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Showing 17 of 117 academic articles

Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets

Michał Wawer, Jarosław A. Chudziak, Ewa Niewiadomska-Szynkiewicz • Applied Sciences (2024)

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability...

DOAJ Open Access (DOAJ) DOI: 10.3390/app142411897

Artificial Intelligence in Routine IVF Practice

Grzegorz Mrugacz, Aleksandra Mospinek, Małgorzata Jagielska, Dariusz Miszczak et al. • Biology (2025)

Background: Artificial Intelligence (AI) has emerged as a transformative tool in in vitro fertilization (IVF) as it has done in other sectors. In IVF, AI offers advancements in embryo selection, treatment personalization, and outcome predic...

DOAJ Open Access (DOAJ) DOI: 10.3390/biology15010042

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Sharada Prasanna Mohanty, Jakub Czakon, Kamil A. Kaczmarek, Kamil A. Kaczmarek et al. • Frontiers in Artificial Intelligence (2020)

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in compu...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2020.534696

Non-Destructive Monitoring of External Quality of Date Palm Fruit (<i>Phoenix dactylifera</i> L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

Younes Noutfia, Ewa Ropelewska, Zbigniew Jóźwiak, Krzysztof Rutkowski • Sensors (2024)

The emergence of new technologies focusing on “computer vision” has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh...

DOAJ Open Access (DOAJ) DOI: 10.3390/s24237560

A Dual-Segmentation Framework for the Automatic Detection and Size Estimation of Shrimp

Malik Muhammad Waqar, Hassan Ali, Heng Zhou, Heba G. Mohamed et al. • Sensors (2025)

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. ...

DOAJ Open Access (DOAJ) DOI: 10.3390/s25185830

Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand

Ahmed E. Radwan, David A. Wood, Ahmed A. Radwan • Journal of Rock Mechanics and Geotechnical Engineering (2022)

Pore pressure is an essential parameter for establishing reservoir conditions, geological interpretation and drilling programs. Pore pressure prediction depends on information from various geophysical logs, seismic, and direct down-hole pre...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jrmge.2022.01.012

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

Evaluation of Predictive Maintenance by Marginal Utility: A Case Study on Offshore Wind

Rasmus Dovnborg Frederiksen, Grzegorz Radzki, Grzegorz Bocewicz, Peter Nielsen et al. • Applied Sciences (2026)

This paper investigates the relationship between the benefits achieved by predictive maintenance implementation and the related costs incurred intentionally or not. Specifically, we seek to establish an evaluation method for predictive main...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16178708

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Tomasz Rokicki, Piotr Bórawski, Aneta Bełdycka-Bórawska, Bogdan Klepacki • Applied Sciences (2026)

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16189017

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

PERMEPSY: a multicentre, randomized, double-blind proof-of-concept trial of personalized metacognitive training for adults with psychosis — a study protocol

Maria Lamarca, Maria Lamarca, Maria Lamarca, Maria Lamarca et al. • Frontiers in Psychiatry (2026)

BackgroundWhile psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1711659

Artificial Empathy and Imprecise Communication in a Multi-Agent System

Joanna Siwek, Konrad Pierzyński, Przemysław Siwek, Adrian Wójcik et al. • Applied Sciences (2024)

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applicatio...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15010008

SEMANTIC URBAN MESH SEGMENTATION BASED ON AERIAL OBLIQUE IMAGES AND POINT CLOUDS USING DEEP LEARNING

Ł. Wilk, Ł. Wilk, D. Mielczarek, W. Ostrowski et al. • The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2022)

The use of deep machine learning methods for semantic classification of city mesh models is one of the current trends in geoscience development. Thanks to the thriving development of Convolutional Neural Networks (CNNs) it is now achievable...

DOAJ Open Access (DOAJ) DOI: 10.5194/isprs-archives-XLIII-B2-2022-485-2022

Prediction of Seven Artificial Intelligence-Based Intraocular Lens Power Calculation Formulas in Medium-Long Caucasian Eyes

Wiktor Stopyra, Oleksiy Voytsekhivskyy, Andrzej Grzybowski • Life (2025)

Purpose: To compare the accuracy of seven artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in medium-long Caucasian eyes regarding the root-mean-square absolute error (RMSAE), the median absolute error (M...

DOAJ Open Access (DOAJ) DOI: 10.3390/life15010045

New Meanings of Computer-Based Entertainment and Communication among Students in Poland During the COVID-19 Pandemic

Mokras-Grabowska Justyna, Mroczek-Żulicka Aleksandra, Olasik Marta • Quaestiones Geographicae (2022)

This article is an attempt at investigating new meanings and significance of computer-based entertainment during the COVID-19 pandemic. The authors explore how and to what extent students in Poland have been using electronics for entertainm...

DOAJ Open Access (DOAJ) DOI: 10.2478/quageo-2022-0034