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Showing 18 of 38 academic articles

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Rittik Chandra Das Turjy, Fahmid Al Farid, Fahmid Al Farid, Md. Hasan Imam Bijoy et al. • Frontiers in Artificial Intelligence (2026)

Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1884567

A comprehensive analytical assessment of dino-based architectures for sustainable automated potato inspection in the Al Kharj Region

Zahraa Tarek, Esraa Hasan • Journal of the Saudi Society of Agricultural Sciences (2026)

Abstract Self-supervised learning (SSL) has emerged as a pivotal paradigm in computer vision, particularly for applications where labeled data are limited, costly, or labor-intensive to obtain. This paradigm is especially relevant to sustai...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44447-026-00151-x

Hierarchical Multimodal Sleep Staging with Optimized EEG, EOG, and PPG Features for Wearable Applications

Roberto De Fazio, Matteo Paiano, Ramiro Velazquez, Carolina Del-Valle-Soto et al. • Applied Sciences (2026)

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with compu...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16168164

Greedy Broad Learning System

Weitong Ding, Yubo Tian, Shudan Han, Huining Yuan • IEEE Access (2021)

In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting and generalization ab...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2021.3084610

Rethinking Gated Recurrent Units for Rotating Machinery Prognostics: A Physics-Consistency Benchmark on the Mismatch Between Gating Mechanisms and Degradation Dynamics

Zhonghua Feng, Minglun Ren • Applied Sciences (2026)

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, wh...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16178379

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 review of machine learning in obesity

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...

OPENALEX Open Access DOI: https://doi.org/10.1111/obr.12667 PMC: 8176949

Finding Density Functionals with Machine Learning

John C. Snyder, Matthias Rupp, Katja Hansen, Klaus‐Robert Müller et al. • Physical Review Letters (2012)

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with ...

OPENALEX Open Access DOI: https://doi.org/10.1103/physrevlett.108.253002

Mathematics for Machine Learning

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...

OPENALEX DOI: https://doi.org/10.1017/9781108679930

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 Action

Peter de B. Harrington (2012)

SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to bui...

OPENALEX

Personalized ideological content recommendation via reinforcement learning as a digital therapeutic for anxiety reduction in youth

Yu Wang • Discover Artificial Intelligence (2026)

Abstract Adaptive digital therapies are needed with increasing rates of youth anxiety. However, existing systems lack personalization and dynamic learning. To solve this, the proposed research is to build a personalized ideological content ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01840-w

Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches

T. Anitha Kumari, Sanket Mishra • Egyptian Informatics Journal (2024)

The integration of sensors in the monitoring of essential bodily measurements, air quality, and energy consumption in buildings demonstrates the importance of the Internet of Things (IoT) in everyday life. These security breaches are caused...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.eij.2024.100520

A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems

Tala Talaei Khoei, Naima Kaabouch • Information (2023)

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and ...

DOAJ Open Access (DOAJ) DOI: 10.3390/info14020103