Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. …”
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KNEE OSTEOARTHRITIS STAGE CLASSIFICATION BASED ON HYBRID FUSION DEEP LEARNING FRAMEWORK by Delveen Luqman Abd Alnabi, Shereen Sh Ahmed, Nisreen Luqman Abd Alnabi

“…The feature-level, decision-level, score-level, and meta-based fusion technologies were also performed on the outputs of the best three trained models to minimize the individual models’ errors. …”
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Impaired cognitive function and decreased monoamine neurotransmitters in the DNAJC12 gene knockout mouse model by Shunan Wang, Ming Shen, Bo Pang, Bo Zhou, Yuan Yuan, Mei Lu, Xiangling Deng, Min Yang, Shufang Liu, Qiong Wang, Mei Xue, Qisheng Xia, Zhixin Zhang

“…Purpose This study aims to elucidate the role of DNAJC12 in intellectual disability and explore the mechanisms by which DNAJC12 deficiency leads to hyperphenylalaninemia through developing a DNAJC12 gene knockout mouse model. Methods We thoroughly examined the clinical features and genetic mutations evident in two patients with biallelic mutations in the DNAJC12 gene. …”
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Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia by Yihang Bao, Wanying Wang, Zhe Liu, Weidi Wang, Xue Zhao, Shunying Yu, Guan Ning Lin

“…By utilizing multimodal features, our deep learning model achieved a classification accuracy of 0.81 and an AUC of 0.9. …”
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Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

“…The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. …”
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Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT. by Haoran Zhang, Jinlong Liu, Danyang Su, Zhen Bai, Yan Wu, Yuanbo Ma, Qiuju Miao, Mingyue Wang, Xiaopeng Yang

“…<h4>Conclusion</h4>A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. …”
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Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings by Yijie Qian, Hongying Pan, Jun Chen, Hongyang Hu, Mei Fang, Chen Huang, Yihong Xu, Yang Gao

“…Given the rapid advancements in computer technology, we aimed to develop an interpretable artificial intelligence (AI) model for predicting DRPI, utilizing SHAP (SHapley Additive exPlanations) to enhance the model’s transparency and provide insights into feature importance. …”
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Adapting Vision Transformer-Based Object Detection Model for Handwritten Text Line Segmentation Task by Osman Furkan KARAKUŞ, Ayla GULCU, Ali Can KARACA

“…DETR’s use of a transformer’s global attention mechanism allows it to better understand the entire context of an image rather than relying solely on local features. This is particularly beneficial for managing the diverse and complex patterns found in handwritten text where traditional models might struggle with issues such as overlapping text lines or varied handwriting styles.…”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…This framework applies transfer learning to adapt pre-trained features to new traffic environments. This enhances vehicle classification in complex urban scenes and improves the model&#x2019;s ability to distinguish vehicles from non-vehicle objects. …”
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A deep-learning-based consistency test approach for Earth system models on HPC systems by Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, Xiaojing Lv

“…Summary: Evaluating climate consistency is a critically important step in the development and optimization of Earth system models (ESMs) on the high-performance computing (HPC) systems. …”
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DB-Net: A Dual-Branch Hybrid Network for Stroke Lesion Segmentation on Non-Contrast CT Images by Xiao Jia, He Dong, Jiashu Xu, Yanhong Zhang, Yihua Lan

“…The CNN branch of the encoder performs local feature extraction whereas the Transformer branch captures global dependencies, overcoming limitations of CNN in modeling long-range relationships. …”
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A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil by Eberton Rodrigues de Oliveira Neto, Fábio Júnior Damasceno Fernandes, Tuany Younis Abdul Fatah, Raquel Macedo Dias, Zoraida Roxana Tejada da Piedade, Antonio Fernando Menezes Freire, Wagner Moreira Lupinacci

“…However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. …”
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Creating a Novel Attention-Enhanced Framework for Video-Based Action Quality Assessment by Wenhui Gong, Wei Li, Huosheng Hu, Zhijun Song, Zhiqiang Zeng, Jinhua Sun, Yuping Song

