WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images by Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li

“…Experimental validation using the JPL, Airey, and ViViD++ thermal infrared datasets demonstrates that the proposed algorithm exhibits superior real-time performance and robustness across various environments. …”
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Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm by Songyue Han, Mingyu Wang, Junhong Duan, Jialong Zhang, Dongdong Li

“…Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. …”
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A levy chaotic horizontal vertical crossover based artificial hummingbird algorithm for precise PEMFC parameter estimation by Pradeep Jangir, Absalom E. Ezugwu, Kashif Saleem, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, G. Gulothungan, Laith Abualigah

“…In particular, we propose a multi strategy variant, the Lévy Chaotic Artificial Hummingbird Algorithm (LCAHA), which combines sinusoidal chaotic mapping, Lévy flights and a new cross update foraging strategy. …”
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Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer by Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao

“…Simulated images were generated as follows: [A] a ResNet model was trained to identify recurrent cervix cancer; [B] a model was evaluated on T2W MRI data for subjects to obtain corresponding feature maps; [C] most important feature maps were determined for each image; [D] feature maps were combined across all images to generate a simulated image; [E] the final image was reviewed by a radiation oncologist and an initial algorithm to identify the likelihood of recurrence. …”
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Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models by Patrick H. Kelly, Rob Kwark, Harrison M. Marick, Julie Davis, James H. Stark, Harish Madhava, Gerhard Dobler, Jennifer C. Moïsi

“…Importantly, we demonstrate the utility of ML models to generate reliable insights into TBE hazard risks when trained with sufficient explanatory variables and to provide high resolution and harmonized risk maps for public use.…”
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Mapping the air temperature in China from time-normalized MODIS land surface temperature data via zone-based stacking ensemble models by Yan Xin, Yongming Xu, Xudong Tong, Yaping Mo, Yonghong Liu, Shanyou Zhu

“…However, several critical issues have been overlooked in current remote sensing-based Ta estimation, including temporal inconsistencies of land surface temperature (LST), spatial variability over large-scale area, and limitations of single-algorithm approaches. This study developed a three-step ensemble framework to address these issues and map high-accuracy daily average Ta from remote sensing data. …”
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Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa by Boris Ouattara, Michael Thiel, Barbara Sponholz, Heiko Paeth, Marta Yebra, Florent Mouillot, Patrick Kacic, Kwame Hackman

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Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm by Balachandran Nair Premakumari Sreeja, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler

“…Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods—namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test—across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. …”
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Research on injection molded parts defect detection algorithm based on multiplicative feature fusion and improved attention mechanism by Rongnan Zhang, Yang Li, Zhiguang Guan

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Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend by Haoyu (Richard) Zhuang, Arthur T. DeGaetano, Flavio Lehner

“…Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. …”
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Four-dimensional, dynamic mosaicism is a hallmark of normal human skin that permits mapping of the organization and patterning of human epidermis during terminal differentiation. by Yun Wang, Taro Masaki, Sikandar G Khan, Deborah Tamura, Christiane Kuschal, Megan Rogers, John J DiGiovanna, Kenneth H Kraemer

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Enhancing Path Planning for Autonomous Robots in Large, Obstacle-Crowded Environments: A Practical Improvement to the PRM Algorithm by Shimon Aviram, Eugene Levner

“…Additionally, when encountering obstacles, the algorithm searches for detour options in relatively small, obstacle-crowded subareas rather than processing each obstacle individually or the entire map. …”
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The Lightweight Fracture Segmentation Algorithm for Logging Images Based on Fully 3D Attention Mechanism and Deformable Convolution by Qishun Yang, Liyan Zhang, Zihan Xi, Yu Qian, Ang Li

“…Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. …”
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Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering applications by Yujie Chen, Guangyu Wang, Baichuan Yin, Chongyun Ma, Zhiqiao Wu, Ming Gao

“…The No Free Lunch theorem implies that no single optimiser dominates across all problem classes, making domain-specific metaheuristics indispensable. …”
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Predicting the high-strain-rate deformation behavior and constructing processing maps of 304L stainless steel through machine learning and deep learning by M. Ghaffari Farid, H.R. Abedi, R. Ghasempour, A. Taylor, S. Khoddam, P.D. Hodgson

“…This study deals with predicting the high-temperature compressive flow behavior of stainless steel 304L employing the machine learning and deep learning algorithms. An special focus has been laid on the high strain rate regime, where the phenomenological models are basically incapable of precise predicting. …”
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Novel HIV-1 recombinants spreading across multiple risk groups in the United Kingdom: the identification and phylogeography of Circulating Recombinant Form (CRF) 50_A1D. by Geraldine M Foster, John C Ambrose, Stéphane Hué, Valerie C Delpech, Esther Fearnhill, Ana B Abecasis, Andrew J Leigh Brown, Anna Maria Geretti, UK HIV Drug Resistance Database

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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

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Drone-Captured Wildlife Data Encryption: A Hybrid 1D–2D Memory Cellular Automata Scheme with Chaotic Mapping and SHA-256 by Akram Belazi, Héctor Migallón

“…This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. …”
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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

“…ResultsThe model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000‐1.000), recall of 1.000 (95% CI 1.000‐1.000), mAP50 of 0.995 (95% CI 0.995‐0.995), and mAP95 of 0.893 (95% CI 0.870‐0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. …”
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