Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques on - SLAML '11

2011 ◽  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Large Scale Systems ◽  
Learning Techniques ◽  
System Logs

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

scholarly journals Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Giorgos Borboudakis ◽  
Taxiarchis Stergiannakos ◽  
Maria Frysali ◽  
Emmanuel Klontzas ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Large Scale Screening

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 13-35
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

scholarly journals Precipitation forecasting by large-scale climate indices and machine learning techniques

2020 ◽  
Vol 12 (5) ◽  
pp. 854-864
Author(s):  
Mehdi Gholami Rostam ◽  
Seyyed Javad Sadatinejad ◽  
Arash Malekian
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Climate Indices ◽  
Precipitation Forecasting ◽  
Learning Techniques

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2020 ◽  
pp. 38-66
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

scholarly journals Chemically intuited, large-scale screening of MOFs by machine learning techniques

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Giorgos Borboudakis ◽  
Taxiarchis Stergiannakos ◽  
Maria Frysali ◽  
Emmanuel Klontzas ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Large Scale Screening
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