Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles DOI Creative Commons

Khaled Tarmissi,

Hanan Abdullah Mengash, Noha Negm

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(12), P. 35678 - 35701

Published: Jan. 1, 2024

<p>Autonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing detecting numerous targets real-time, mainly city arrangements adversarial environmental conditions, has become significant challenge for AVs. The effectiveness vehicle detection been measured as crucial stage visual surveillance or traffic monitoring. After developing driver assistance AV methods, weather conditions an essential problem. Nowadays, deep learning (DL) machine (ML) models are critical to enhancing object AVs, conditions. However, according statistical learning, conventional AI is fundamental, facing restrictions due manual feature engineering restricted flexibility adaptive environments. This study presents the explainable with fusion-based transfer on adverse autonomous (XAIFTL-AWCDAV) method. XAIFTL-AWCDAV model's main aim detect classify AVs challenging scenarios. In preprocessing stage, model utilizes non-local mean filtering (NLM) method noise reduction. Besides, performs extraction by fusing three models: EfficientNet, SqueezeNet, MobileNetv2. denoising autoencoder (DAE) technique employed Next, DAE method's hyperparameter selection uses Levy sooty tern optimization (LSTO) approach. Finally, ensure transparency predictions, integrates (XAI) techniques, utilizing SHAP visualize interpret each feature's impact decision-making process. efficiency validated comprehensive studies using benchmark dataset. Numerical results show that obtained superior value 98.90% over recent techniques.</p>

Language: Английский

Explainable Artificial Intelligence Visions on Incident Duration Using eXtreme Gradient Boosting and SHapley Additive exPlanations DOI Creative Commons
Khaled Hamad, Emran Alotaibi, Lubna Obaid

et al.

Multimodal Transportation, Journal Year: 2025, Volume and Issue: unknown, P. 100209 - 100209

Published: Feb. 1, 2025

Language: Английский

Citations

1
Selecting the most suitable 3D printing technology for custom manufacturing using fuzzy decision-making methodology DOI
Betül Yıldırım, Ertuğrul Ayyıldız

International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

Language: Английский

Citations

0
An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior DOI Creative Commons

Fanlong Zeng,

Jintao Wang,

Chaoyan Zeng

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0316287 - e0316287

Published: March 6, 2025

The accurate prediction and interpretation of corporate Environmental, Social, Governance (ESG) greenwashing behavior is crucial for enhancing information transparency improving regulatory effectiveness. This paper addresses the limitations in hyperparameter optimization interpretability existing models by introducing an optimized machine learning framework. framework integrates Improved Hunter-Prey Optimization (IHPO) algorithm, eXtreme Gradient Boosting (XGBoost) model, SHapley Additive exPlanations (SHAP) theory to predict interpret ESG behavior. Initially, a comprehensive dataset was developed through extensive literature review expert interviews. IHPO algorithm then employed optimize hyperparameters XGBoost forming IHPO-XGBoost ensemble model predicting Finally, SHAP used model's outcomes. results demonstrate that achieves outstanding performance greenwashing, with R², RMSE, MAE, adjusted R² values 0.9790, 0.1376, 0.1000, 0.9785, respectively. Compared traditional HPO-XGBoost combined other algorithms, exhibits superior overall performance. analysis using highlights key features influencing outcomes, revealing specific contributions feature interactions impacts individual sample features. findings provide valuable insights regulators investors more effectively identify assess potential behavior, thereby efficiency investment decision-making.

Language: Английский

Citations

0
Reviewing Additive Manufacturing Techniques: Material Trends and Weight Optimization Possibilities Through Innovative Printing Patterns DOI Open Access
A. Ramos, Virginia García-Angel, Miriam Siqueiros-Hernández

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(6), P. 1377 - 1377

Published: March 20, 2025

Additive manufacturing is transforming modern industries by enabling the production of lightweight, complex structures while minimizing material waste and energy consumption. This review explores its evolution, covering historical developments, key technologies, emerging trends. It highlights advancements in innovations, including metals, polymers, composites, ceramics, tailored to enhance mechanical properties expand functional applications. Special emphasis given bioinspired designs their contribution enhancing structural efficiency. Additionally, potential these techniques for sustainable industrial scalability discussed. The findings contribute a broader understanding Manufacturing’s impact on design optimization performance, offering insights into future research

Language: Английский

Citations

0
Extreme heat prediction through deep learning and explainable AI DOI Creative Commons

Fatima Shafiq,

Amna Zafar, Muhammad Usman Ghani Khan

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0316367 - e0316367

Published: March 20, 2025

Extreme heat waves are causing widespread concern for comprehensive studies on their ecological and societal implications. With the ongoing rise in global temperatures, precise forecasting of heatwaves becomes increasingly crucial proactive planning ensuring safety. This study investigates efficacy deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional (CNN) Long-Short Term Memory (LSTM), using five years meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance interpretability models. Although Weather has advanced predicting sunshine, rain, clouds, general weather patterns, extreme heat, particularly computer remains largely unexplored, overlooking this gap risks significant disruptions daily life. Our addresses collecting dataset developing a framework DL XAI models prediction. Key variables such as temperature, pressure, humidity, wind, precipitation examined. findings demonstrate that LSTM model outperforms others with lead time 1–3 days minimal error metrics, achieving an accuracy 96.2%. Through utilization SHAP LIME methods, we elucidate significance humidity maximum temperature accurately events. Overall, emphasizes how important it is investigate intricate integrate prediction heat. Making these understood allows us identify parameters, improving heatwave guiding risk-reduction strategies.

Language: Английский

Citations

0
Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning DOI Creative Commons
Rui Zhang,

Qichao Zhao,

Mingyue Liu

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0319540 - e0319540

Published: March 20, 2025

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer Water Conservation Reserves (WCR), deep learning to uncover regional patterns driving mechanisms. The model evaluates Xiong’an New Area’s characteristics from 2000 2020, showing 74% average increase depth with an inverted “V” spatial distribution. Spatiotemporal analysis identifies temporal changes, WCR land use, key protection areas, revealing that Area primarily shifts lowest areas lower areas. potential enhancement are concentrated northern region. Deep quantifies data complexity, highlighting critical factors precipitation, drought influencing WC. detailed enables development personalized zones strategies, offering new insights into managing complex data.

Language: Английский

Citations

0
Artificial Intelligence in Fault Diagnosis and Signal Processing DOI Creative Commons
Andrés Bustillo, Athanasios Karlis

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3922 - 3922

Published: April 3, 2025

Industry 4 [...]

Citations

0
An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations DOI Creative Commons

Baoqian Huan,

Xianglong Li, Jian-Guo Wang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 3, 2025

Language: Английский

Citations

0
Transforming Additive Manufacturing with Artificial Intelligence: A Review of Current and Future Trends DOI
Shyam S. Pancholi, Munish Kumar Gupta, Marian Bartoszuk

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Spatial Distribution Characteristics and Mechanical Properties of Artificial Coarse Aggregates with Different Morphologies in Asphalt Mixtures DOI Creative Commons
Bo Chen, Xinyu Wang, Zihao Li

et al.

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04667 - e04667

Published: April 1, 2025

Language: Английский

Citations

0