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The Effects of Gender Signals and Performance in Online Product Reviews

Sandipan Sikdar, Rachneet Sachdeva, Johannes Wachs, Johannes Wachs et al. • Frontiers in Big Data (2022)

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are...

DOAJ Open Access (DOAJ) DOI: 10.3389/fdata.2021.771404

Deep Learning

John D. Kelleher • The MIT Press eBooks (2019)

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vi...

OPENALEX DOI: https://doi.org/10.7551/mitpress/11171.001.0001

Deep learning in bioinformatics

Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics (2016)

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...

OPENALEX Open Access DOI: https://doi.org/10.1093/bib/bbw068

Machine learning insight in quality control and maintenance management of drilling mud pumps

Xiaoming Wu, Jingxuan Chang, Chengcheng Long, Hamzeh Ghorbani • Journal of Big Data (2026)

Abstract This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs....

DOAJ Open Access (DOAJ) DOI: 10.1186/s40537-026-01475-9

MARKETING ACTIVITIES OF IT COMPANIES: INFORMATION AND ORGANISATIONAL CAPABILITIES FOR DIGITAL PRODUCT DEVELOPMENT

Kostiantyn Fuks • Green, Blue and Digital Economy Journal (2024)

The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...

DOAJ Open Access (DOAJ) DOI: 10.30525/2661-5169/2024-1-4

Machine learning for ecosystem services

Simon Willcock, Javier Martínez‐López, Danny A. P. Hooftman, Kenneth J. Bagstad et al. • Ecosystem Services (2018)

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within i...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.ecoser.2018.04.004

Machine learning and its applications: A review

Sheena Angra, Sachin Ahuja • International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017)

Nowadays, large amount of data is available everywhere. Therefore, it is very important to analyze this data in order to extract some useful information and to develop an algorithm based on this analysis. This can be achieved through data m...

OPENALEX DOI: https://doi.org/10.1109/icbdaci.2017.8070809

Machine Learning for Computational Heterogeneous Catalysis

Philomena Schlexer, Kirsten T. Winther, José Antonio Garrido Torres, Verena Streibel et al. • ChemCatChem (2019)

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the developme...

OPENALEX DOI: https://doi.org/10.1002/cctc.201900595

Machine learning in cardiovascular medicine: are we there yet?

Khader Shameer, Kipp W. Johnson, Benjamin Scott Glicksberg, Joel T. Dudley et al. • Heart (2018)

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

OPENALEX DOI: https://doi.org/10.1136/heartjnl-2017-311198

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Mikhail Ivanenko, Damian Wanta, Waldemar T. Smolik, Przemysław Wróblewski et al. • Life (2024)

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was e...

DOAJ Open Access (DOAJ) DOI: 10.3390/life14030419

Machine learning in pain research

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...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

A Sensor-to-API Data Infrastructure for Building Digital Twins: Harmonizing Multi-Rate IoT Energy and Indoor Data Streams for Proactive Analytics

J. W. Njoroge, M. Koeva, M. Farnaghi, D. Petrova-Antonova et al. • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2026)

Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...

DOAJ Open Access (DOAJ) DOI: 10.5194/isprs-annals-XII-4-W1-2026-251-2026

Benchmarking Supervised Classifiers for Concurrent Multidomain Dropout-Intention Attributions in Higher Education: Evidence from a Colombian Public University

Marieth Agnes Guillen-García, Osnamir Elias Bru-Cordero, Cristian David Correa-Álvarez • Big Data and Cognitive Computing (2026)

Student retention analytics often treats withdrawal as a single outcome, although students may attribute dropout intention to personal, socioeconomic, and academic pressures simultaneously. We benchmarked nine supervised classifiers for ide...

DOAJ Open Access (DOAJ) DOI: 10.3390/bdcc10080274

Machine Learning in Agriculture: A Review

Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson et al. • Sensors (2018)

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive revi...

OPENALEX cc-by DOI: https://doi.org/10.3390/s18082674 PMC: 6111295

Machine Learning from Theory to Algorithms: An Overview

Jafar Ahmad Abed Alzubi, Anand Nayyar, Akshi Kumar • Journal of Physics Conference Series (2018)

The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which i...

OPENALEX cc-by DOI: https://doi.org/10.1088/1742-6596/1142/1/012012

Synergistic multi-altimeter for estimating water level in the coastal zone of Beibu Gulf using SEL, ALES + and BFAST algorithms

Jiaoling Qin, Jiaoling Qin, Sunzhe Li, Sunzhe Li et al. • Frontiers in Marine Science (2023)

Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal usin...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmars.2022.1113387

MATLAB Machine Learning

Michael Paluszek, Stephanie J. Thomas • Apress eBooks (2016)

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It prov...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-2250-8