On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

“…This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. …”
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Predicting traffic flow between bike-sharing system stations: A case study of Chicago by Ali Behroozi, Ali Edrisi

“…Specifically, we compare two parallel multilayer perceptron deep learning models, incorporating matrix factorization and gate recurrent unit (GRU) neural networks. …”
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The Influence of Non-Landslide Sample Selection Methods on Landslide Susceptibility Prediction by Yu Fu, Zhihao Fan, Xiangzhi Li, Pengyu Wang, Xiaoyue Sun, Yu Ren, Wengeng Cao

“…The EIV method leverages machine learning to assign adaptive weights to influencing factors, prioritizing sample selection in low-susceptibility regions and avoiding high-susceptibility areas, thereby enhancing sample quality. …”
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Data-driven analysis and visualization of dielectric properties curated from scientific literature by Tomoki Murata, Naoto Saito, Eiji Koyama, Ton Nu Thanh Phuong, Ryusuke Misawa, Satoshi Yokomizo, Tomoya Mato, Yu Takada, Sakyo Hirose, Yukari Katsura

“…This dataset enabled the development of machine learning models with high predictive performance and facilitated the identification of important descriptors through recursive feature eliminations. …”
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence by Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad

“…To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. …”
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Distributed energy sharing algorithm for Micro Grid energy system based on cloud computing by Wenwei Su, Yan Shi

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Opportunities and Limitations of Wrist-Worn Devices for Dyskinesia Detection in Parkinson’s Disease by Alexander Johannes Wiederhold, Qi Rui Zhu, Sören Spiegel, Adrin Dadkhah, Monika Pötter-Nerger, Claudia Langebrake, Frank Ückert, Christopher Gundler

“…Each representation was assessed on public datasets to identify the best-performing machine learning model and subsequently applied to our own collected dataset to assess generalizability. …”
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The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China by Zhihao Song, Zhihao Song, Wen He, Yuefeng Yao, Ling Yu, Jinjun Huang, Yong Xu, Haoyu Wang

“…This performance gain was attributed to rigorous overfitting control: PLSR’s dimensionality reduction synergized with ensemble machine learning (RF, XGBoost, MLP) to eliminate redundant spectral features while retaining predictive signals. …”
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Players’ Performance Prediction for Fantasy Premier League, Using Transformer-based Sentiment Analysis on News and Statistical Data by Tamimi M., Tran T.

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Key technical indicators for stock market prediction by Seyed Mostafa Mostafavi, Ali Reza Hooman

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Unveiling the drivers contributing to global wheat yield shocks through quantile regression by Srishti Vishwakarma, Xin Zhang, Vyacheslav Lyubchich

“…Furthermore, we assess the relationships between shocks and their key ecological and socioeconomic drivers using quantile regression based on statistical (linear quantile mixed model) and machine learning (quantile random forest) models. …”
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Efficient feature selection based on Gower distance for breast cancer diagnosis by Salwa Shakir Baawi, Mustafa Noaman Kadhim, Dhiah Al-Shammary

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Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer by Yuzhe Zhang, Yuhang Xue, Yongju Gao, Ye Zhang

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Balancing CICIoV2024 Dataset with RUS for Improved IoV Attack Detection by Muhammad David Firmansyah, Ifan Rizqa, Fauzi Adi Rafrastara

“…This research employed RUS to mitigate data imbalance within the CICIoV2024 dataset, which often impedes effective threat detection in machine learning models. Four machine learning classifiers Random Forest, AdaBoost, Gradient Boosting, and XGBoost were evaluated on both imbalanced and balanced datasets to compare their performance. …”
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Elucidating thermal phenomena of non-Newtonian experimental data based copper-alumina-ethylene glycol hybrid nanofluid in a cubic enclosure with central heated plate by machine lea... by Md. Mamun Molla, Md Farhad Hasan, Md. Mahadul Islam

“…Finally, a cross-validation performance analysis was conducted using a machine learning model and good accuracy was obtained. …”
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Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes

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Impacts of long-range transport of aerosols and biomass burning smoke from the Bay of Bengal to the Indian Ocean by Benjamin de Foy, Noora Khaleel, Thameem Abdul Razzaq, Md Firoz Khan, Siti Jariani Mohd Jani, Michael H Bergin, James J Schauer

“…In this way, they are a type of Interpretable Machine Learning (IML) / eXplainable Artificial Intelligence (XAI) that provide quantitative information on the sources of PM _2.5 . …”
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Testing an inverse modeling approach with gradient boosting regression for stroke volume estimation using patient thermodilution data by Vasiliki (Vicky) Bikia, Dionysios Adamopoulos, Dionysios Adamopoulos, Dionysios Adamopoulos, Marco Roffi, Marco Roffi, Georgios Rovas, Stéphane Noble, Stéphane Noble, François Mach, François Mach, Nikolaos Stergiopulos

“…We developed a modified method for SV estimation that combines a validated 1-D model of the systemic circulation with machine learning. …”
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Innovative Machining Strategies for Metal Matrix Composites: Trends and Future Prospects by Olugbenga Ogunbiyi, Tamba Jamiru, Samson Olaitan Jeje, Kazeem Oladiti Sanusi, Mxolisi Brendon Shongwe

“…The transformative role of artificial intelligence (AI) and machine learning (ML) in process optimization is explored, showcasing improvements in precision, tool wear reduction, and surface quality. …”
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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…A potential solution involves using data-driven machine learning (ML) black-box models. This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). …”
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