Ship motion identification model based on enhanced Bi-LSTM by Haozhe ZHANG, Zhibo YANG, Xuguo JIAO, Chengxing LÜ, Peng LEI

“…Finally, using the navigation data of KLVCC2 ships, the prediction effects of the enhanced Bi-LSTM model are compared with those of the Support Vector Machine (SVM), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) models.ResultsThe Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indicators of the enhanced Bi-LSTM model in the test set are lower than 0.015 and 0.011 respectively, and the coefficient of determination（R2）is higher than 0.99913, demonstrating prediction accuracy significantly higher than that of the SVM, GRU, and LSTM models.ConclusionThe proposed enhanced Bi-model has excellent generalization performance and excellent prediction stability and precision, and effectively realizes ship motion identification.…”
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An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer by Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen

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Recurrent neural network-based automated early detection of pandemic-prone diseases through symptoms analysis by Aditika Tungal, Kuldeep Singh, Prabhsimran Singh, Ateeq Ur Rehman, Sandeep Sood, Vishnu Kant, Anand Kumar, Seada Hussen, Habib Hamam

“…Therefore, the current study employs the proposed architecture of a recurrent neural network (RNN) for the accurate identification of patients infected with COVID-19 disease through analysis of its major symptoms. …”
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Recurrence prediction in chronic subdural hematomas: a risk stratification score based on 118 consecutive patients by Francesco M.C. Lioi, Jon Ramm-Pettersen, Andrea Fratini, Gabriele Dentato, Giovanni Facchinetti, Niccolo Colella, Luigi Rosito, Elena Furno, Camilla Riva, Andrea G. Ruggeri, Luca D'Angelo, Antonio Santoro, Alessandro Frati

“…This model enables personalized postoperative management, facilitating early identification of high-risk patients who may benefit from adjunctive treatments to reduce recurrence.…”
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Dynamics of recurrent neural networks with piecewise linear activation function in the context-dependent decision-making task by Kononov, Roman Andreevich, Maslennikov, O.  V., Nekorkin, Vladimir Isaakovich

“…This paper aims to elucidate the dynamic mechanism underlying context-dependent two-alternative decision-making task solved by recurrent neural networks through reinforcement learning. …”
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Incidence and risk factors for recurrent membranous nephropathy after kidney transplantation: a systematic review and meta-analysis by Jiang Bai, Zhifang Zheng, Jiajing Cao, Linghui Ji, Junchi Zhang, Yanan Yang, Qiang Guo

“…Background The risk factors for membranous nephropathy (MN) following kidney transplantation remain unclear, mainly attributed to the constrained identification of predictive clinical presentation features. …”
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3D tooth identification for forensic dentistry using deep learning by Hamza Mouncif, Amine Kassimi, Thierry Bertin Gardelle, Hamid Tairi, Jamal Riffi

“…Our proposed approach addresses these issues with a novel method that extracts critical representative features from 3D tooth models and transforms them into a 2D image format suitable for detailed analysis. …”
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Identification of Prognostic Genes in Neuroblastoma in Children by Weighted Gene Coexpression Network Analysis by Jun Yang, Ying Zhang, Jiaying Zhou, Shaohua Wang

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Intelligent Identification and Prediction of Roof Deterioration Areas Based on Measurements While Drilling by Jing Wu, Zhi-Qiang Zhao, Xiao-He Wang, Yi-Qing Wang, Xiao-Xiang Wei, Zhi-Qiang You

“…Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. …”
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Deep neural networks and fractional grey lag Goose optimization for music genre identification by Yuanye Tian

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The phenotypes of sickle cell disease: strategies to aid the identification of undiagnosed patients in the Italian landscape by Maddalena Casale, Silvia Benemei, Cristiano Gallucci, Giovanna Graziadei, Giovanni Battista Ferrero

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A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM by Xin He, Meipeng Zhong, Chengcheng He, Jinhao Wu, Haiyang Yang, Zhigao Zhao, Wei Yang, Cong Jing, Yanlin Li, Chen Gao

“…Experiments involving milling tool wear identification demonstrate that the proposed method significantly outperforms support vector regression (SVR) and recurrent neural network (RNN)-based methods, when a small amount of labeled samples and abundant unlabeled samples are available. …”
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Real-World Observational Study of Incidence and Outcomes in an HR+/HER2– Early Breast Cancer Population with High-Risk of Recurrence in Finland by Ravinder Singh, Samuli Tuominen, Mariann I. Lassenius, Merja Auvinen, Astrid Torstensson, Tom Wiklund

“…Abstract Introduction Real-world data on patients with early breast cancer (EBC) with high-risk features remains limited. This population-based study determined the incidence, outcomes and characteristics of patients with hormone receptor (HR)-positive, human epidermal growth factor 2 receptor (HER2)-negative EBC with high-risk features treated in everyday clinical care in two Finnish hospital districts which represent approximately 40% (2.5 million) of the total Finnish population (5.5 million). …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Feature Derivation Module leverages dilated convolutions, gated recurrent units, and attention mechanisms to construct new features in batches. …”
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Isolation and identification of patient-derived liver cancer stem cells and development of personalized treatment strategies by Tingting Guo, Shuai Zhang, Weiping Zeng, Yan Liang, Jinghe Xie, ShouPei Liu, Yaqi Qiu, Yingjie Fu, Yimeng Ou, Keqiang Ma, Bailin Wang, Weili Gu, Yuyou Duan

“…Abstract Background Liver cancer stem cells (LCSCs) are thought to drive the metastasis and recurrence, however, the heterogeneity of molecular markers of LCSCs has hindered the development of effective methods to isolate them. …”
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Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies by Youcef ATMANI, Ammar Mesloub, Said Rechak

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Hardware realization of neuromorphic computing with a 4–port photonic reservoir for modulation format identification by Enes Şeker, Rijil Thomas, Guillermo von Hünefeld, Stephan Suckow, Mahdi Kaveh, Gregor Ronniger, Pooyan Safari, Isaac Sackey, David Stahl, Colja Schubert, Johannes Karl Fischer, Ronald Freund, Max C Lemme

“…Reservoir computing encompasses recurrent neural networks for sequential data processing and matches the performance of other recurrent networks with less training and lower costs. …”
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Key method of digitization of power distribution panel with artificial intelligence identification for power communication network by LIU Lin, YANG Zhimin, HUANG Qiang, YANG Jingwei, CHEN Yitong

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Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data. by Xu Shi, Xiao Wang, Lu Jin, Leena Halakivi-Clarke, Robert Clarke, Andrew F Neuwald, Jianhua Xuan

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