Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications by William O’Donnell, David Mahon, Guangliang Yang, Simon Gardner

“…To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. …”
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Composite dark energy and the cosmological tensions by Adrià Gómez-Valent, Joan Solà Peracaula

“…However, to alleviate the tensions the DE needs more features. In the proposed wXCDM model [45], the DE is actually a composite cosmic fluid with two components (X,Y) acting sequentially: first X (above a transition redshift zt) and second Y (below zt). …”
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MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention by Zhao Liangjun, Ning Feng, Xi Yubin, Liang Gang, He Zhongliang, Zhang Yuanyang

“…The fused attention c2fSE module is integrated into the YOLOv8n baseline network to optimize feature extraction for SAR ships. In addition, the DenseASPP module is incorporated to enhance the model’s adaptability to ships of varying scales, improving its ca-pability to accommodate larger ships within lower model scales. …”
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A Robust Tuberculosis Diagnosis Using Chest X-Rays Based on a Hybrid Vision Transformer and Principal Component Analysis by Sameh Abd El-Ghany, Mohammed Elmogy, Mahmood A. Mahmood, A. A. Abd El-Aziz

“…<b>Results</b>: The performance of our CAD model was assessed against existing models, yielding excellent results. …”
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Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia by Jamilu Sani, Salad Halane, Abdiwali Mohamed Ahmed, Mohamed Mustaf Ahmed

“…Seven ML algorithms were evaluated for predictive performance, with Random Forest emerging as the optimal model based on metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). …”
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Real-time fear emotion recognition in mice based on multimodal data fusion by Hao Wang, Zhanpeng Shi, Ruijie Hu, Xinyi Wang, Jian Chen, Haoyuan Che

“…By integrating and analyzing these distinct data sources through feature encoders and attention classifiers, we developed a robust emotion classification model. …”
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Predicting interval from diagnosis to delivery in preeclampsia using electronic health records by Xiaotong Yang, Hailey K. Ballard, Aditya D. Mahadevan, Ke Xu, David G. Garmire, Elizabeth S. Langen, Dominick J. Lemas, Lana X. Garmire

“…Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.…”
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MixRformer: Dual-Branch Network for Underwater Image Enhancement in Wavelet Domain by Jie Li, Lei Zhao, Heng Li, Xiaojun Xue, Hui Liu

“…To address the problems of insufficient global modeling in existing CNN models, weak local feature extraction of Transformer and high computational complexity, multi-resolution feature decomposition is performed through a discrete wavelet transform (IWT/DWT) in which low-frequency components retain structure and texture, and high-frequency components capture detail features. …”
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Situational Awareness Prediction for Remote Tower Controllers Based on Eye-Tracking and Heart Rate Variability Data by Weijun Pan, Ruihan Liang, Yuhao Wang, Dajiang Song, Zirui Yin

“…The annotated dataset containing 25 ET and HRV features is trained using the LightGBM model optimized by a Tree-structured Parzen Estimator, and feature selection and model interpretation are performed using the SHapley Additive exPlanations (SHAP) analysis. …”
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LBT-YOLO: A Lightweight Road Targeting Algorithm Based on Task Aligned Dynamic Detection Heads by Pei Tang, Zhenyu Ding, Minnan Jiang, Weikai Xu, Mao Lv

“…Secondly, a new neck network structure BCFPN (Bidirectional Collocated Feature Pyramid Network) is designed based on the weighted bidirectional feature pyramid network, which enhances the feature fusion and the interaction of contextual information, and improves the detection accuracy of the model. …”
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Pyrolysis Kinetics of Pine Waste Based on Ensemble Learning by Alok Dhaundiyal, Laszlo Toth

“…It was seen that the boosting technique did not provide a reasonable fit if the number of features was increased in the kinetic models. This happened owing to an inability to maintain a tradeoff between variance and bias. …”
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Analysis of disease severity and mortality prediction using machine learning during COVID-19 by Hodjat (Hojatollah) Hamidi, Mostafa Moradi

“…Therefore, it can be concluded that the Logistic Regression model performs best in diagnosing both classes.…”
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Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis by Abida Noaman, Reyaz Ahmad, Muhammad Farhan Khan, Abdul Salam Mohammed, Muhammad Farooq, Khan Muhammad Adnan

“…This variation in class size has been overcome using data augmentation and resizing in the preprocessing phase. Preprocessing is performed in such a way that no colour detail or rich identification features will be lost from the images. …”
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Precision Recognition of Rock Thin Section Images With Multi‐Head Self‐Attention Convolutional Neural Networks by Pengfei Lv, Weiying Chen, Xinyu Zou

“…Notably, in the most challenging scenario with 105 rock categories, the MSA‐VGG16 model outperforms the previously reported best‐performing model on the same data set by approximately 9.61%. …”
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Design and application of human-computer interaction visual communication platform for Guandong culture by integrating RF and light GBM algorithm by Qiao Wu, Ying Jin

“…The results showed that the model classification accuracy after combining long short-term feature embedding techniques reached 95%, which could better capture the time series features of user behavior. …”
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Survey of research on encrypted traffic classification based on machine learning by FU Yu, LIU Taotao, WANG Kun, YU Yihan

“…The content was further subdivided into seven stages: traffic collection, dataset construction, data preprocessing, feature extraction, feature selection, classification models, and performance evaluation. …”
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Anesthetic action on the transmission delay between cortex and thalamus explains the beta-buzz observed under propofol anesthesia. by Meysam Hashemi, Axel Hutt, Darren Hight, Jamie Sleigh

“…This feature is called the beta-buzz. In the present study, we use a thalamo-cortical neural population feedback model to reproduce observed characteristic features in frontal EEG power obtained experimentally during propofol general anesthesia, such as this beta-buzz. …”
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Pathway-like Activation of 3D Neuronal Constructs with an Optical Interface by Saeed Omidi, Yevgeny Berdichevsky

“…Three-dimensional neuronal organoids, spheroids, and tissue mimics are increasingly used to model cognitive processes in vitro. These 3D constructs are also used to model the effects of neurological and psychiatric disorders and to perform computational tasks. …”
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Simultaneous Color Registration and Depth Completion of Point Clouds with Curriculum Learning by Juan Camilo Martinez, Ana María Montes, Cesar Marín, David Álvarez-Martínez

“…By leveraging shared features between the depth and color tasks, the joint model outperforms separate single-task approaches. …”
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Comprehensive Insights into Global Mineral Commodities: Analysis, Visualization, and Intelligent Assistance by Trupti Mohanty, Hasan M. Sayeed, Chitrasen Mohanty, Taylor D. Sparks

“…We evaluated multiple open-source LLMs for the RAG task and selected the best-performing model, llama-3, to implement in the system. …”
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