Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. …”
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ConvXGB: A novel deep learning model to predict recurrence risk of early-stage cervical cancer following surgery using multiparametric MRI images by Ji Wu, Jian Li, Bo Huang, Sunbin Dong, Luyang Wu, Xiping Shen, Zhigang Zheng

“…We designed a novel deep learning model called “ConvXGB” for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). The predictive performance of the ConvXGB model was evaluated using time-dependent area under curve (AUC), compared with the deep learning radio-clinical model, clinical model, conventional radiomics nomogram and an existing histology-specific tool. …”
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Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset by Chang-Won Jeong, Dong-Wook Lim, Si-Hyeong Noh, Sung Hyun Lee, Chul Park

“…Methods We developed an automated lumbar spine slice classification model using the CNN (EfficientNetV2) algorithm and an automated domain segmentation model to identify the subcutaneous fat, visceral fat, and muscle areas using the U-NET algorithm. …”
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Use of artificial intelligence for gestational age estimation: a systematic review and meta-analysis by Sabahat Naz, Sahir Noorani, Syed Ali Jaffar Zaidi, Abdu R. Rahman, Saima Sattar, Jai K. Das, Jai K. Das, Zahra Hoodbhoy

“…In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). …”
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Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models by Li Zhang, Xiaodong Gao, Shuyi Zhou, Zhibo Zhang, Tianjie Zhao, Yaohui Cai, Xining Zhao

“…These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. …”
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Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion by Mehmood Nawaz, Sheheryar Khan, Muhammad Daud, Muhammad Asim, Ghazanfar Ali Anwar, Ali Raza Shahid, Ho Pui Aaron HO, Tom Chan, Daniel Pak Kong, Wu Yuan

“…The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. …”
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Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position... by Delong Xiang, Yuyan Sun, Hanji Zhu, Jianhua Wang, Sisi Huang, Shengmao Zhang, Famou Zhang, Heng Zhang

“…Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop <i>Illex argentinus</i> trawling ground prediction models. …”
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