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Machine learning in geosciences and remote sensing

David John Lary, Amir H. Alavi, Amir H. Gandomi, Annette L. Walker • Geoscience Frontiers (2015)

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.gsf.2015.07.003

A Review on Fairness in Machine Learning

Dana Pessach, Erez Shmueli • ACM Computing Surveys (2022)

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college ad...

OPENALEX DOI: https://doi.org/10.1145/3494672

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar • The MIT Press eBooks (2018)

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference fo...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/10290.001.0001

What is Machine Learning? A Primer for the Epidemiologist

Qifang Bi, Katherine E Goodman, Joshua Kaminsky, Justin Lessler • American Journal of Epidemiology (2019)

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well...

OPENALEX DOI: https://doi.org/10.1093/aje/kwz189

Machine Learning in Materials Science

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...

OPENALEX DOI: https://doi.org/10.1002/9781119148739.ch4

Evaluation of PCA with variable selection for cluster typological domains

Silvânia Alves Braga de Castro, André Carlos Silva • REM: International Engineering Journal (2024)

Abstract The modeling of mineral deposits has been improved over the years with the incorporation of mineralogical and metallurgical information obtained from drilling samples that make up the pillars for the construction of resource models...

DOAJ Open Access (DOAJ) DOI: 10.1590/0370-44672023770071

Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

Hilal M. El Misilmani, Tarek Naous • International Conference on High Performance Computing & Simulation (HPCS) (2019)

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently...

OPENALEX DOI: https://doi.org/10.1109/hpcs48598.2019.9188224

Machine Learning into Metaheuristics

El‐Ghazali Talbi • ACM Computing Surveys (2021)

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...

OPENALEX Open Access DOI: https://doi.org/10.1145/3459664

Machine Learning in Medicine

Rahul C. Deo • Circulation (2015)

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular va...

OPENALEX Open Access DOI: https://doi.org/10.1161/circulationaha.115.001593 PMC: 5831252

Adversarial machine learning

Ling Huang, Anthony D. Joseph, Blaine A. Nelson, Benjamin I. P. Rubinstein et al. • ACM Workshop on Artificial Intelligence and Security (AISec) (2011)

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a...

OPENALEX DOI: https://doi.org/10.1145/2046684.2046692

Machine Learning and Its Applications to Biology

Adi Laurentiu Tarca, Vincent J. Carey, Xuewen Chen, Roberto J. Romero et al. • PLoS Computational Biology (2007)

The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechan...

OPENALEX public-domain DOI: https://doi.org/10.1371/journal.pcbi.0030116 PMC: 1904382

Machine Learning for Neural Decoding

Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich et al. • eNeuro (2020)

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve deco...

OPENALEX cc-by DOI: https://doi.org/10.1523/eneuro.0506-19.2020 PMC: 7470933

Introduction to machine learning

Rafael A. Irizarry • Chapman & Hall/CRC eBooks (2019)

The machine learning approach is to train an algorithm using a dataset for which the people do know the outcome, and then apply this algorithm in the future to make a prediction when they don't know the outcome. In this chapter, the authors...

OPENALEX DOI: https://doi.org/10.1201/9780429341830-27

A machine learning based scheme for enhancing the detection of position falsification attacks in vehicular ad hoc networks

Eslam Abdelkreem, Sherif Hussein, Ashraf Tammam • Scientific Reports (2026)

Abstract Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers’ safety a...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-39867-9

Data on Vulnerability Detection in Android

Shivi Garg, Niyati Baliyan • Data in Brief (2019)

The data in this article have been collaborated from mainly four sources- Google Playstore,11 Google Playstore https://play.google.com/store/apps. Wandoujia22 Wandoujia apps http://www.wandoujia.com/apps. (third party app store market), AMD...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2018.12.038

Classification of bioactive peptides: A systematic benchmark of models and encodings

Edoardo Bizzotto, Guido Zampieri, Laura Treu, Pasquale Filannino et al. • Computational and Structural Biotechnology Journal (2024)

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.csbj.2024.05.040

Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants

Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu et al. • Journal of Hydrology: Regional Studies (2025)

Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° t...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ejrh.2025.102311

Advancing Biosensors with Machine Learning

Feiyun Cui, Yun Yue, Yi Zhang, Ziming Zhang et al. • ACS Sensors (2020)

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, es...

OPENALEX DOI: https://doi.org/10.1021/acssensors.0c01424