Preoperative psychological health impacts pain and disability outcomes following anterior cervical discectomy and fusion for cervical radiculopathy by Erin E. Cunningham, Erin Bigney, Stephan U. Dombrowski, Niels Wedderkopp, Neil Manson, Najmeddan Attabib, Edward Abraham, Chris Small, Eden Richardson, Michael M. H. Yang, Eric Crawford, Michael H. Weber, Jerome Paquet, Sean D. Christie, Raphaele Charest-Morin, Bernard LaRue, Andrew Nataraj, Hamilton Hall, Y. Raja Rampersaud, Charles Fisher, Nicolas Dea, Chris Bailey, Jeffrey J. Hébert

“…This retrospective cohort study included data from patients enrolled in the Canadian Spine Outcomes and Research Network who underwent anterior cervical discectomy and fusion for radiculopathy. …”
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Overview of object detection methods based on LiDAR point cloud under adverse weather conditions by Yutian WU, Qing LI, Wenwei SUN, Heng WANG, Weiran WANG

“…Advanced LiDAR-based object detection methods leverage deep learning techniques to extract object features, predict object locations, and classify objects within an end-to-end neural network framework, achieving remarkable accuracy in many general scenarios. …”
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Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation by You Wu, Li Xu, Xiaohong Cong, Hanxiao Li, Yanli Li

“…The masking mechanism ensures the preservation of complete cellular information, while convolution and attention mechanisms are employed to capture both global and local features. Residual networks augment feature representation and effectively mitigate the risk of model overfitting. …”
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A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models by L. Godlin Atlas, Daniel Arockiam, Arvindhan Muthusamy, Balamurugan Balusamy, Shitharth Selvarajan, Taher Al-Shehari, Nasser A. Alsadhan

“…The input reviews are preprocessed using natural language processing techniques like tokenization, lemmatization, stop word removal, named entity recognition and part of speech tagging. Feature extraction is done using bidirectional gated recurrent unit shortly called as BiGRU feature extractor and the sentiments are classified into three polarities such as positive, negative and neutral using a hybrid recurrent neural network based long short-term memory classifier. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Although porescale imaging and network modeling techniques can compute capillary pressure from micro-CT rock images (Øren and Bakke, 2003; Valvatne and Blunt, 2004), these approaches are time-consuming, limited to small sample volumes, and not yet practical for routine reservoir evaluation. …”
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Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure by Jiuyi Wang, Qingxia Kang, Shiqi Tian, Shunli Zhang, Kai Wang, Guibo Feng

“…Four predictive models were developed and compared: Cox proportional hazards, random survival forest (RSF), Cox proportional hazards deep neural network (DeepSurv), and eXtreme Gradient Boosting (XGBoost). …”
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A Machine Learning Approach for the Clustering and Classification of Geothermal Reservoirs in the Ying-Qiong Basin by Yujing Duan, Yuan Liang, Qingyun Ji, Zhong Wang

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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Subsequently, radiomic features were extracted using pyradiomics tool, while deep learning features from each cross-section were derived using the Inception_v3 neural network. …”
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Research on Park Perception and Understanding Methods Based on Multimodal Text–Image Data and Bidirectional Attention Mechanism by Kangen Chen, Xiuhong Lin, Tao Xia, Rushan Bai

“…Experimental results show that compared to traditional methods such as residual network (ResNet), recurrent neural network (RNN), and long short-term memory (LSTM), the proposed model achieves significant advantages across multiple evaluation metrics, including mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R²). …”
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A bearing fault diagnosis method based on hybrid artificial intelligence models. by Lijie Sun, Xin Tao, Yanping Lu

“…Addressing the issue of that the difficulty of incipient weak signals feature extraction influences the rolling bearing diagnosis accuracy, an efficient bearing fault diagnostic technique, a proposition is forwarded for hybrid artificial intelligence models, which integrates Improved Harris Hawks Optimization (IHHO) into the optimization of Deep Belief Networks and Extreme Learning Machines (DBN-ELM). …”
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Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning by Xin Lei, Xing Wang, Guanlin Chen, Ce Liang, Quhuan Li, Huaiguang Jiang, Wei Xiong

