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Academic Articles & Research Papers
Showing 20 of 85 academic articles
Heming Jia, Marjan Kordani, Iman Ahmadianfar, Arvin Samadi Koucheksaraee et al. • Engineering Applications of Computational Fluid Mechanics
(2026)
Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices usin...
DOAJ
Open Access (DOAJ)
DOI: 10.1080/19942060.2025.2602582
Sarah M. Alghamdi, Paul N. Schofield, Robert Hoehndorf • Disease Models & Mechanisms
(2022)
Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past d...
DOAJ
Open Access (DOAJ)
DOI: 10.1242/dmm.049441
Ari Mohammed ali Ahmed, Aree Ali Mohammed, Professor • UHD Journal of Science and Technology
(2021)
Prostate cancer can be viewed as the second most dangerous and diagnosed cancer of men all over the world. In the past decade, machine and deep learning methods play a significant role in improving the accuracy of classification for both bi...
DOAJ
Open Access (DOAJ)
DOI: https://doi.org/10.21928/uhdjst.v5n2y2021.pp41-47
Zhiqiang Gong, Ping Zhong, Weidong Hu • IEEE Access
(2019)
Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/access.2019.2917620
Jörn Lötsch, Alfred Ultsch • Pain
(2017)
1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...
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cc-by-nc-nd
DOI: https://doi.org/10.1097/j.pain.0000000000001118
PMC: 5895117
Ildar M. Ishkulov, Irik G. Fattakhov • Записки Горного института
(2025)
The problem of timely and accurate evaluation of well integrity is becoming increasingly relevant in the context of mature field development, high wellstream water cut, and a growing number of old wells. For production casing diagnostics, g...
DOAJ
Open Access (DOAJ)
Sergios Theodoridis (2015)
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabil...
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K. W. DeGregory, Patrick K. Kuiper, Thomas DeSilvio, James D. Pleuss et al. • Obesity Reviews
(2018)
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine lea...
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Open Access
DOI: https://doi.org/10.1111/obr.12667
PMC: 8176949
Dominic Dwyer, Peter Falkai, Nikolaos Koutsouleris • Annual Review of Clinical Psychology
(2018)
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide...
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DOI: https://doi.org/10.1146/annurev-clinpsy-032816-045037
Daisuke Komura, Shumpei Ishikawa • Computational and Structural Biotechnology Journal
(2018)
Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have so...
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DOI: https://doi.org/10.1016/j.csbj.2018.01.001
PMC: 6158771
Charles Nickmilder, Anthony Tedde, Isabelle Dufrasne, Françoise Lessire et al. • Remote Sensing
(2021)
Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs13030408
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong • Cambridge University Press eBooks
(2020)
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in d...
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DOI: https://doi.org/10.1017/9781108679930
Stephen Marsland • Chapman & Hall/CRC eBooks
(2011)
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is re...
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DOI: https://doi.org/10.1201/9781420067194
Theodore Warsavage, Fuyong Xing, Anna E Barón, William J Feser et al. • PLoS ONE
(2020)
We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some...
DOAJ
Open Access (DOAJ)
DOI: 10.1371/journal.pone.0231468
Shihao Gu, Bryan Kelly, Dacheng Xiu • Review of Financial Studies
(2020)
Abstract We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, i...
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cc-by-nc-nd
DOI: https://doi.org/10.1093/rfs/hhaa009
S. Mostafa Mousavi, Gregory C. Beroza • Annual Review of Earth and Planetary Sciences
(2022)
Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...
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DOI: https://doi.org/10.1146/annurev-earth-071822-100323
Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis
(2019)
Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...
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other-oa
DOI: https://doi.org/10.1021/acscatal.9b04321
Rene Y Choi, Aaron S. Coyner, Jayashree Kalpathy–Cramer, Michael F Chiang et al. • PubMed Central
(2020)
Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods: A systematic literature search in PubMed was ...
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cc-by-nc-nd
DOI: https://doi.org/10.1167/tvst.9.2.14
PMC: 7347027
Joseph A. Cruz, David Scott Wishart • Cancer Informatics
(2006)
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, ...
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cc-by-nc
DOI: https://doi.org/10.1177/117693510600200030
PMC: 2675494
Tim Mueller, Aaron Gilad Kusne, Rampi Ramprasad • Reviews in computational chemistry
(2016)
This chapter addresses the role that data-driven approaches, especially machine learning methods, are expected to play in materials research in the immediate future. Machine learning, an important part of artificial intelligence, has made m...
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DOI: https://doi.org/10.1002/9781119148739.ch4