A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…By leveraging both the feature extraction strengths of Convolutional layers, which capture spatial hierarchies from input, and the ability of Gated Recurrent Unit (GRU) layers to learn long-term dependencies, the proposed CGRU model can capture both spatial and temporal features of atmospheric corrosion data within time-series signals, resulting in precise predictions. …”
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Gated attention based generative adversarial networks for imbalanced credit card fraud detection by Jiangmeng Ge, Lanxiang Yin, Shiqing Zhang, Xiaoming Zhao

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Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates by João Neto, Abhishek Singh Dahiya, Ravinder Dahiya

“…Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. …”
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CAG-MoE: Multimodal Emotion Recognition with Cross-Attention Gated Mixture of Experts by Axel Gedeon Mengara Mengara, Yeon-kug Moon

“…For example, physiological signals, audio features, and textual data capture complementary yet distinct aspects of emotion, requiring specialized processing to extract meaningful cues. …”
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24 Days-Stable CNOT Gate on Fluxonium Qubits with Over 99.9% Fidelity by Wei-Ju Lin, Hyunheung Cho, Yinqi Chen, Maxim G. Vavilov, Chen Wang, Vladimir E. Manucharyan

“…Compared with the 99.96% fidelity of a 60-ns identity gate, our data brings the investigation of the nondecoherence-related errors during logical operations down to 2×10^{−4}. …”
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Stock movement prediction with multimodal stable fusion via gated cross-attention mechanism by Chang Zong, Jian Wan, Lucia Cascone, Hang Zhou

“…The MSGCA framework consists of three integral components: (1) a trimodal encoding module, responsible for processing indicator sequences, dynamic documents, and a relational graph, and standardizing their feature representations; (2) a cross-feature fusion module, where primary and consistent features guide the multimodal fusion of the three modalities via a pair of gated cross-attention networks; and (3) a prediction module, which refines the fused features through temporal and dimensional reduction to execute precise movement forecasting. …”
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HGTFM: Hierarchical Gating-Driven Transformer Fusion Model for Robust Multimodal Sentiment Analysis by Chengcheng Yang, Zhiyao Liang, Dashun Yan, Zeng Hu, Ting Wu

“…This paper presents a Hierarchical Gating-Driven Transformer Fusion Model (HGTFM), which effectively achieves multimodal data fusion through an advanced transformer architecture and a Hierarchical Gated Fusion Module (HGFM). …”
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Fault Prediction of Bearing Based on Dual Dimensional Perception and Composite Gated Recurrent Network by Wang Weiping, Xue Shibei

“…In the trend dimension, 7 preprocessed vibration waveform feature quantities were proposed from 33 feature data as the feature data for the trend dimension. …”
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Multi-task genomic prediction using gated residual variable selection neural networks by Yuhua Fan, Patrik Waldmann

“…Conclusion The suggested GRVSNN framework provides a novel and computationally effective approach to improve genomic prediction accuracy by integrating information from traditional pedigrees with genomic data. The model’s ability to conduct multi-task predictions underscores its potential to enhance selection processes in agricultural species and predict multiple diseases in precision medicine.…”
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Experimental Study of Dike‐Break Induced Flow Generated by Instantaneous Opening of the Side Gate by Dawei Zhang, Zhongxiang Wang, Huiwen Liu, Wuxia Bi

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Iono–Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion‐Gating by Wataru Namiki, Daiki Nishioka, Yuki Nomura, Takashi Tsuchiya, Kazuo Yamamoto, Kazuya Terabe

“…Although nonlinear interfered spin‐wave multi‐detection exhibits high nonlinearity and the ability to map in high dimensional feature space, it does not have sufficient performance to process time‐series data precisely. Herein, development of an iono–magnonic reservoir by combining such interfered spin wave multi‐detection and ion‐gating involving protonation‐induced redox reaction triggered by the application of voltage is reported. …”
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Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation by Huimei Duan, Chenggang Guo, Yuan Ou

“…Unlike the conventional ConvGRUs, our CAGRU abandons the conventional practice of using convolutional layers for all the gates and innovatively designs the coordinate attention as an attention gate and combines it with the convolutional gate. …”
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Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach by Suhas Kamshetty Chinnababu, Suhas Kamshetty Chinnababu, Suhas Kamshetty Chinnababu, Ananda Babu Jayachandra, Ananda Babu Jayachandra, Swathi Holalu Yogesh, Swathi Holalu Yogesh, Mohamed Abouhawwash, Mohamed Abouhawwash, Doaa Sami Khafaga, Eman Abdullah Aldakheel, Vinaykumar Vajjanakurike Nagaraju, Vinaykumar Vajjanakurike Nagaraju

“…However, it is a challenging task for ML-based algorithms to capture the long-term dependencies like glucose levels in the diabetes data. Hence, this research developed the skip-gated recurrent unit (Skip-GRU) with gradient clipping (GC) approach which is a deep learning (DL)-based approach to predict diabetes effectively. …”
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Utilizing rat kidney gene co-expression networks to enhance safety assessment biomarker identification and human translation by Steven J. Kunnen, Giulia Callegaro, Jeffrey J. Sutherland, Panuwat Trairatphisan, Hugo W. van Kessel, Lukas S. Wijaya, Git Chung, Keith Pye, Keith M. Goldstein, Claire R. Teague, Ciaran P. Fisher, Julio Saez-Rodriguez, Colin Brown, Susan A. Elmore, Kathleen M. Heinz-Taheny, James L. Stevens, Bob van de Water

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SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS by Muhammad Nazhif Abda Putera Khano, Dewi Retno Sari Saputro, Sutanto Sutanto, Antoni Wibowo

“…Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. …”
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Design and Optimization of Optical NAND and NOR Gates Using Photonic Crystals and the ML-FOLD Algorithm by Alireza Mohammadi, Fariborz Parandin, Pouya Karami, Saeed Olyaee

“…To optimize the phase parameters efficiently, we employ the ML-FOLD (Meta-Learning and Formula Optimization for Logic Design) optimization formula, which outperforms traditional methods and machine learning approaches in terms of computational efficiency and data requirements. Through finite-difference time-domain (FDTD) simulations, the proposed optical structure demonstrates successful implementation of NAND and NOR gate logic, achieving high contrast ratios of 4.2 dB and 4.8 dB, respectively. …”
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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

“…It provides an intuitive way for humans to interact with machines emotionally by understanding the emotion machine. The process begins with collecting and preprocessing EEG information to use the data for training and testing the proposed system. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…Based on features, the sorted-out data gets evaluated through a GRU-LSTM (Gated Recurrent Unit - Long Short-Term Memory) network to identify the state of the infant as usual and suggestive of hypoglycemia—blood glucose below 70 mg/dL, pale complexion, profuse perspiration. …”
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Classification of Review Text using Hybrid Convolutional Neural Network and Gated Recurrent Unit Methods by Fiqih Fathor Rachim, Auli Damayanti, Edi Winarko

“…Optimal weight is used for validation test on test data. The implementation of review text classification using the hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) method was made using the python programming language. …”
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Graph Isomorphism and Hybrid-Order Residual Gated Graph Neural Network for Session-Based Recommendation by WANG Yonggui, YU Qi

“…Secondly, hybrid-order gated graph neural network is used to process the position embedding to capture the user intention reflected by the user􀆳s re-interaction after a long time, and the residual module is added to solve the degradation problem of deep network. …”
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