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Assessing climate and land use impacts on surface water yield using remote sensing and machine learning

Flipboard

An ensemble of machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), were used to evaluate the effects of climate variability and land use on annual water yield. This study also showed a reduction in lateral flow and higher evapotranspiration levels in 2000 and 2017.

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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

That’s great news for researchers who often work on SLRs because the traditional process is mind-numbingly slow: An analysis from 2017 found that SLRs take, on average, 67 weeks to produce. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. dollars apiece.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Support vector machine (SVM) based models. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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Calibration Techniques in Deep Neural Networks

Heartbeat

International conference on machine learning. PMLR, 2017. [2] Support vector machine classifiers as applied to AVIRIS data.” arXiv preprint arXiv:1710.09412 (2017). [7] References [1] Guo, Chuan, et al. “ On calibration of modern neural networks. 2] Lin, Zhen, Shubhendu Trivedi, and Jimeng Sun.

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Computer Vision and Deep Learning for Healthcare

PyImageSearch

In the future, using large datasets and machine learning may predict optimal locations to edit DNA to alleviate suboptimal gene editing outcomes, enabling researchers to focus efforts on genes that are less likely to be at risk to patients. AI may also improve gene editing accuracy (a method of altering DNA at the cellular or organism level).