Flash Flood Regionalization for the Hengduan Mountains Region, China, Combining GNN and SHAP Methods by Yifan Li, Chendi Zhang, Peng Cui, Marwan Hassan, Zhongjie Duan, Suman Bhattacharyya, Shunyu Yao, Yang Zhao

“…The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. …”
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Exploring the integration of medical and preventive chronic disease health management in the context of big data by Yueyang Wang, Yueyang Wang, Ruigang Deng, Xinyu Geng

“…Through synthesizing existing research, we propose a novel framework that includes the development of machine learning models and interoperable health information platforms for real—time data sharing. …”
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Response Relationship Between Runoff and Meteorological Drought and Flood Characteristics in Jialing River Basin by LI Wen-hui, ZHANG Yang, CAO Hui, XING Long, REN Yu-feng, ZHAI Shao-jun, MA Yi-ming, LI Wen-da

“…The findings indicate that emerging machine learning models, such as support vector machines and random forests, can effectively simulate the complex mechanisms through which meteorological drought and flood events affect runoff in the river basin. …”
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Physics-Informed Neural Networks for Enhanced State Estimation in Unbalanced Distribution Power Systems by Petros Iliadis, Stefanos Petridis, Angelos Skembris, Dimitrios Rakopoulos, Elias Kosmatopoulos

“…Physics-Informed Neural Networks (PINNs) offer a compelling solution by integrating machine learning with the physical laws that govern power system behavior. …”
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Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data by Xiaoping Zhang, Xiaoping Zhang, Zixuan Cui, Chaoxian Feng, Xin Wen, Huabin Xiao, Jianbo Ni

“…By integrating spatial statistical analysis and high-precision machine learning models, the system has systematically revealed the spatio-temporal evolution laws, spatial differentiation characteristics and driving mechanisms of BEF on RBCE. …”
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Explainable multi-step heating load forecasting: Using SHAP values and temporal attention mechanisms for enhanced interpretability by Alexander Neubauer, Stefan Brandt, Martin Kriegel

“…The deployment of explainable artificial intelligence methodologies enables the actions of these machine learning models to be made transparent.In this study, a multi-step forecast was employed using an Encoder–Decoder model to forecast the hourly heating load for an multifamily residential building and a district heating system over a forecast horizon of 24-h. …”
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Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification by V. L. Sowmya, A. Bharathi Malakreddy, Santhi Natarajan, N. Prathik, I. S. Rajesh

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MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents by Moran Zhang, Qianqian Li, Shunhang Li, Binxian Sun, Zhuli Wu, Jinxuan Liu, Xingchao Geng, Fangyi Chen

“…<b>Methods:</b> MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. …”
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Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

“…The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices.…”
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A hybrid ECC-AES encryption framework for secure and efficient cloud-based data protection by P. Selvi, S. Sakthivel

“…Furthermore, this work outlines the integration of machine learning (ML)-based depression detection within the encrypted framework, ensuring privacy-preserving data analysis. …”
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Bitcoin price direction prediction using on-chain data and feature selection by Ritwik Dubey, David Enke

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Embedded feature selection using dual-network architecture by Abderrahim Abbassi, Arved Dörpinghaus, Niklas Römgens, Tanja Grießmann, Raimund Rolfes

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Graph2Mat: universal graph to matrix conversion for electron density prediction by Pol Febrer, Peter Bjørn Jørgensen, Miguel Pruneda, Alberto García, Pablo Ordejón, Arghya Bhowmik

“…As a prediction target for machine learning (ML) models, electron density is often represented on a dense real space grid, which is data heavy, or through density fitting approximations. …”
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A Review of Finite Element Studies on Laser-Based Acoustic Applications in Solid Media by Evaggelos Kaselouris, Vasilis Dimitriou

“…In biomedical applications, the FEM aids in tissue characterization and disease detection, while in engineering, its integration with laser-based methods contributes to noise reduction and vibration control. Furthermore, this review provides a comprehensive synthesis of FEM simulations and experimental validation while also highlighting the emerging role of artificial intelligence and machine learning in optimizing FEM models and improving computational efficiency, which has not been addressed in previous studies. …”
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Supply Chains Problem During Crises: A Data-Driven Approach by Farima Salamian, Amirmohammad Paksaz, Behrooz Khalil Loo, Mobina Mousapour Mamoudan, Mohammad Aghsami, Amir Aghsami

“…This study addresses these challenges by proposing a novel bi-objective optimization framework. The model integrates a Mixed-Integer Linear Programming (MILP) approach with advanced machine learning techniques to simultaneously minimize total costs and maximize patient satisfaction. …”
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Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations by Moustapha Jadayel, Farbod Khameneifar

“…This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D printed parts produced by Fused Filament Fabrication (FFF), a widely used material extrusion process in which thermoplastic filament is heated and deposited layer by layer to form a part. …”
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Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks by Ghazi Gharsallah, Georges Kaddoum

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A New Hyperspectral Image Identification Method Based on LSDA and OSELM by Chengjiang Zhou, Mingli Yang, Sasa Duan, Lu Shao, Zhilin Zhang, Yunfei Liu

“…To solve the problems of information loss and nonlinear transformation of hyperspectral image dimensionality reduction methods and the problem that recognition methods are sensitive to noise and cannot be trained online, a new hyperspectral image identification method based on locality sensitive discriminant analysis (LSDA) and online sequential extreme learning machine (OSELM) is proposed. …”
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Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules by Xiao Wang, Hui Xue, Wei Ding, Fei Huang, Yu Zhang, Xin Pang

“…Radiomic features from tumor margins of 1, 2, 3, 4, and 5 mm were extracted. Eight machine learning models were constructed following dimensionality reduction. …”
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Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning by Satoshi Fukui, Lihua Wang, Seiichi Ozawa

“…With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. …”
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