V-STAR: A Cloud-Based Tool for Satellite Detection and Mapping of Volcanic Thermal Anomalies by Simona Cariello, Arianna Beatrice Malaguti, Claudia Corradino, Ciro Del Negro

“…In contrast, advanced machine learning algorithms offer a data-driven alternative. …”
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REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised tria... by Lily G Bessette, Elad Yom-Tov, Niteesh K Choudhry, Julie C Lauffenburger, Nancy Haff, Marie E McDonnell, Constance P Fontanet, Ellen S Sears, Erin Kim, Kaitlin Hanken, Renee A Barlev, Punam A Keller, J Joseph Buckley

“…By contrast, reinforcement learning is a machine learning method that can be used to identify individuals’ patterns of responsiveness by observing their response to cues and then optimising them accordingly. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…A cohort of 1,578 pediatric KD cases was systematically divided into training and validation sets. Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…GEDI LiDAR proved to be a necessary input for accurate SV and AGB retrieval, and GPR was the best-performing ML algorithm. The resulting spatial maps were artifact-free and successfully delineated ecological gradients and management patterns. …”
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Bridging aging, immunity, and atherosclerosis: novel insights into senescence-related genes by Yan Lu, Rong Yuan, Rong Yuan, Qiqi Xin, Qiqi Xin, Keji Chen, Keji Chen, Weihong Cong, Weihong Cong

“…Subsequently, differential expression analysis, weighted gene co-expression network analysis, accompanied by 3 machine learning algorithms, LASSO, SVM and RF, were performed to identify diagnostic genes. …”
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Exploring the Relationship between Spatiotemporal Distribution of Urban Vibrancy and Neighborhood Attributes by Coupling Multi-Source Data: A Case of Nanshan District in Shenzhen by Liu Feng, Tang Zhong, Zhang Liang, Yu Lingmin, Liu Ke

“…This work focuses on the Nanshan District of Shenzhen City as the study area, and has three objectives: (i) one-week passenger flow data that characterized the spatiotemporal distribution of urban vibrancy were provided; (ii) broadly collected Street View Images (SVI) were incorporated as a visional environmental factor, together with functional and morphological factors, into understanding the influencing mechanism of urban vibrancy; and finally, (iii) machine learning tools were employed to apply the Random Forest Regression (RFR) algorithm in exploring the independent driving role of the characteristic factors behind the temporal distribution of urban vibrancy, and the Geographically Weighted Regression (GWR) model was used to probe the influence of the characteristic factors on the spatial distribution of this dynamic concept. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…ObjectiveWe aimed to develop and evaluate a machine learning (ML)–based digital phenotyping approach to monitor the severity of clinically-relevant MS symptoms. …”
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Identification of m5C RNA modification-related gene signature for predicting prognosis and immune microenvironment-related characteristics of heart failure by Zirui Liu, Rui Feng, Ying Xu, Meili Liu, Haocheng Wang, Yu Lu, Weiqi Wang, Jikai Wang, Cao Zou

“…Four hub genes were identified by machine-learning algorithms and all verified by validation model. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…It also proposes the Solar FaultNet‐a novel deep learning‐based approach that significantly improves fault detection performance in solar PV systems and integrates the model with state‐of‐the‐art ML techniques like CNN and LSTM to capture inherent complex patterns and interdependencies of fault data. Besides, the proposed model outperforms conventional machine‐learning algorithms and state‐of‐the‐art deep‐ learning models for better performance by yielding higher accuracy, precision, recall, F1‐score, and low error rate on various fault types such as PV array faults, inverter faults, grid synchronization faults, and environmental faults. …”
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Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients by Jianan Zheng, Haoran Lin, Wei Ye, Mingjun Du, Chenjun Huang, Jun Fan

“…Key marker genes were identified using the FindAllMarkers function, and these genes were subsequently analyzed for mutations, copy number variations, and prognostic significance in LUAD patients. Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”
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A comprehensive review of bibliometric and methodological approaches in flood mitigation studies: Current trends and future directions by Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi

“…Furthermore, it highlights the growing diversity of approaches, with increasing interest in machine learning algorithms and combined methods. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. …”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). …”
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Bi-modal contrastive learning for crop classification using Sentinel-2 and Planetscope by Ankit Patnala, Scarlet Stadtler, Scarlet Stadtler, Martin G. Schultz, Martin G. Schultz, Juergen Gall, Juergen Gall

“…However, the existing algorithms require a huge amount of annotated data. …”
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An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications by Ahmed Shakir Ali Ali, Farhad Jazaei, Peyman Babakhani, Muhammad Masood Ashiq, Alireza Bakhshaee, Brian Waldron

“…DL models utilize complex algorithms to identify patterns that may be difficult to observe with traditional physics-based models, specifically where the underlying physics is complex or poorly understood or where the available physical model is too simple. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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Lost in translation: using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution by Dorian Quelle, Calvin Yixiang Cheng, Alexandre Bovet, Scott A. Hale

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Advancing Neurodegenerative Disease Management: Technical, Ethical, and Regulatory Insights from the NeuroPredict Platform by Marilena Ianculescu, Lidia Băjenaru, Ana-Mihaela Vasilevschi, Maria Gheorghe-Moisii, Cristina-Gabriela Gheorghe

“…Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Abstract Background In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. …”
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