Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…Objective: The chest computed tomography severity score (CT-SS) is significantly associated with the severity of the disease and subsequently intensive care unit (ICU) admission in coronavirus disease-19 (COVID-19) patients. …”
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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…Object detection is a crucial aspect of computer vision (CV), which involves classifying objects within images, enabling applications such as image retrieval, augmented reality, and many more. …”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

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Evaluation of Mechanical Characteristics of Tungsten Inert Gas (TIG) Welded Butt Joint of Inconel 600 by Arash Moradi, Fatemeh Marashi Najafi, Yong Chen, Mahmoud Chizari

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Optimization of a Heavy-Duty Hydrogen-Fueled Internal Combustion Engine Injector for Optimum Performance and Emission Level by Murat Ozkara, Mehmet Zafer Gul

“…This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). …”
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Predicting abnormal epicardial adipose tissue in psoriasis patients by integrating radiomics from non-contrast chest CT with serological biomarkers by Rui Han, Juan Hou, Ping Xia, Yan Xing, Wenya Liu

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A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting by Honghui Wan, Feiyu Zhao

“…This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. …”
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M-RSF: a multilevel feedback queue task scheduling mechanism for Unikernel by DONG Bonan, YANG Qiusong, LI Mingshu

“…Unikernel, as a cutting-edge technology in the field of cloud computing, is characterized by its fast start-up speed and minimal resource usage. …”
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Comparative morpho-topometric characteristics of human T_I and T_VI vertebrae in the first period of adulthood and old age by A. A. Balandin, O. A. Chudinov, I. A. Balandina

“…CT examination was performed on an Optima 660 computed tomograph (GE HealthCare, USA), the height, width and anteroposterior size of the TI and TVI vertebrae were measured. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…The model has small parameters and low computational effort and is suitable for edge devices. …”
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Protocol for processing and analyzing multiplexed images improves lymphatic cell identification and spatial architecture in human tissue by Justin A. Smith, Drew T. Bloss, MacKenzie D. Williams, Amanda L. Posgai, Todd M. Brusko, Mark A. Atkinson, Clive H. Wasserfall, Maigan A. Brusko

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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…Experiments conducted on public datasets, including URPC, Brackish, and TrashCan, showed that the mAP@0.5 reached 74.1%, 97.5%, and 66.2%, respectively, with parameter sizes and computational complexities of 2.7M and 7.2 GFLOPs, and the model size is only 5.9 Mb. …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational cost, and inability to retain longer-term dependencies. …”
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An improved lightweight tongue segmentation model with self-attention parallel network and progressive upsampling by Xuan Wang, Yifang Cao, Yijia Chen, Huixia Li, Aiqing Han, Yan Tang

“…The improved model not only has fewer parameters but also exhibits a notably lower computational complexity compared to classical models. …”
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Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…We propose filamentary convolution to replace rectangular kernels, reducing the parameters while preserving inter-frame features by focusing solely on frequency patterns. …”
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An Efficient Fine-Grained Recognition Method Enhanced by Res2Net Based on Dynamic Sparse Attention by Qifeng Niu, Hui Wang, Feng Xu

“…Furthermore, strategic architectural optimizations are applied throughout to minimize computational complexity, resulting in a model that demands significantly fewer parameters and exhibits faster inference times. …”
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Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection by Jiahui Zhang, Zoia Vladimirovna Beliaeva, Yue Huang

“…A lightweight C3Ghost module reduces parameter count and computation, while a bidirectional multi-scale feature fusion structure replaces the standard neck to enhance efficiency. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. …”
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