3,327 results for machine learning
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A review of the application of machine learning in water quality evaluation
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve…
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Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance
…diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing…
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What Role Does Hydrological Science Play in the Age of Machine Learning?
Abstract This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than…
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Application of machine learning and artificial intelligence in oil and gas industry
…This paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry problems. It also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and…
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Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for…
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Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
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Ensemble machine learning approach for classification of IoT devices in smart home
…The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to…
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Time to reality check the promises of machine learning-powered precision medicine
Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice…
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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and…
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A benchmark study of machine learning models for online fake news detection
The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as…
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Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
…for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their…
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Machine learning based marine water quality prediction for coastal hydro-environment management
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Machine learning enables interpretable discovery of innovative polymers for gas separation membranes
…To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H 2…
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Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
…progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are…
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Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective
…of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML…
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DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels
…researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording…
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Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning
…time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and…
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Machine learning models to accelerate the design of polymeric long-acting injectables
…candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis…
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Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
…between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we…
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Machine learning classification can reduce false positives in structure-based virtual screening
…highly against a given target protein do not actually show activity. Machine learning approaches have not solved this problem, which we hypothesize is because models were not trained on sufficiently compelling “decoys.” By addressing this through a unique training strategy, we show that more…
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