“…This model proficiently assimilates and utilizes inherent biological features from natural promoter sequences to engineer synthetic variants. …”
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An online innovation platform to promote collaboration and sustainability in short food supply chains by Foteini Chrysanthopoulou, Marieke Lameris, Gunter Greil, Dusan Vudragovic, Katherine Flynn

“…However, not all SFSC stakeholders network with others in this way, and it is not clear what will draw them to ICT interaction. …”
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UCSwin‐UNet model for medical image segmentation based on cardiac haemangioma by Jian‐Ting Shi, Gui‐Xu Qu, Zhi‐Jun Li

“…Abstract Cardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. This study proposes a novel segmentation method based on a Convolutional Neural Network (CNN) and Transformer integration, with Swin‐UNet as the core model. …”
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Lightweight terminal cross-domain authentication protocol in edge computing environment by Hongying ZHU, Xinyou ZHANG, Huanlai XING, Li FENG

“…Edge computing has gained widespread usage in intelligent applications due to its benefits, including low latency, high bandwidth, and cost-effectiveness.However, it also faces many security challenges due to its distributed, real-time, multi-source and heterogeneous data characteristics.Identity authentication serves as the initial step for terminal to access to the network and acts as the first line of defense for edge computing.To address the security issues in the edge computing environment, a terminal cross-domain authentication protocol suitable for the edge computing environment was proposed based on the ＆quot;cloud-edge-end＆quot; three-level network authentication architecture.Access authentication was implemented between terminals and local edge nodes based on the SM9 algorithm, and session keys were negotiated.The secret key was combined with symmetric encryption technology and hash function to achieve cross-domain authentication for the terminal.The pseudonym mechanism was used in the authentication process to protect the privacy of end users.The terminal only needs to register once, and it can roam randomly between different security domains.BAN logic was used to prove the correctness of the protocol and analyze its security.The results show that this protocol is capable of resisting common attacks in IoT scenarios, and it features characteristics such as single sign-on and user anonymity.The performance of the cross-domain authentication protocol was evaluated based on computational and communication costs, and compared with existing schemes.The experimental results show that this protocol outperforms other schemes in terms of computational and communication costs, making it suitable for resource-constrained terminal devices.Overall, the proposed protocol offers lightweight and secure identity authentication within edge computing environments.…”
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LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation. by Ming Li, Jingang Ma, Jing Zhao

“…This network adopts a large-kernel depthwise convolution attention mechanism to simulate the self-attention mechanism of Transformers. …”
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Analysis of Single-Pilot Intention Modeling in Commercial Aviation by Lei Dong, Hongbing Chen, Changxiao Zhao, Peng Wang

“…The deep information in the feature vector of a single-pilot operation item is captured by the BiLSTM network, and the neural weight is adaptively assigned by the training mechanism. …”
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A man–vehicle e-passport system using biometric blockchain for automated border control by Bing Xu, Ahmed Bouridane, Qiang Ni, Richard Jiang

“…Since the mid-1990s, the evolution of internet technologies has significantly transformed global connectivity and digital interaction. …”
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Multimodal rapid identification of growth stages and discrimination of growth status for Morchella by Ning Jia, Chunjun Zheng

“…In the multimodal Morchella growth state discrimination method, text and image modalities are integrated, a Non downsampled Contourlet Transform Mask Region based Convolutional Neural Network (NSCT Mask R-CNN) model is designed, and a multimodal feature extraction strategy combining Non downsampled Contourlet Transform (NSCT) features with environmental features is explored. …”
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Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. by Jamil Ahmad, Khan Muhammad, Sung Wook Baik

“…To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. …”
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Digital Assistant for Medical Management by Maria-Dana-Ștefania CÂMPAN

“…MedLife, on the other hand, covers a broader network of medical institutions and provides access to test results and consultation scheduling. …”
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