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Big data and machine learning for materials science
Discover Materials, 2021 Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence.
J. F. Rodrigues +4 more
semanticscholar +1 more source
A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
Applied Sciences, 2023 In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science ...
Shahneela Pitafi +2 more
semanticscholar +1 more source
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case [PDF]
, 2018 Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new ...
A. J. Connolly +33 more
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Advancing Computational Toxicology by Interpretable Machine Learning
Environmental Science and Technology, 2023 Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in ...
Xuelian Jia, Tong Wang, Hao Zhu
semanticscholar +1 more source
Discriminative Cooperative Networks for Detecting Phase Transitions [PDF]
, 2018 The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science ...
Liu, Ye-Hua +1 more
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Machine Learning in Cybersecurity: Techniques and Challenges
European journal of technology, 2023 In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security ...
J. Bharadiya
semanticscholar +1 more source
Kinases and Phosphatases, 2023 The goal of drug discovery is to uncover new molecules with specific chemical properties that can be used to cure diseases. With the accessibility of machine learning techniques, the approach used in this search has become a significant component in ...
Soham Choudhuri +6 more
semanticscholar +1 more source
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy [PDF]
, 2017 Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky.
Gieseke, Fabian +4 more
core +2 more sources
Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification [PDF]
IEEE Transactions on Biomedical Engineering, 2020 Objective: Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the ...
Morteza Heidari +13 more
semanticscholar +1 more source
Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces [PDF]
Frontiers in Chemistry, 2020 The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach.
Li, Xiaoke +2 more
openaire +4 more sources