Machine Learning Methods for Sweet Spot Detection: A Case Study

Geostatistics Valencia 2016 - Quantitative Geology and Geostatistics ◽

10.1007/978-3-319-46819-8_38 ◽

2017 ◽

pp. 573-588 ◽

Author(s):

Vera Louise Hauge ◽

Gudmund Horn Hermansen

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

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Container Demand Forecasting Using Machine Learning Methods: A Real Case Study from Turkey

Lecture Notes in Mechanical Engineering - Digital Conversion on the Way to Industry 4.0 ◽

10.1007/978-3-030-62784-3_70 ◽

2020 ◽

pp. 842-852

Author(s):

Ayhan Darendeli ◽

Aylin Alparslan ◽

Mehmet Serdar Erdoğan ◽

Özgür Kabadurmuş

Keyword(s):

Machine Learning ◽

Demand Forecasting ◽

Real Case ◽

Learning Methods ◽

Machine Learning Methods

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Comparing calibrated statistical and machine learning methods for wildland fire occurrence prediction: a case study of human-caused fires in Lac La Biche, Alberta, Canada

International Journal of Wildland Fire ◽

10.1071/wf20139 ◽

2021 ◽

Author(s):

Nathan Phelps ◽

Douglas G. Woolford

Keyword(s):

Machine Learning ◽

Wildland Fire ◽

Fire Occurrence ◽

Learning Methods ◽

Machine Learning Methods

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Predicting First-Year Computer Science Students Drop-Out with Machine Learning Methods: A Case Study

Educating Engineers for Future Industrial Revolutions - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-68201-9_70 ◽

2021 ◽

pp. 719-726

Author(s):

Natalja Maksimova ◽

Avar Pentel ◽

Olga Dunajeva

Keyword(s):

Machine Learning ◽

Computer Science ◽

Drop Out ◽

Science Students ◽

Learning Methods ◽

Machine Learning Methods

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Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

Ain Shams Engineering Journal ◽

10.1016/j.asej.2020.09.011 ◽

2020 ◽

Author(s):

Wanie M. Ridwan ◽

Michelle Sapitang ◽

Awatif Aziz ◽

Khairul Faizal Kushiar ◽

Ali Najah Ahmed ◽

...

Keyword(s):

Machine Learning ◽

Forecasting Model ◽

Rainfall Forecasting ◽

Learning Methods ◽

Machine Learning Methods

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Precipitation Interception Modelling Using Machine Learning Methods – The Dragonja River Basin Case Study

Practical Hydroinformatics - Water Science and Technology Library ◽

10.1007/978-3-540-79881-1_25 ◽

2008 ◽

pp. 347-358 ◽

Author(s):

L. Stravs ◽

M. Brilly ◽

M. Sraj

Keyword(s):

Machine Learning ◽

River Basin ◽

Learning Methods ◽

Machine Learning Methods

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Protein spot detection and quantification in 2-DE gel images using machine-learning methods

10.1002/pmic.201000601 ◽

2011 ◽

Vol 11 (10) ◽

pp. 2038-2050 ◽

Author(s):

Panagiotis Tsakanikas ◽

Elias S. Manolakos

Keyword(s):

Machine Learning ◽

Protein Spot ◽

Learning Methods ◽

Machine Learning Methods ◽

Spot Detection ◽

Detection And Quantification

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A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

Environmental Modelling & Software ◽

10.1016/j.envsoft.2016.07.005 ◽

2016 ◽

Vol 84 ◽

pp. 240-250 ◽

Author(s):

Binh Thai Pham ◽

Biswajeet Pradhan ◽

Dieu Tien Bui ◽

Indra Prakash ◽

M.B. Dholakia

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Landslide Susceptibility ◽

Susceptibility Assessment ◽

Learning Methods ◽

Landslide Susceptibility Assessment ◽

Machine Learning Methods

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Predictive lithological mapping through machine learning methods: a case study in the Cinzento Lineament, Carajás Province, Brazil

Journal of the Geological Survey of Brazil ◽

10.29396/jgsb.2019.v2.n1.3 ◽

2019 ◽

Vol 2 (1) ◽

pp. 26-36 ◽

Author(s):

Iago Costa ◽

◽

Felipe Tavares ◽

Junny Oliveira ◽

◽

...

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Lithological Mapping ◽

Machine Learning Methods ◽

Carajás Province

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Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion

Proceedings of the 9th Unconventional Resources Technology Conference ◽

10.15530/urtec-2021-5303 ◽

2021 ◽

Author(s):

B.L. Myers ◽

B. Davis ◽

R. Duman ◽

T. Cross

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods

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An improvement on the prediction power of the 3D-QSAR CoMFA models using a hybrid of statistical and machine learning methods: a case study on γ‑secretase modulators of Alzheimer’s disease

Medicinal Chemistry Research ◽

10.1007/s00044-017-1828-7 ◽

2017 ◽

Vol 26 (6) ◽

pp. 1184-1200 ◽

Author(s):

Hadi Khani ◽

Mohammad B. Sepehrifar ◽

Shantia Yarahmadian

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

3D Qsar ◽

Learning Methods ◽

Machine Learning Methods

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