lactating cows. Means for the gestating cow data set was 11.4 ± 1.9 kg DMI, 599 ± 77 kg BW, 1.24 ± 0.14 Mcal/kg NEm per kg of feed and lactating cow data set was 14.5 ± 2.0 kg DMI, 532 ± 116.3 kg BW, and 1.26 ± 0.24 Mcal NEm per kg feed, respectively. Non intercept models were used to determine equation accuracy in predicting validation data set DMI. The slope for linear bias in the NASEM gestation equation did not differ from 1 (P = 0.07) with a 3.5% positive bias. However, when the NASEM equation was used to predict DMI in lactating cows, the slope for linear bias significantly differed from 1 (P < 0.001) with a downward bias of 13.7%. Therefore, a new multiple regression equation was developed from the validation data set: DMI= (-4.336 + (0.086427 (BW^.75) + 0.3 (Milk yield)+6.005785(NEm)), (R-squared=0.84). The NASEM equation for gestating beef cows was reasonably accurate while the lactation equation underestimated feed intake.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

Sign in / Sign up

Export Citation Format

Share Document