Reset
Showing 20 of 978 academic articles

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Sabina-Cristiana Necula • Behavioral Sciences (2023)

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavio...

DOAJ Open Access (DOAJ) DOI: 10.3390/bs13060439

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan

Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid et al. • ISPRS International Journal of Geo-Information (2024)

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with heal...

DOAJ Open Access (DOAJ) DOI: 10.3390/ijgi13090330

Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation

Caricato A., Ficarella A., Chiodo L. Spada • E3S Web of Conferences (2021)

Predictive maintenance is the latest frontier in the management and maintenance of many industrial assets, including aeroengines. Made possible by last decades advances in monitoring equipment and machine learning algorithms, it permits ind...

DOAJ Open Access (DOAJ) DOI: 10.1051/e3sconf/202131211017

Explainable machine learning in materials science

Xiaoting Zhong, Brian J. Gallagher, Shusen Liu, Bhavya Kailkhura et al. • npj Computational Materials (2022)

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41524-022-00884-7

Big data and machine learning for materials science

Jose Fernando Rodrigues Junior, Larisa Florea, Maria Cristina Ferreira de Oliveira, Dermot Diamond et al. • Discover Materials (2021)

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the so...

OPENALEX cc-by DOI: https://doi.org/10.1007/s43939-021-00012-0 PMC: 8054236

Machine learning-based dynamic risk measurement for white sugar futures under geopolitical risks

Zihao Qiu, Siyu Chen, Zixin Feng, Ruitong Luo et al. • Frontiers in Physics (2025)

Futures, as significant financial derivatives, play a crucial role in financial markets by fulfilling price discovery functions and providing efficient risk hedging tools. Against the backdrop of geopolitical conflicts, market risk emerges ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphy.2025.1674717

Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors

Mohammed Saleh Alshaikh • International Journal of Emerging Research in Engineering, Science, and Management (2025)

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Re...

DOAJ Open Access (DOAJ) DOI: 10.58482/ijeresm.v4i4.4

Quantum Computing in Transportation Engineering: A Survey

Shriyank Somvanshi, Md Monzurul Islam, Sazzad Bin Bashar Polock, Gaurab Chhetri et al. • IEEE Transactions on Intelligent Transportation Systems (2026)

Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superpo...

OPENALEX Open Access DOI: https://doi.org/10.1109/tits.2026.3662626

Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images

Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen, Shiqing Wei et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025)

To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. This stu...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2025.3571988

Machine learning in plant science and plant breeding

Aalt D. J. van Dijk, Gert Kootstra, Willem Kruijer, Dick de Ridder • iScience (2020)

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to in...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.isci.2020.101890 PMC: 7750553

3D Printing parameter optimisation combined with heat treatment for achieving high density and enhanced performance in refractory high-entropy alloys

Deyu Jiang, Miao Luo, Changxi Liu, Yashuo Zhang et al. • Virtual and Physical Prototyping (2025)

In this study, a Ti1.5Nb1Ta0.5Zr1Mo0.5 (TNTZM) high-entropy alloy was fabricated using laser powder bed fusion (LPBF). By integrating 63 sets of parameter trials with machine learning (ML) models, an optimised process window was identified,...

DOAJ Open Access (DOAJ) DOI: 10.1080/17452759.2025.2524524

Modeling antiretroviral therapy adherence among people living with HIV: a cross-sectional study at Pantang Hospital, Ghana

Eric Nyarko, Irene A. Kretchy, Grace Lovia Allotey-Babington, Adwoa Oforiwaa Kwakye et al. • BMC Infectious Diseases (2026)

Abstract Background Poor adherence to antiretroviral therapy (ART) continues to undermine HIV treatment outcomes in sub-Saharan Africa, including Ghana. Objective This study aimed to quantify ART treatment adherence among people living with...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12879-026-13836-z

Integration of Hydrological Model and Time Series Model for Improving the Runoff Simulation: A Case Study on BTOP Model in Zhou River Basin, China

Qintai Xiao, Li Zhou, Xin Xiang, Lingxue Liu et al. • Applied Sciences (2022)

Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated run...

DOAJ Open Access (DOAJ) DOI: 10.3390/app12146883

A robust and uncertainty-aware machine learning framework for PM2.5 prediction in a coastal urban Indian environment: implications for sustainable air quality management

Salvator Lawrence, Srimuruganandam Bhathmanabhan • Sustainable Environment Research (2026)

Abstract Accurate prediction of fine particulate matter (PM2.5) remains challenging because atmospheric processes are nonlinear, nonstationary, and influenced by temporal variability, measurement uncertainty, and episodic pollution events. ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s42834-026-00296-y

Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Mahbub Ul Islam Khan, Md. Ilius Hasan Pathan, Mohammad Mominur Rahman, Md. Maidul Islam et al. • IEEE Access (2024)

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable pri...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3400913

Developing an Interferogram-Based Module with Machine Learning for Maintaining Leveling of Glass Substrates

Syuan-Cheng Chang, Chung-Ping Chang, Yung-Cheng Wang, Chi-Chieh Chu • Machines (2023)

In this research, we propose a method that utilizes machine learning to maintain the parallelism of the resonant cavity in a Fabry–Perot interferometer designed specifically for glass substrates. Based on the optical principle and theory, w...

DOAJ Open Access (DOAJ) DOI: 10.3390/machines11060668