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Species Distribution Modeling for Machine Learning Practitioners: A Review [article]

Sara Beery, Elijah Cole, Joseph Parker, Pietro Perona, Kevin Winner
2021 arXiv   pre-print
Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found.  ...  The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology.  ...  We hope that this document is useful for any computer scientist interested in bringing machine learning expertise to species distribution modeling.  ... 
arXiv:2107.10400v1 fatcat:mh7kkptpzvgpzfxxk2khpncnli

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Harish Bhaskar, David C. Hoyle, Sameer Singh
2006 Computers in Biology and Medicine  
Machine learning is used in a large number of bioinformatics applications and studies.  ...  We finally discuss various critical issues relating to bioinformatic data sets and make a number of recommendations on the proper use of machine learning techniques for bioinformatics research based upon  ...  In Table 1 , we present a qualitative comparison of the various tools based on the consensus in the opinion of four human experts in machine learning and our literature review.  ... 
doi:10.1016/j.compbiomed.2005.09.002 pmid:16226240 fatcat:yx7tvgzlvng3rfx5tbi6cj5loa

Machine learning for metabolic engineering: A review

Chris Lawson, Jose Manuel Martí, Tijana Radivojevic, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake A. Simmons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay (+3 others)
2020 Metabolic Engineering  
Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed.  ...  Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable.  ...  The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide  ... 
doi:10.1016/j.ymben.2020.10.005 pmid:33221420 fatcat:hac34yggd5hnrhkikd7elbzz3m

Weed detection using machine learning: A systematic literature review

Bashir Salisu Abubakar
2021 Systematic Literature Review and Meta-Analysis Journal  
Recently, many researchers and practitioners used Machine Learning (ML) algorithms in digital agriculture to help farmers in decision making.  ...  The most applied ML algorithm is Neural Networks in these models.  ...  Conclusion A systematic literature review on weed detection using machine learning algorithms was conducted.  ... 
doi:10.54480/slrm.v2i2.21 fatcat:oel766jjnzd7tk6jlymxwdkf2q

Boosting algorithms in energy research: A systematic review [article]

Hristos Tyralis, Georgia Papacharalampous
2020 arXiv   pre-print
Among the most prominent machine learning algorithms are the boosting ones, which are known to be "garnering wisdom from a council of fools", thereby transforming weak learners to strong learners.  ...  Machine learning algorithms have been extensively exploited in (renewable) energy research, due to their flexibility, automation and ability to handle big data.  ...  Renewable Energy 133:620-635. doi:10.1016/j.renene.2018.Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: A review.  ... 
arXiv:2004.07049v1 fatcat:i3omfuqf3rhutm5rsc5shofsoe

A review of multi-instance learning assumptions

James Foulds, Eibe Frank
2010 Knowledge engineering review (Print)  
Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector.  ...  This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed  ...  As MI learning continues to be applied to a wider selection of practical machine learning problems, the use of appropriate MI assumptions for the problems at hand becomes increasingly important.  ... 
doi:10.1017/s026988890999035x fatcat:d25inqjjzbggxjfunfpb7hxyfe

On the state of the art in machine learning: A personal review

Peter A. Flach
2001 Artificial Intelligence  
This paper reviews a number of recent books related to current developments in machine learning. Some (anticipated) trends will be sketched.  ...  a tighter integration of machine learning techniques with techniques from areas of application such as bioinformatics.  ...  Acknowledgements I am grateful to Nada Lavrač for encouragement and suggestions which helped improve an earlier draft, to Edward Ross for assisting in the internet search for machine learning and data  ... 
doi:10.1016/s0004-3702(01)00125-4 fatcat:uaetjtusandk3f4x4o5nkjad5a

A review of agile manufacturing systems

Luis M. Sanchez, Rakesh Nagi
2001 International Journal of Production Research  
Each contribution has tackled a di erent aspect of this large ®eld. In this paper, we review a wide range of recent literature on agile manufacturing.  ...  About a decade ago, the agile manufacturing paradigm was formulated in response to the constantly changing'new economy' and as a basis for returning to global competitiveness.  ...  Sanchez acknowledges the support of The National Council for Science and Technology (CONACYT) and The Tecnolo gico de Monterrey campus Leo n.  ... 
doi:10.1080/00207540110068790 fatcat:hfv7hwpnfvh5xjuma4l7vm6nsa

Deep learning for healthcare applications based on physiological signals: A review

Oliver Faust, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, U Rajendra Acharya
2018 Computer Methods and Programs in Biomedicine  
Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods.  ...  Deep learning algorithms try to develop the model by using all the available input.  ...  Acknowledgements Funding: No external funding sources supported this review.  ... 
doi:10.1016/j.cmpb.2018.04.005 pmid:29852952 fatcat:3tn4ookjyjgafgnbb2tyzznhz4

A review of machine learning applications in wildfire science and management [article]

Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, Mike D Flannigan
2020 arXiv   pre-print
Here, we present a scoping review of ML in wildfire science and management.  ...  However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity  ...  The authors would also like to thank Intact Insurance and the Western Partnership for Wildland Fire Science for their support.  ... 
arXiv:2003.00646v1 fatcat:5ufhtbwlsvd2rdk3ogbmqpnxuu

Computational bioacoustics with deep learning: a review and roadmap [article]

Dan Stowell
2021 arXiv   pre-print
In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps.  ...  Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments  ...  as machine learning.  ... 
arXiv:2112.06725v1 fatcat:vvoxl4rz7feb3hwqn4k6ytoicu

A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

Yi-Hui Zhou, Paul Gallins
2019 Frontiers in Genetics  
In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction.  ...  Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans.  ...  Chris Smith for the IT support in Bioinformatics Research Center.  ... 
doi:10.3389/fgene.2019.00579 pmid:31293616 pmcid:PMC6603228 fatcat:yjjvaz3dqngxlm7wq6zm2jwnv4

Intelligent management systems in operations: a review

N C Proudlove, S Vaderá, K A H Kobbacy
1998 Journal of the Operational Research Society  
hybrid fuzzy logic and neural network systems,’ '4 model-based approaches, ' r machine learning,''® abductive reasoning models''’ and CBR.  ...  On the application of machine learning techniques to fault diagnosis of power distribution lines. JEEE Trans on Power Delivery 10: 1927-1936. Kumar GP and Venkataram P (1995).  ... 
doi:10.1038/sj.jors.2600519 fatcat:bm6vzcb7yfahfjh6oaym47mieq

Intelligent management systems in operations: a review

N C Proudlove, S Vaderá, K A H Kobbacy
1998 Journal of the Operational Research Society  
hybrid fuzzy logic and neural network systems,’ '4 model-based approaches, ' r machine learning,''® abductive reasoning models''’ and CBR.  ...  On the application of machine learning techniques to fault diagnosis of power distribution lines. JEEE Trans on Power Delivery 10: 1927-1936. Kumar GP and Venkataram P (1995).  ... 
doi:10.1057/palgrave.jors.2600519 fatcat:5zc7dbzymffi5cqj2e2tgpekyi

Data clustering: a review

A. K. Jain, M. N. Murty, P. J. Flynn
1999 ACM Computing Surveys  
to the broad community of clustering practitioners.  ...  This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible  ...  The audience for this paper includes practitioners in the pattern recognition and image analysis communities (who should view it as a summarization of current practice), practitioners in the machine-learning  ... 
doi:10.1145/331499.331504 fatcat:sxbydoeuobabbgiz3gms6p3dve
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