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Showing 20 of 30 academic articles

Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques

Zainab Tahir, Muhammad Haseeb, Syed Amer Mahmood, Saira Batool et al. • Scientific Reports (2025)

Abstract This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-87796-w

A Novel Construction Method of Bayesian Neural Networks Based on Multi-type Engineering Knowledge

Wenbin Ye, YanZhou Duan, Jun Yuan, Jixu Wang et al. • International Journal of Computational Intelligence Systems (2026)

Abstract In the field of engineering, the utilization of surrogate models to replace computationally intensive simulation software has become a widely adopted approach. However, when addressing complex engineering problems, the costs of sim...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44196-026-01214-1

Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis With Adaptive Compression and Fire Source Detection

Yanbing Bai, Rui-Yang Ju, Lemeng Zhao, Junjie Hu et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2026)

Uncrewed Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by facilitating aerial video analysis. Due to the limited computational resources available on UAVs, large models cannot be run efficiently fo...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2026.3685660

Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization

Senyefia Bosson-Amedenu, Emmanuel Ayitey, Francis Ayiah-Mensah, Luyton Asare • BMC Cancer (2025)

Abstract Background Frailty models are extensively utilized in survival analysis to address unobserved heterogeneity among individuals. However, selecting the most robust model for survival prediction, especially in the context of high-dime...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12885-025-14040-z

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Sachin Kumar, Mahendra Kumar Choudhary, T. Thomas • Scientific Reports (2024)

Abstract Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybri...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-77655-5

Prediction of metabolic status of dairy cows in early lactation using milk fatty acids and test-day variables

Muluken Girma, A.T.M. van Knegsel, S. Heirbaut, L. Vandaele et al. • Journal of Dairy Science (2023)

ABSTRACT: Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with o...

DOAJ Open Access (DOAJ) DOI: 10.3168/jds.2022-22702

Visual Context and Behavioral Priming in Pedestrian Crossing Decisions: Evidence from a Stated Preference Experiment in Ecuadorian Urban Areas

Yasmany García-Ramírez, Fernando Arrobo-Herrera, Alejandra Cruz-Cortez, Luis Fernández-Garrido et al. • Smart Cities (2026)

Pedestrian safety in developing countries faces critical challenges from rapid urbanization and infrastructure deficiencies. This study investigates how visual context influences pedestrian crossing preferences through a controlled stated p...

DOAJ Open Access (DOAJ) DOI: 10.3390/smartcities9010019

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University) (2016)

We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1609.07061

Training‐Efficient Personalised Knee Joint Moment Estimation via an Incremental Broad Learning System

Guoyu Zuo, Minghui Zhang, Jingwei Ren, Qifei Wu et al. • CAAI Transactions on Intelligence Technology (2026)

ABSTRACT Knee joint moment estimation is a critical component in biomechanical analysis, with profound implications for rehabilitation assessment and the development of assistive devices like exoskeletons. However, existing data‐driven appr...

DOAJ Open Access (DOAJ) DOI: 10.1049/cit2.70165

Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets

Michał Wawer, Jarosław A. Chudziak, Ewa Niewiadomska-Szynkiewicz • Applied Sciences (2024)

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability...

DOAJ Open Access (DOAJ) DOI: 10.3390/app142411897

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Aditi S. Krishnapriyan, Joseph Montoya, Maciej Haranczyk, Jens Hummelshøj et al. • Scientific Reports (2021)

Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-021-88027-8

A lightweight optimization framework for real-time pedestrian detection in dense and occluded scenes

C. Chen, J. Li, J. Li, Z. Shuai et al. • Mechanical Sciences (2025)

Pedestrian detection is one of the most widely applied tasks in industrial computer vision. It encapsulates three core challenges of object detection: detecting small objects, handling heavy occlusion, and balancing speed and accuracy for d...

DOAJ Open Access (DOAJ) DOI: 10.5194/ms-16-877-2025

Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

Xiaosai Duan, Suihuai Yu, Jianjie Chu, Dengkai Chen et al. • Buildings (2022)

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...

DOAJ Open Access (DOAJ) DOI: 10.3390/buildings12111880

Deep Learning-Based Prediction of the Axial Capacity of CFRP-Strengthened Concrete Columns

Nasim Shakouri Mahmoudabadi, Charles V. Camp, Afaq Ahmad • Infrastructures (2026)

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concre...

DOAJ Open Access (DOAJ) DOI: 10.3390/infrastructures11050151

Rethinking Gated Recurrent Units for Rotating Machinery Prognostics: A Physics-Consistency Benchmark on the Mismatch Between Gating Mechanisms and Degradation Dynamics

Zhonghua Feng, Minglun Ren • Applied Sciences (2026)

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, wh...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16178379

Deep learning: a statistical viewpoint

Peter L. Bartlett, Andrea Montanari A, Alexander Rakhlin • Acta Numerica (2021)

The remarkable practical success of deep learning has revealed some major surprises from a theoretical perspective. In particular, simple gradient methods easily find near-optimal solutions to non-convex optimization problems, and despite g...

OPENALEX cc-by DOI: https://doi.org/10.1017/s0962492921000027

Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning

Jiuxin Wang, Yutian Luo, Zhengming Yang, Xinli Zhao et al. • Applied Sciences (2021)

In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in ...

DOAJ Open Access (DOAJ) DOI: 10.3390/app11083589