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Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Jongho Lee, Jiuk Shin, Jaewook Lee, Chorong Park et al. • Applied Sciences (2023)

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

DOAJ Open Access (DOAJ) DOI: 10.3390/app132111866

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

Somporn Sahachaiseree, Takashi Oguchi • IET Intelligent Transport Systems (2025)

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

DOAJ Open Access (DOAJ) DOI: 10.1049/itr2.70034

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Sun-Feel Yang, So-Won Choi, Eul-Bum Lee • Energies (2023)

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affe...

DOAJ Open Access (DOAJ) DOI: 10.3390/en16114271

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Heechan Han, Boran Kim, Kyunghun Kim, Donghyun Kim et al. • Water Science and Technology (2023)

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...

DOAJ Open Access (DOAJ) DOI: 10.2166/wst.2023.237

Overfitting and undercomputing in machine learning

Thomas G. Dietterich • ACM Computing Surveys (1995)

suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough search...

OPENALEX Open Access DOI: https://doi.org/10.1145/212094.212114

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

G. Baaklini, G. Baaklini, R. El Hourany, M. Fakhri et al. • Ocean Science (2022)

The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping t...

DOAJ Open Access (DOAJ) DOI: 10.5194/os-18-1491-2022

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

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

Introduction to Machine Learning

• Series in machine perception and artificial intelligence (2019)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict ...

OPENALEX DOI: https://doi.org/10.1142/9789811201967_0001

Machine Learning

Ethem Alpaydın • The MIT Press eBooks (2021)

A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product ...

OPENALEX Open Access DOI: https://doi.org/10.7551/mitpress/13811.001.0001

Machine learning and deep learning

Christian Janiesch, Patrick Zschech, Kai Heinrich • Electronic Markets (2021)

Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of a...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12525-021-00475-2

Machine learning-based diagnostic prediction of minimal change disease: model development study

Ryunosuke Noda, Daisuke Ichikawa, Yugo Shibagaki • Scientific Reports (2024)

Abstract Minimal change disease (MCD) is a common cause of nephrotic syndrome. Due to its rapid progression, early detection is essential; however, definitive diagnosis requires invasive kidney biopsy. This study aims to develop non-invasiv...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-73898-4

Investigating the Role of Artificial Intelligence in Management of Diabetes in Iran: A Systematic Review

Fatemeh Bahador, Azam Sabahi, Samaneh Jalali, Fatemeh Ameri • پیاورد سلامت (2023)

Background and Aim: Diabetes is one of the most common metabolic diseases in Iran and the fifth leading cause of death all over the world. Its spread around the world has created new methods in biomedical research, including artificial inte...

DOAJ Open Access (DOAJ)

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Galen Richardson, Sylvain G. Leblanc, Julie Lovitt, Krishan Rajaratnam et al. • Drones (2021)

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones5030099

Optimizing Ultrasonic-Assisted Extraction Process of <i>Paralepista flaccida</i>: A Comparative Study of Antioxidant, Anticholinesterase, and Antiproliferative Activities via Response Surface Methodology and Artificial Neural Network Modeling

Mustafa Sevindik, Ayşenur Gürgen, Aras Fahrettin Korkmaz, Ilgaz Akata • Molecules (2025)

In this study, extraction conditions were optimized to maximize the biological activities of extracts obtained from Paralepista flaccida, an edible mushroom species. Extraction processes were carried out using an ultrasonically assisted sys...

DOAJ Open Access (DOAJ) DOI: 10.3390/molecules30163317