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

“…The resulting analyses reveal hidden patterns in thermal data, contributing to improved disaster risk reduction strategies associated with volcanic hazards.…”
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Multi‐target cognitive electronic reconnaissance for unmanned aerial vehicles based on scene reconstruction by Yun Zhang, Shixun You, Yunbin Yan, Qiaofeng Ou, Jie Liu, Ling Chen, Xiang Zhu

“…The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. …”
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RECONFIGURATION OF THE AIRCRAFT INTEGRATED CONTROL SYSTEM REGARDING CONTROL CONSTRAINTS UNDER ACTUATOR FAILURES by A. M. Kulchak, V. V. Kosyanchuk, E. Yu. Zybin

“…This allows for a multistep consistent reduction in power and increasing in error of reconfiguration, until an acceptable solution is obtained. …”
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Disentangling High-Paced Alternating I/O in Gaze-Based Interaction by Yulia G. Shevtsova, Artem S. Yashin, Sergei L. Shishkin, Anatoly N. Vasilyev

“…By applying machine learning algorithms to gaze features and action context information, we achieved a threefold reduction in false positives, improved the quality of in-game decisions, and increased participant satisfaction with system ergonomics. …”
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Anisotropic metric-based mesh adaptation for ice flow modelling in Firedrake by D. Dundovic, J. G. Wallwork, S. C. Kramer, F. Gillet-Chaulet, R. Hock, R. Hock, M. D. Piggott

“…Since the fixed-point algorithms require that the problem is solved multiple times, the reported reduction in the number of vertices ultimately translates into a 3–6 times lower overall computational cost compared to uniform mesh simulations.…”
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Accuracy-Aware MLLM Task Offloading and Resource Allocation in UAV-Assisted Satellite Edge Computing by Huabing Yan, Hualong Huang, Zijia Zhao, Zhi Wang, Zitian Zhao

“…The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms.…”
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Identifying the optimal number of topics in text mining: a case study on reindeer pastoralism literature by Barbara Contiero, Øystein Holand, Giulio Cozzi

“…Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. …”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. …”
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A Novel Weighted Fractional GM(1,1) Model and Its Applications by Qin-Qin Shen, Quan Shi, Tian-Pei Tang, Lin-Quan Yao

“…Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.…”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. …”
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Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. …”
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Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning by Jiao Qiu, Yalin Xu, Luyuan Tong, Xingchun Yang, Xiao Wu

“…A LR model was developed, and its predictive accuracy was verified using the ROC curve derived from an external independent dataset. …”
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Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method by Jiawei Zhou, Junhao Wen, Liwen Yao, Zidong Yang, Lijun Xu, Lijian Yao

“…The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. …”
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Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

“…Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. …”
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Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records by Ioannis Koutroulis, Tom Velez, Tony Wang, Seife Yohannes, Jessica E. Galarraga, Joseph A. Morales, Robert J. Freishtat, James M. Chamberlain

“…Additional prospective studies are needed to validate clinical utility of predictive models that target derived pediatric sepsis phenotypes in emergency department settings.…”
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Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…The intersecting genes were identified from weighted gene co-expression network analysis (WGCNA), DEGs (osteosarcoma vs. normal) and DE-AAMRGs (high- vs. low-score). An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…The evaluation metrics include ROUGE score, BLEU score, cohesion, sensitivity, positive predictive value, readability, and scenarios of best, worst, and average case performance to ensure coherence, non-redundancy, and grammatical correctness. …”
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Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050 by Kyle A. Mani, BS, Samuel N. Goldman, BS, Noel Akioyamen, MD, Emily Kleinbart, BS, Yaroslav Gelfand, MD, Saikiran Murthy, DO, Jonathan Krystal, MD, Ananth Eleswarapu, MD, Reza Yassari, MD, Mitchell S. Fourman, MD, MPhil

“…Predictive performance was evaluated by splitting the data into training (75%) and validation (25%) sets, and calculating normalized root mean square error (NRMSE). …”
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Proximal Neural Networks based reconstruction for few-view CT applications by Hoang Trieu Vy Le, Caroline Bossuyt, Julie Escoda, Marius Costin, Jan De Beenhouwer, Jan Sijbers

“…Recent advancements in Plug-and-Play (PnP) algorithms have shown promise for solving imaging inverse problems by utilizing the capabilities of Gaussian denoising algorithms to handle complex optimization tasks. …”
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Information in Plant Life and Development: A Biosemiotic Approach by Peter W. Barlow

“…The cell maps resulting from these algorithms enable retrospective inferences and future predictions about the behaviour of the cellular systems con-cerned. …”
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