Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…The introduction of the Focal-EIOU loss function calculates the width and height differences of target bounding boxes and uses Focal Loss to address the imbalance of difficult and easy samples, achieving faster convergence and superior localization capability. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mspace width="3.33333pt"></mspace><mn>1.96</mn><mi>σ</mi></mrow></semantics></math></inline-formula> = −0.00175 ± 1.04. …”
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Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps by Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu, Shuo Hu, Tammie L. S. Benzinger, Jie Lu, Yun Zhou

“…A three dimensional-convolutional neural network model was employed for AD detection using standardized uptake value ratio (SUVR) images. …”
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Prediction of Mechanical Strength Based on Deep Learning Using the Scanning Electron Image of Microscopic Cemented Paste Backfill by Xuebin Qin, Shifu Cui, Lang Liu, Pai Wang, Mei Wang, Jie Xin

“…In addition, the difference between the measured and predicted values is calculated and the mean and variance of the error are analyzed. …”
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Approximate Crank–Nicolson Algorithm with Higher-Order PML Implementation for Plasma Simulation in Open Region Problems by Liqiang Niu, Yongjun Xie, Jie Gao, Peiyu Wu, Haolin Jiang

“…More precisely, the proposed implementation is based on the CN Direct-Splitting (CNDS) procedure for the finite-difference time-domain (FDTD) unmagnetized plasma simulation. …”
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SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing by Qingjun Niu, Kun Wu, Jialu Zhang, Zhenqi Han, Lizhuang Liu

“…SDEC combines wavelet transform and difference convolution(DC) to enhance high-frequency features while preserving low-frequency information. …”
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The Cultural Value Validity of Digital Media Art Based on Deep Learning Network Model by Yuan Ruan

“…In order to solve this problem, this paper proposes a deep learning neural network model based on a dual-core compression activation module. The convolution kernels of different sizes in one module are used to extract the overall features and local details of the image, and another module is used to achieve the main goal. …”
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AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma by Franz Wegner, Malte Maria Sieren, Hanna Grasshoff, Lennart Berkel, Christoph Rowold, Marcel Philipp Röttgerding, Soleiman Khalil, Sam Mogadas, Felix Nensa, René Hosch, Gabriela Riemekasten, Anna Franziska Hamm, Nikolas von Bubnoff, Jörg Barkhausen, Roman Kloeckner, Cyrus Khandanpour, Theo Leitner

“…The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. …”
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Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG by Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin

“…Furthermore, we investigate the generalization capability of the models by using multisource data collected within the same period, as well as single-source data gathered across different periods. This is the first time that we utilize ASO-S/FMG data for flare forecasting. …”
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Spatiotemporal Feature Enhancement for Lip-Reading: A Survey by Yinuo Ma, Xiao Sun

“…And each spatiotemporal feature enhancement method was divided into different subclasses based on the differences in the architecture structure, feature attributes, and application types. …”
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Surface water mapping from remote sensing in Egypt’s dry season using an improved U-Net model with multi-scale information and attention mechanism by Yong Li, Xiuhui Liu, Vagner Ferreira, Heiko Balzter, Huiyu Zhou, Ying Ge, Meiyun Lai, Simin Chu, Han Ding, Zhenrong Gu

“…During dry seasons, Egyptian water bodies exhibit unique challenges for remote sensing detection due to their significant spectral differences, complex morphological patterns, and numerous small streams. …”
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Artificial Intelligence in the Surgery-First Approach: Harnessing Deep Learning for Enhanced Condylar Reshaping Analysis: A Retrospective Study by Umberto Committeri, Gabriele Monarchi, Massimiliano Gilli, Angela Rosa Caso, Federica Sacchi, Vincenzo Abbate, Stefania Troise, Giuseppe Consorti, Francesco Giovacchini, Valeria Mitro, Paolo Balercia, Antonio Tullio

“…The SFA and SLA exhibited similar rates of condylar surface remodeling, with minor differences in resorption and formation across dentoskeletal classifications. …”
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EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation by Zhe Yang, Baozhong Mu, Mingxun Wang, Xin Wang, Jie Xu, Baolu Yang, Cheng Yang, Hong Li, Rongqi Lv

“…It requires models to balance computational cost and performance by employing more efficient mechanisms to effectively recognize differences in RGB-D multimodal information, retain complementary information, and reduce redundancies. …”
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Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds by Rui Zhang, Guanlong Huang, Fengpu Bao, Xin Guo

“…Finally, a multi-neighborhood feature fusion strategy was developed that combines the attention mechanism to fuse the local features of different neighborhoods and obtain global features with fine-grained information. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images by Daniel L. van den Kroonenberg, Florian T. Delberghe, Auke Jager, Arnoud W. Postema, Harrie P. Beerlage, Wim Zwart, Massimo Mischi, Jorg R. Oddens

“…Automated prostate segmentation facilitates workflows, and zonal segmentation can aid in PC diagnosis, accounting for differences in imaging characteristics and tumor incidence. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. …”
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Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip by Ruixin Li, Xiao Wang, Tianran Li, Beibei Zhang, Xiaoming Liu, Wenhua Li, Qirui Sui

“…Results The results obtained from both manual measurements and the artificial intelligence model demonstrated no significant differences in the Sharp, Tönnis, and Center edge angles (all p > 0.05). …”
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TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…This convolutional operation is embedded following standard convolution to mitigate the loss of detailed features. …”
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Dual scale light weight cross attention transformer for skin lesion classification. by Dhirendra Prasad Yadav, Bhisham Sharma, Shivank Chauhan, Julian L Webber, Abolfazl Mehbodniya

“…The attention from different scales improved the spatial features by focusing on the different parts of the skin lesion. …”
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