Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI by Amr M. Hamed, Abdel-Fattah Attia, Heba El-Behery

“…This study proposes an optimized classification framework that evaluates ten machine learning (ML) models, emphasizing ensemble methods, feature selection (FS), and outlier utilization. …”
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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. by Toviah Moldwin, Li Shay Azran, Idan Segev

“…We demonstrate that by modulating the plasticity thresholds and calcium influx from each calcium source, we can reproduce a wide range of learning and plasticity protocols, such as Hebbian and anti-Hebbian learning, frequency-dependent plasticity, and unsupervised recognition of frequently repeating input patterns. …”
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Pre-Filtering SCADA Data for Enhanced Machine Learning-Based Multivariate Power Estimation in Wind Turbines by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Haoxuan Luo

“…Finally, the performance of the power estimation model is validated using two wind turbine datasets and two machine learning algorithms, with results compared with and without filtering. …”
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A Comprehensive Feature Extraction Network for Deep-Learning-Based Wildfire Detection in Remote Sensing Imagery by Haiyan Pan, Die Luo, Yuewei Zhang

“…These techniques usually depend on set thresholds or the extraction of local features, which can lead to incorrect positives and overlooked detections. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. …”
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DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting by Jialei Cao, Wenxia Zheng, Yao Ge, Jiyuan Wang

“…This article presents DriftShield, a novel adaptive fraud detection framework that addresses these limitations through four key technical innovations: (1) the first application of Soft Actor-Critic (SAC) reinforcement learning with continuous action spaces to fraud detection, enabling simultaneous fine-grained optimization of detection thresholds and feature importance weights; (2) a dynamic feature reweighting mechanism that automatically adapts to evolving fraud patterns while providing interpretable insights into changing fraud strategies; (3) an adaptive experience replay buffer combining sliding windows with prioritized sampling to balance catastrophic forgetting prevention with rapid concept drift adaptation; and (4) an entropy-driven exploration framework with automatic temperature tuning that intelligently balances exploitation of known fraud patterns with discovery of emerging threats. …”
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FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets by Nazerke Baisholan, J. Eric Dietz, Sergiy Gnatyuk, Mussa Turdalyuly, Eric T. Matson, Karlygash Baisholanova

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Machine Learning Ensemble Methods for Co-Seismic Landslide Susceptibility: Insights from the 2015 Nepal Earthquake by Tulasi Ram Bhattarai, Netra Prakash Bhandary

“…Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience.…”
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RaDiT: A Differential Transformer-Based Hybrid Deep Learning Model for Radar Echo Extrapolation by Wenda Zhu, Zhenyu Lu, Yuan Zhang, Ziqi Zhao, Bingjian Lu, Ruiyi Li

“…Our differential attention mechanism enhances noise suppression under high-threshold conditions, effectively minimizing spurious feature generation while improving metric reliability. …”
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Experimenting with learning-based image orientation approaches for photogrammetric mapping of <em>Posidonia oceanica</em> meadows by F. Menna, A. Calantropio, A. Pansini, G. Ceccherelli, E. Nocerino

“…Moreover we verified that, in such a complex scenario, it is crucial to adjust processing thresholds at the different stages of SfM (from matching to bundle adjustment) and take manual intervention measures to improve image orientation.…”
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Frontal plane mechanical leg alignment estimation from knee x-rays using deep learning by Kenneth Chen, Christoph Stotter, Christopher Lepenik, Thomas Klestil, Christoph Salzlechner, Stefan Nehrer

“…In this study, we develop and validate a deep learning model that classifies leg alignment as “normal” or “malaligned” from knee antero-posterior (AP)/postero-anterior (PA) radiographs alone, using an adjustable hip-knee-ankle (HKA) angle threshold. …”
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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

“…ADA was identified as the most important feature. The ROC of machine learning model outperformed those of conventional diagnostic thresholds. …”
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The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects by Chunying Yan, Zhanfang Zhu, Xueyan Guo, Wei Zong, Guisheng Liu, Yan Jin, Shiyuan Cui, Fuqiang Liu, Shujuan Gao

“…The model demonstrated an amplification of effects in subgroups with severe obesity (OR = 2.66, 95% CI: 2.08–3.24) and impaired fasting glucose.DiscussionThis study establishes a machine learning framework for assessing multi-pollutant risks in NAFLD, identifying 2-Hydroxynaphthalene as a significant hepatotoxicant and EPEI as a quantifiable metric of exposure. …”
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Enhancing Voice Activity Detection for an Elderly-Centric Self-Learning Conversational Robot Partner in Noisy Environments by Subashkumar Rajanayagam, Max Andreas Ingrisch, Pascal Müller, Patrick Jahn, Stefan Twieg

“…Voice Activity Detection (VAD) is a root component in Human-Robot Interaction (HRI), especially for use cases such as a self-learning personalized conversational robot partner designed to support elderly users with high acceptance. …”
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The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss by Gerardo Roa-Dabike, Michael A. Akeroyd, Scott Bannister, Jon P. Barker, Trevor J. Cox, Bruno Fazenda, Jennifer Firth, Simone Graetzer, Alinka Greasley, Rebecca R. Vos, William M. Whitmer

“…This paper details the first use of an open challenge methodology to improve the audio quality of music for those with hearing loss through machine learning. The first challenge (CAD1) had 9 participants. …”
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Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis by Zhao W, Sun P, Li W, Shang L

“…Integration into hospital information systems with real-time dashboards could enhance early intervention strategies.Keywords: MDRO, machine learning, prediction, intensive care unit…”
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Development and Evaluation of a Machine Learning Model for Predicting 30-Day Readmission in General Internal Medicine by Abdullah M. Al Alawi, Mariya Al Abdali, Al Zahraa Ahmed Al Mezeini, Thuraiya Al Rawahia, Eid Al Amri, Maisam Al Salmani, Zubaida Al-Falahi, Adhari Al Zaabi, Amira Al Aamri, Hatem Al Farhan, Juhaina Salim Al Maqbali

“…The ensemble model improved recall to 70.6% through adjusted thresholds and model weighting, though precision declined. …”
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Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models by Zhao Rong Liu, Zhao Rong Liu, Zhan Jiang Yu, Jie Zhou, Jian Biao Huang

“…PurposeThis study aimed to develop three types of machine learning (ML) models based on gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) to explore their predictive value for the stone-free rate after percutaneous nephrolithotomy (PCNL).Patients and methodsA retrospective analysis was conducted on 160 patients who underwent PCNL. …”
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Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant by Jacopo Burrello, Stefano Panella, Ilaria Barison, Chiara Castellani, Alessio Burrello, Lorenzo Airale, Jessica Goi, Veronica Dusi, Roberto Frigerio, Gino Gerosa, Chiara Tessari, Nicola Pradegan, Giuseppe Toscano, Giovanni Pedrazzini, Mattia Corianò, Francesco Tona, Sara Bolis, Alessandro Gori, Marina Cretich, Marny Fedrigo, Annalisa Angelini, Lucio Barile

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The impact of project-based learning on mathematics interest and self-efficacy among senior high school students by Arief Aulia Rahman, Nyak Wha Usalmy, César Hernández, Craig N Refugio

“…The robustness of these results was confirmed by Pillai's Trace, Wilks' Lambda, Hotelling's Trace, and Roy's Largest Root tests, each demonstrating F-values with a significance level of 0.000, well below the threshold of 0.05. Moreover, the Coefficient of determination (R²) revealed that the PjBL model accounted for 73.5% of the variance in learning interest and 90.2% in self-efficacy, with the remaining variance being attributable to other factors not addressed in this study. …”
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