Almost Periodic Functions on the Quantum Time Scale and Applications by Yongkun Li

“…Finally, as an application, we propose a class of high-order Hopfield neural networks on the quantum time scale and establish the existence and global exponential stability of almost periodic solutions of this class of neural networks.…”
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Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods by Hongqing Song, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, Qipeng Liu

“…Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. …”
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The classification of concentration of mixture of analytes using total principal component regression by Romas Baronas, Feliksas Ivanauskas, Robertas Paulauskas, Pranas Vaitkus

“…The results are compared with the results obtained using artificial neural networks. …”
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Geometrical shape learning as basis for compact microstructure representations and microstructure-properties linkages by Rodrigo Iza Teran, Daniela Steffes-lai, Lukas Morand

“…For this purpose, typically, state-of-the-art feature extraction methods, such as principal component analysis, are used in combination with regression models, such as neural networks. For complex spatially-resolved microstructure representations convolutional neural networks are often used, which are, however, very data-intensive and not explainable. …”
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Physiology-informed regularisation enables training of universal differential equation systems for biological applications. by Max de Rooij, Balázs Erdős, Natal A W van Riel, Shauna D O'Donovan

“…The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. …”
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Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). …”
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Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm by Hanqing Liu, Xiaojun Li, Jin Wei, Xiaodong Kang

“…In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. …”
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A Joint Deep Recommendation Framework for Location-Based Social Networks by Omer Tal, Yang Liu

“…One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks. …”
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Autoregressive neural quantum states of Fermi Hubbard models by Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G. Melko, Richard T. Scalettar, Juan Carrasquilla, Annabelle Bohrdt, Ehsan Khatami

“…Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. …”
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Fuel Cell Output Current Prediction with a Hybrid Intelligent System by José-Luis Casteleiro-Roca, Antonio Javier Barragán, Francisca Segura, José Luis Calvo-Rolle, José Manuel Andújar

“…This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. …”
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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects by Isaac Kofi Nti, Juanita Ahia Quarcoo, Justice Aning, Godfred Kusi Fosu

“…The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. …”
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The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). by DanielRaj K, Ponseka G, Bharath Sanjai Lordwin D J3

“…This field seeks to bridge the gap between traditional computing architectures and biological neural networks by replicating brain-like functionalities. …”
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Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples by Yuanxin Tong, Hongxia Luo, Zili Qin, Hua Xia, Xinyao Zhou

“…To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). …”
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Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data by Jaqueline Alvarenga Silveira, Alexandre Ray da Silva, Mariana Zuliani Theodoro de Lima

“…Support Vector Machine (SVM) and Neural Networks, especially when applied to combined data, demonstrate strong potential in improving prediction accuracy. …”
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A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach by Ghulam Abbas, Wu Zhi, Aamir Ali

“…Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.…”
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The Construction of Sports Health Management Model Based on Deep Learning by Junniao Meng, Song Wang

“…This article first introduces related concepts such as deep learning and convolutional neural networks and then conducts an experimental exploration on the combination of convolutional neural networks and multilayer perceptrons. …”
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Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning by Yuling Wang, Vijay Vittal

“…Each agent has independent actor neural networks to output generator control commands and critic neural networks that evaluate command performance. …”
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Improving ocean reanalyses of observationally sparse regions with transfer learning by Simon Lentz, Sebastian Brune, Christopher Kadow, Johanna Baehr

“…We investigate the applicability of transfer learning based neural networks to reconstruct North Atlantic temperatures in times with sparse observations. …”
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A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction by Bodong Zhou, Jiahui Liu, Songyi Cui, Yaping Zhao

“…Specifically, we utilize Convolutional Neural Networks (CNNs) for spatial information processing and a combination of Recurrent Neural Networks (RNNs) for final spatio-temporal traffic prediction. …”
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