“…To assess feature quality, we employ score distribution regression and propose an uncertainty-aware score distribution learning strategy that models features as Gaussian distributions. …”
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Seismic Behavior of Bahareque Walls Under In-Plane Horizontal Loads by Karol Cristancho, Iván Fernando Otálvaro, Daniel M. Ruiz, Natalia Barrera, Jesús D. Villalba-Morales, Yezid A. Alvarado, Orlando Cundumí

“…This research addresses this gap by experimentally evaluating the seismic behavior of five wall models with different combinations of guadua, wood, and earth filling materials. …”
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Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology by Xuejiao Zhong

“…Experimental results: the comparison of SVR1 and SVR2 shows that the utilization of multiscale timing feature maps should occur after tem (SVR2) rather than being directly fused in the feature dimension (SVR1), mainly because small-scale information affects the resolution of large-scale information; on data sets such as ActivityNet, in order to verify the effectiveness of SVR and DR-Dvc algorithms, the performance of the proposed algorithm and the baseline before improvement and the current mainstream algorithm are respectively compared. …”
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A disentangled generative model for improved drug response prediction in patients via sample synthesis by Kunshi Li, Bihan Shen, Fangyoumin Feng, Xueliang Li, Yue Wang, Na Feng, Zhixuan Tang, Liangxiao Ma, Hong Li

“…However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. …”
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Language models learn to represent antigenic properties of human influenza A(H3) virus by Francesco Durazzi, Marion P. G. Koopmans, Ron A. M. Fouchier, Daniel Remondini

“…Methods based on deep learning language models (BiLSTM and ProtBERT) and more classical approaches based solely on genetic distances and physicochemical properties of amino acid sequences had comparable performances over the coarser features of the map, but the first two performed better over fine-grained features like single amino acid-driven antigenic change and in silico deep mutational scanning experiments to rank the substitutions with the largest impact on antigenic properties. …”
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Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors by Abdulrahman Alkurdi, Jean Clore, Richard Sowers, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez

“…This indicated a proportional decline in performance across both feature-based and end-to-end models as noise levels increased, challenging initial assumptions about model resilience. …”
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Automated detection of Parkinson’s disease using improved linknet-ghostnet model based on handwriting images by P Pradeep, J Kamalakannan

“…Then, modified PHOG, Deep features and Shape features are extracted. Finally, detection is performed using hybrid Improved LinkNet and Ghostnet models, termed (ILN-GNet), whose outcomes indicate if the individual is healthy or affected. …”
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A Novel Inflammation-Marker-Based Prognostic Model for Advanced Pulmonary Lymphoepithelioma-Like Carcinoma by Chen X, Liu T, Mo S, Yang Y, Chen X, Hong S, Zhou T, Chen G, Zhang Y, Ma Y, Ma Y, Zhang L, Zhao Y

“…Xueyuan Chen,1,&ast; Tingting Liu,1,&ast; Silang Mo,1,&ast; Yuwen Yang,1 Xiang Chen,1 Shaodong Hong,1 Ting Zhou,1 Gang Chen,1 Yaxiong Zhang,1 Yuxiang Ma,2 Yuanzheng Ma,1 Li Zhang,1 Yuanyuan Zhao1 1Medical Oncology Department, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Clinical Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li Zhang; Yuanyuan Zhao, Sun Yat-sen University Cancer Center, 651 Dong Feng Road East, Guangzhou, 510060, People’s Republic of China, Email zhangli@sysucc.org.cn; zhaoyy@sysucc.org.cnPurpose: This study aimed to investigate the prognostic value of inflammation markers for advanced pulmonary lymphoepithelioma-like carcinoma (PLELC) and develop an effective prognostic model based on inflammation markers to predict the overall survival (OS) of this population.Methods: Cox regression analysis was performed on 18 clinical and inflammation features, and a nomogram was created to predict overall survival (OS). …”
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