Coordinated Reentry Guidance with A* and Deep Reinforcement Learning for Hypersonic Morphing Vehicles Under Multiple No-Fly Zones by Cunyu Bao, Xingchen Li, Weile Xu, Guojian Tang, Wen Yao

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Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE/GRACE-FO Gravimetric Data and Machine Learning by Mohamed Hamdi, Anas El Alem, Kalifa Goita

“…The prediction model was developed using a machine-learning approach based on multiple linear regression to estimate GWS variations as a function of various environmental parameters. …”
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An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson’s disease with purine metabolism-related genes by Yao Wang, Dongchuan Wu, Man Zheng, Tiantian Yang

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Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome by Seul Gi Park, Sae Byeol Mun, Young Jae Kim, Kwang Gi Kim

“…This study explores the integration of machine learning and 3D motion capture gait data for effective classification of these conditions. …”
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DBS-NET: A Dual-Branch Network Integrating Supervised and Contrastive Self-Supervised Learning for Birdsong Classification by Ziyi Wang, Hao Shi, Yan Zhang, Yong Cao, Danjv Lv

“…Additionally, to address class imbalance in the dataset, a weighted loss function is introduced to adjust the cross-entropy loss with optimized class weights. …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

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An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques by Salman Mahmood, Raza Hasan, Saqib Hussain, Rochak Adhikari

“…This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. …”
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Development of an imitation learning method for a neural network system of mobile robot’s movement on example of the maze solving by T. Yu. Kim, R. A. Prakapovich

“…The second agent, the student agent, tries to reduce the distance to the first agent by trial and error. The learning process was implemented using the reinforcement learning method, which was used in the imitation mode and for which a corresponding reward function was developed, allowing the robot's center of mass to be kept in the center of the corridor and, if necessary, to turn, following the expert agent. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

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Biografisches und traditionsorientiertes Erzählen als elementare Lernformen der Katechese by Monika Scheidler

Subjects: “…profile of catechetical learning…”
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Function focused care in hospital among nurses and patients from geriatric and neurology wards: A mixed method process evaluation by Selma Kok, Elke G.E. Mathijssen, Lisette Schoonhoven, Carolien Verstraten, Silke F. Metzelthin, Nienke Bleijenberg, Janneke M. de Man-van Ginkel

“…Background: Function Focused Care is a promising approach stimulating physical activity of patients admitted to hospital. …”
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Active learning based surrogate ensemble assisted multi-objective optimization framework for reservoir water-flooding optimization by Lian Wang, Liang Zhang, Rui Deng, Jianhua Qu, Hehua Wang, Liehui Zhang, Xing Zhao, Bing Xu, Xindong Lv, Caspar Daniel Adenutsi

“…In this proposed ALSA-MOPO method, three frequently-used surrogate models, the radial basis function network, Gaussian process regression, and support vector regression are adopted to construct the surrogate ensemble. …”
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CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…This study investigates the use of modified montmorillonite (MMT) for CO₂ adsorption through an integrated approach combining Machine Learning (ML) modeling and Response Surface Methodology (RSM). …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…The aim of this paper is to analyze the architecture of attack detection that are deployed in the IoT network and correspondingly demonstrate whether their function is to track and follow or even attack subjects. …”
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Dual Focus-3D: A Hybrid Deep Learning Approach for Robust 3D Gaze Estimation by Abderrahmen Bendimered, Rabah Iguernaissi, Mohamad Motasem Nawaf, Rim Cherif, Séverine Dubuisson, Djamal Merad

“…Key innovations include a multimodal feature fusion strategy, an angular loss function optimized for 3D gaze prediction, and regularization techniques to mitigate overfitting. …”
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Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning by Guido Ascenso, Andrea Ficchì, Matteo Giuliani, Enrico Scoccimarro, Andrea Castelletti

“…In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…Additionally, a Gini-based feature selection method is implemented to prioritize the most discriminative features, and the extracted features from each network are optimally combined using a fusion technique modeled after a linear production function, maximizing each model’s contribution to the final prediction. …”
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A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making by Hamed Poursiami, Ayana Moshruba, Keiland W. Cooper, Derek Gobin, Md Abdullah-Al Kaiser, Ankur Singh, Rouhan Noor, Babak Shahbaba, Akhilesh Jaiswal, Norbert J. Fortin, Maryam Parsa

“…Abstract Efficient decision-making in context-dependent, sequential tasks remains a fundamental challenge in reinforcement learning (RL). Inspired by the function of the brain’s hippocampal system, we introduce Hippocampal-Augmented Memory Integration (HAMI), a biologically inspired memory-based RL framework that leverages symbolic indexing, hierarchical memory refinement, and structured episodic retrieval to enhance both learning efficiency and adaptability. …”
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Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology by Ayako Yagahara, Masahito Uesugi, Hideto Yokoi

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Optimizing oil production forecasts in Iranian oil fields: a comprehensive analysis using ensemble learning techniques by Mohammad Ghodsi, Pouya Vaziri, Mahdi Kanaani, Behnam Sedaee

“…Abstract This study introduces the application of Stacking Ensemble Learning in petroleum engineering, marking a significant advancement in oil production rate forecasting. …”
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