Results 41 to 50 of about 13,208 (247)

A Hybrid Approach to Music Recommendations Based on Audio Similarity Using Autoencoder and LightGBM

Journal of Applied Informatics and Computing
Music recommendation systems help users navigate large music collections by suggesting songs aligned with their preferences. However, conventional methods often overlook the depth of audio content, limiting personalization and accuracy.
Winda Ardelia Aristawidya, Majid Rahardi
doaj   +1 more source

Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk Stratification

Arthritis &Rheumatology, EarlyView.
Objective Proteome‐wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. Methods Patients with lupus and individuals with other autoimmune diseases in the UK Biobank were included.
Mehmet Hocaoǧlu, Jishnu Das, Amr H. Sawalha   +2 more
wiley   +1 more source

Estimating the water quality index based on interpretable machine learning models

Water Science and Technology
The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and
Shiwei Yang, Ruifeng Liang, Junguang Chen, Yuanming Wang, Kefeng Li   +4 more
doaj   +1 more source

Artificial Intelligence–Driven and Digital Practices for Circular Business and Finance: Insights for Advancing Hubs for Circularity

Business Strategy and the Environment, EarlyView.
ABSTRACT The emerging concept of Hubs for Circularity (H4Cs) presents an opportunity to create collaborative, self‐sustaining regional industrial ecosystems that drive circular economy transitions at scale. However, the operationalisation of H4Cs faces financial, organisational and data‐driven challenges.
Aditya Tripathi, Marcos R. Machado, Laura Spierdijk, Devrim Murat Yazan   +3 more
wiley   +1 more source

An interpretable machine learning model for predicting forest fire danger based on Bayesian optimization

Emergency Management Science and Technology
As global warming increases forest fire frequency, early prevention and effective management become crucial. This requires models that are both accurate and easily understood.
Zhiyang Liu, Kuibin Zhou, Qichao Yao, Pedro Reszka   +3 more
doaj   +1 more source

Forecasting Airport Passenger Flows Using Mobile Network Data: Insights for Sustainable Mobility and Strategic Infrastructure Planning

Business Strategy and the Environment, EarlyView.
ABSTRACT Airports are strategic and environmental nodes in global transport systems where rapid passenger growth challenges capacity, sustainability, and resilience. This study uses anonymized mobile network data to analyze and forecast passenger dynamics at Lisbon Airport, Portugal, linking data‐driven insights to sustainable mobility and ...
João Carlos Ferreira, Carlos M. P. Sousa, Marco Esperança, Babak Taheri   +3 more
wiley   +1 more source

Application of LightGBM in the Chinese stock market

Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network
This study employs LightGBM, a gradient boosting decision tree model, to predict stock returns and identify key pricing factors in the Chinese A-share market. The empirical analysis yields two main findings. First, LightGBM demonstrates superior predictive performance, achieving a monthly out-of-sample R² of 2.13%, more than doubling the 0.95%
openaire   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source

Comparing the Performance of Algorithmic Trading Systems based on Machine Learning in the Cryptocurrency Market [PDF]

راهبرد مدیریت مالی
The purpose of this research is to use the ensemble learning model to combine the predictions of random forest models, short-term long memory and recurrent neural network to provide an algorithmic trading system based on its.
Emad Koosha, Mohsen Seighaly, Ebrahim Abbasi   +2 more
doaj   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source