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Showing 11 of 31 academic articles

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

From Causes to Forecasts: Granger Causality and Machine Learning Predictions of Housing Sales in Türkiye

Musa Gün, Ahmet Akusta, Haydar Karadağ • İstanbul İktisat Dergisi (2025)

This study aims to examine the impact of macro-financial indicators on the housing sales volume in Türkiye and evaluate the forecasting performance of traditional time-series models versus modern machine-learning algorithms. Using monthly d...

DOAJ Open Access (DOAJ) DOI: 10.26650/ISTJECON2025-1707729

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

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

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Ammar Alnmr, Ammar Alnmr, Rashad Alsirawan, Haidar Hosamo et al. • Frontiers in Built Environment (2026)

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

DOAJ Open Access (DOAJ) DOI: 10.3389/fbuil.2026.1829950

Multi-Resolution and Multi-Temporal Satellite Remote Sensing Analysis to Understand Human-Induced Changes in the Landscape for the Protection of Cultural Heritage: The Case Study of the MapDam Project, Syria

Nicodemo Abate, Diego Ronchi, Sara Elettra Zaia, Gabriele Ciccone et al. • Land (2025)

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating sat...

DOAJ Open Access (DOAJ) DOI: 10.3390/land14112233

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

Jian Liu, Fangyu Liu, Linbing Wang • Journal of Traffic and Transportation Engineering (English ed. Online) (2024)

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, whic...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jtte.2023.10.002

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network

Johannes Leiner, Vincent Pellissier, Sebastian König, Sven Hohenstein et al. • Respiratory Research (2022)

Abstract Background Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both i...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12931-022-02180-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