Online Persian/Arabic Writer Identification using Gated Recurrent Unit Neural Networks by Mahsa Aliakbarzadeh, Farbod Razzazi

“…Conventional methods in writer identification mostly rely on hand-crafted features to represent the characteristics of different handwritten scripts. …”
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome by Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán

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Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification by Xuanxin Liu, Fu Xu, Yu Sun, Haiyan Zhang, Zhibo Chen

“…To tolerate the significant intraclass variances, the convolutional recurrent neural networks (C-RNNs) are proposed for observation-centered plant identification to mimic human behaviors. …”
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Drilling Condition Identification Method for Imbalanced Datasets by Yibing Yu, Huilin Yang, Fengjia Peng, Xi Wang

“…To address the challenges posed by class imbalance and temporal dependency in drilling condition data and enhance the accuracy of condition identification, this study proposes an integrated method combining feature engineering, data resampling, and deep learning model optimization. …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…First, particle swarm optimization (PSO) identifies and optimizes critical functions and reduces feature dimensionality. Thereafter, long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) architectures are used in emotion identification. …”
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Corrosion type identification in flanged joints using recurrent neural networks on electrochemical noise measurements by Soroosh Hakimian, Abdel-Hakim Bouzid, Lucas A. Hof

“…Electrochemical noise (EN) measurements can detect such corrosion, yet processing EN data is time-consuming and requires expertise. This study applies recurrent neural networks (RNNs) to automate corrosion type identification on flange surfaces using raw EN signals from spontaneous electrochemical reactions. …”
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Gas Leak Detection and Leakage Rate Identification in Underground Utility Tunnels Using a Convolutional Recurrent Neural Network by Ziyang Jiang, Canghai Zhang, Zhao Xu, Wenbin Song

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Risk Factors and Vascular Features Associated With Local Recurrence in Pancreatic Cancer Post‐Pancreaticoduodenectomy: A Retrospective Cohort Study by Ting‐Kai Liao, Ying Jui Chao, Wei‐Hsun Lu, Ping‐Jui Su, Chih‐Jung Wang, Yan‐Shen Shan

“…This study highlights potential risk factors, recurrence patterns, and associated vascular features for early identification. …”
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Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context by Shannon Doyle, Esther H. Lips, Eric Marcus, Lennart Mulder, Yat-Hee Liu, Francesco Dal Canton, Timo Kootstra, Maartje M. van Seijen, Ihssane Bouybayoune, Elinor J. Sawyer, Alastair M. Thompson, Sarah E. Pinder, Clara I. Sánchez, Jonas Teuwen, Jelle Wesseling, Jelle Wesseling, Jos Jonkers, Jacco van Rheenen, Esther H. Lips, Marjanka Schmidt, Lodewyk F.A. Wessels, Proteeti Bhattacharjee, Alastair Thompson, Serena Nik-Zainal, Helen Davies, Elinor J. Sawyer, Andrew Futreal, Nicholas Navin, E. Shelley Hwang, Fariba Behbod, Daniel Rea, Hilary Stobart, Deborah Collyar, Donna Pinto, Ellen Verschuur, Marja van Oirsouw

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Identification of dominant instability modes in power systems based on spatial‐temporal feature mining and TSOA optimization by Miao Yu, Jianqun Sun, Shuoshuo Tian, Shouzhi Zhang, Jingjing Wei, Yixiao Wu

“…Firstly, spatio‐temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. …”
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Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algor... by Qi Li, Hengchao Liu, Yubo Ma, Zhenqi Tang, Chen Chen, Dong Zhang, Zhimin Geng

“…ObjectiveIt is vital to enhance the identification of early recurrence in intrahepatic cholangiocarcinoma (ICC) patients after curative-intent resection and to determine which patients could benefit from adjuvant chemotherapy (ACT). …”
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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

“…The LRCT network effectively leverages the temporal learning advantages of Local Recurrent Networks (Local RNN) and the spatial learning strengths of Convolutional Neural Network (CNN) by designing the local feature extraction block (denoted as LRC Block), which extracts fine-grained local features from 2000-dimensional original sequences and reduces the dimensionality to 125. …”
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Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer by Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao

“…Simulated images were generated as follows: [A] a ResNet model was trained to identify recurrent cervix cancer; [B] a model was evaluated on T2W MRI data for subjects to obtain corresponding feature maps; [C] most important feature maps were determined for each image; [D] feature maps were combined across all images to generate a simulated image; [E] the final image was reviewed by a radiation oncologist and an initial algorithm to identify the likelihood of recurrence. …”
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A siamese neural network model for phase identification in distribution networks by Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara

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A multi-scale temporal feature fusion framework for sheep voiceprint recognition by Xipeng Wang, Delong Wang, Weijiao Dai, Cheng Zhang, Yudongchen Liang, Yong Zhou, Juan Yao, Fang Tian

“…The model uses the feature pyramid network (FPN) structure and a one-dimensional convolutional block attention module (1D-CBAM) for feature fusion to enhance the classification ability of the model. …”
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Application of attention mechanism-based LSTM neural network in stratigraphy identification by Cunde Jia, Xiangdong Kong, Minghui Wang, Zhuowei Yu, Haipeng Li, Yunhong Jiang, Jianliang Hu, Chao Ai

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BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention by Watshara Shoombuatong, Pakpoom Mookdarsanit, Nalini Schaduangrat, Lawankorn Mookdarsanit

“…Therefore, this study proposes an innovative deep learning-based hybrid framework (termed BGATT-GR) that leverages a data augmentation method, a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (ATT) to attain more accurate identification of GR antagonists. …”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

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Propaganda as a Category in Forensic Text Analysis by Anatolii N. Baranov, Pavel B. Parshin

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