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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

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

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

Machine Learning for Sociology

Mario D. Molina, Filiz Garip • Annual Review of Sociology (2019)

Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and soci...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-soc-073117-041106

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

Ployrada Suvarnakuta, Intouch Prakaisak, Pittipol Kantavat, Boonserm Kijsirikul et al. • Engineered Science (2025)

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

DOAJ Open Access (DOAJ) DOI: 10.30919/es1972

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Mafalda Reis Pereira, Mafalda Reis Pereira, Filipe Neves dos Santos, Fernando Tavares et al. • Frontiers in Plant Science (2023)

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant dis...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpls.2023.1242201

Machine Learning–Based Wear Prediction of Recycled Magnesium Matrix Composites Reinforced With Ceramic Fibers

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam, Vivek Sivakumar et al. • Engineering Reports (2026)

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the...

DOAJ Open Access (DOAJ) DOI: 10.1002/eng2.70686

Machine learning in materials science

Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu et al. • InfoMat (2019)

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...

OPENALEX cc-by DOI: https://doi.org/10.1002/inf2.12028

Elements of Machine Learning

Pat Langley B (1995)

Elements of Machine Learning by Pat Langley Preface 1. An overview of machine learning 1.1 The science of machine learning 1.2 Nature of the environment 1.3 Nature of representation and performance 1.4 Nature of the learning component 1.5 F...

OPENALEX

Machine Learning for Medical Imaging

Bradley James Erickson, Panagiotis D. Korfiatis, Zeynettin Akkus, Timothy Lee Kline • Radiographics (2017)

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the ma...

OPENALEX Open Access DOI: https://doi.org/10.1148/rg.2017160130 PMC: 5375621

A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data

Longfei Cui, Xinyu Niu, Haizhong Qian, Xiao Wang et al. • Applied Sciences (2025)

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tail...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15052383

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Arfat Ahmad Khan, Muhammad Asif Nauman, Rab Nawaz Bashir, Rashid Jahangir et al. • IEEE Access (2022)

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2022.3206009

Inferring Markovian quantum master equations of few-body observables in interacting spin chains

Francesco Carnazza, Federico Carollo, Dominik Zietlow, Sabine Andergassen et al. • New Journal of Physics (2022)

Full information about a many-body quantum system is usually out-of-reach due to the exponential growth—with the size of the system—of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phen...

DOAJ Open Access (DOAJ) DOI: 10.1088/1367-2630/ac7df6

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

Introduction to Machine Learning

Yalın Baştanlar, Mustafa Özuysal • Methods in molecular biology (2013)

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity ...

OPENALEX Open Access DOI: https://doi.org/10.1007/978-1-62703-748-8_7

Exploring the Impact of Alternatives of Object-Oriented Cohesion Measures on Machine Learning-Based Predictions of Inheritance Reusability

Jehad Al Dallal, Bader Alkhazi • IEEE Access (2024)

The cohesion of an object-oriented class refers to the relatedness of its methods and attributes. Constructors, destructors, and access methods are special types of methods featuring unique characteristics that can artificially affect class...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3484007

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