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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
John E. Sawyer
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …
Automated Bioacoustics: Methods In Ecology And Conservation And Their Potential For Animal Welfare Monitoring, Michael P. Mcloughlin, Rebecca Stewart, Alan G. Mcelligott
Automated Bioacoustics: Methods In Ecology And Conservation And Their Potential For Animal Welfare Monitoring, Michael P. Mcloughlin, Rebecca Stewart, Alan G. Mcelligott
Alan G. McElligott, PhD
Vocalizations carry emotional, physiological and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation and sound scene classification. These methods are already used to automatically classify animal vocalizations, for example, in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation and sound classification. Following this, …
A Comparison Of Machine Learning Techniques For Taxonomic Classification Of Teeth From The Family Bovidae, Gregory J. Matthews, Juliet K. Brophy, Maxwell Luetkemeier, Hongie Gu, George K. Thiruvathukal
A Comparison Of Machine Learning Techniques For Taxonomic Classification Of Teeth From The Family Bovidae, Gregory J. Matthews, Juliet K. Brophy, Maxwell Luetkemeier, Hongie Gu, George K. Thiruvathukal
George K. Thiruvathukal
This study explores the performance of machine learning algorithms on the classification of fossil teeth in the Family Bovidae. Isolated bovid teeth are typically the most common fossils found in southern Africa and they often constitute the basis for paleoenvironmental reconstructions. Taxonomic identification of fossil bovid teeth, however, is often imprecise and subjective. Using modern teeth with known taxons, machine learning algorithms can be trained to classify fossils. Previous work by Brophy et al. [Quantitative morphological analysis of bovid teeth and implications for paleoenvironmental reconstruction of plovers lake, Gauteng Province, South Africa, J. Archaeol. Sci. 41 (2014), pp. …
An Incremental Reseeding Strategy For Clustering, Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James Von Brecht
An Incremental Reseeding Strategy For Clustering, Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James Von Brecht
Thomas Laurent
In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning. The algorithm alternates between three basic components: diffusing seed vertices over the graph, thresholding the diffused seeds, and then randomly reseeding the thresholded clusters. We demonstrate experimentally that the proper combination of these ingredients leads to an algorithm that achieves state-of-the-art performance in terms of cluster purity on standard benchmarks datasets. Moreover, the algorithm runs an order of magnitude faster than the other algorithms that achieve comparable results in terms of accuracy. We also describe a coarsen, cluster and refine approach similar to GRACLUS and …
Adam: Automated Detection And Attribution Of Malicious Webpages, Ahmed E. Kosba, Aziz Mohaisen, Andrew G. West, Trevor Tonn, Huy Kang Kim
Adam: Automated Detection And Attribution Of Malicious Webpages, Ahmed E. Kosba, Aziz Mohaisen, Andrew G. West, Trevor Tonn, Huy Kang Kim
Andrew G. West
Malicious webpages are a prevalent and severe threat in the Internet security landscape. This fact has motivated numerous static and dynamic techniques to alleviate such threats. Building on this existing literature, this work introduces the design and evaluation of ADAM, a system that uses machine-learning over network metadata derived from the sandboxed execution of webpage content. ADAM aims to detect malicious webpages and identify the nature of those vulnerabilities using a simple set of features. Machine-trained models are not novel in this problem space. Instead, it is the dynamic network artifacts (and their subsequent feature representations) collected during rendering that …
Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara
Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara
Ole J Mengshoel
Using Methods From The Data-Mining And Machine-Learning Literature For Disease Classification And Prediction: A Case Study Examining Classification Of Heart Failure Subtypes, Peter C. Austin
Peter Austin
OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines.
STUDY DESIGN AND SETTING: We compared the performance of these classification methods with that of conventional classification trees to classify patients with heart failure (HF) …
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Gavin Finnie
Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. We conclude with possible future research directions.
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Bjoern Krollner
Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to their research motivation, the machine learning technique used, the surveyed stock market, the forecasting time-frame, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting and that the results are promising. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. …
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Bruce Vanstone
Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. We conclude with possible future research directions.