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Design, framework and benchmark of safety monitors for black-box classifiers

Fahad Ahmed Khokhar, Tommaso Zoppi, Luigi Cennini, Andrea Ceccarelli et al. • Scientific Reports (2026)

Abstract The last decade has seen a surging opportunity for developing autonomous systems (e.g., fully autonomous driving, computer-guided robotic surgery, mobile robots for inspections and surveillance, and manufacturing robots) to relieve...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-45091-2

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

AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS

Dhurgham Ali Mohammed, Kalyani A. Patel • ICTACT Journal on Soft Computing (2025)

In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks su...

DOAJ Open Access (DOAJ) DOI: 10.21917/ijsc.2025.0518

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

The security of machine learning

Marco Barreno, Blaine A. Nelson, Anthony D. Joseph, J. D. Tygar • Machine Learning (2010)

Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We pre...

OPENALEX cc-by-nc DOI: https://doi.org/10.1007/s10994-010-5188-5

Information disclosure and funding success of green crowdfunding campaigns: a study on GoFundMe

Ziyi Yin, Guowei Huang, Rui Zhao, Sen Wang et al. • Financial Innovation (2024)

Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project f...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40854-024-00666-8

Using generalized random forests to characterize vulnerability to adverse health outcomes following wildfire smoke exposure in California

Noémie Letellier, Caitlin G. Jones-Ngo, Michael W. Cheung, Rosana Aguilera et al. • Environment International (2025)

Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience dispropor...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.envint.2025.109955

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

Machine learning in chemoinformatics and drug discovery

Yu‐Chen Lo, Stefano Rensi, Wen Torng, Russ Biagio Altman • Drug Discovery Today (2018)

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learn...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.drudis.2018.05.010 PMC: 6078794

Machine Learning Mastery

Naga Ramesh Palakurti, Nageswararao Kanchepu • Advances in systems analysis, software engineering, and high performance computing book series (2024)

This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing th...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-2909-2.ch002

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale et al. • Journal of Natural Fibers (2025)

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested und...

DOAJ Open Access (DOAJ) DOI: 10.1080/15440478.2025.2502662

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

Anzar Abbas, Vijay Yadav, Emma Smith, Elizabeth Ramjas et al. • Digital Biomarkers (2021)

Introduction: Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine ...

DOAJ Open Access (DOAJ) DOI: 10.1159/000512383

Hands-On Machine Learning with R

Brad Boehmke, Brandon Greenwell • Chapman & Hall/CRC eBooks (2019)

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...

OPENALEX DOI: https://doi.org/10.1201/9780367816377

Python Machine Learning

Sebastian Raschka (2015)

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effect...

OPENALEX

Global patterns of gully occurrence and their sensitivity to environmental changes

Yixian Chen, Sofie De Geeter, Jean Poesen, Francis Matthews et al. • International Soil and Water Conservation Research (2026)

Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains l...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.iswcr.2025.09.004

LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles

Yong Wang, Hongwen He, Yuankai Wu, Pei Wang et al. • Engineering (2025)

An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasin...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.eng.2024.10.021