Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing by Peishan Dai, Hanwei Sheng, Jianmei Zhang, Ling Li, Jing Wu, Min Fan

“…First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. …”
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Document-Level Neural TTS Using Curriculum Learning and Attention Masking by Sung-Woong Hwang, Joon-Hyuk Chang

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Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network by Liyun Su, Xiu Ling

“…Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. …”
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Kodama-Schwarzschild versus Gaussian Normal Coordinates Picture of Thin Shells by Mikhail Z. Iofa

“…Geometry of the spacetime with a spherical shell embedded in it is studied in two coordinate systems: Kodama-Schwarzschild coordinates and Gaussian normal coordinates. We find explicit coordinate transformation between the Kodama-Schwarzschild and Gaussian normal coordinate systems. …”
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Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting by S. V. Sholtanyuk

Subjects: “…neural network…”
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Corrigendum to “Wnt5a Signaling in Normal and Cancer Stem Cells” by Yan Zhou, Thomas J. Kipps, Suping Zhang

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Comparison of artificial neural network models of categorized daily electric load by Vildan Evren, İlker Ali Ozkan

Subjects: “…artificial neural network…”
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Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing by Jianyi Li, Qingfeng Liu, Liying Tan, Jing Ma, Nanxing Chen

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Exponential synchronization of neural networks with mixed delays under impulsive control by Wanshun Zhao, Kelin Li, Yanchao Shi

Subjects: “…neural networks…”
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Emergence of Prediction by Reinforcement Learning Using a Recurrent Neural Network by Kenta Goto, Katsunari Shibata

“…It is suggested that through reinforcement learning using a recurrent neural network, both emerge purposively and simultaneously without testing individually whether or not each piece of information is predictable. …”
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Study on Inlet and Engine Integrated Model with Normal Shock Position Feedback by Haoying Chen, Haibo Zhang, Zhihua Xi, Qiangang Zheng

“…In order to consider the inlet and engine integrated model of supersonic airliner, the dynamic identification and control of inlet normal shock are studied. The research is based on the bleed air flow rate under supersonic conditions. …”
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Diagnostic study of fungal and bacterial pathogens that cause vaginal infection in women by Heba Hadid Rashed, Ibtihal Muiz Al-Hussaini, Ali H. Al-Marzoqi

“…Vaginal candidiasis is a fungal infection that affects the vagina and the vulva, the tissues at the vaginal opening, causing irritation, discharge, and severe itching. …”
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An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network by Tianye Xing, Yidan Wang, Yingxue Liu, Qi Wu, Rong Ma, Xiaoling Shang

“…This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. …”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

“…Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. …”
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PDE-Based Physics Guided Neural Network for SAR Image Segmentation by Rachana Rao, B. Roja Reddy, M. Uttara Kumari

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Synchronization of Neural Networks with Mixed Time Delays under Information Constraints by Dedong Yang, He-Xu Sun, Peng Yang, Tai-Hang Du

“…This paper investigates the synchronization problem of neural networks with mixed time delays under information constrains. …”
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['ANALYSIS OF COVID-19 DATA USING ARIMA-NEURAL ARTIFICIAL INTELLIGENCE'] by Muhammad Ilyas, Shaheen Abbas

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Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network by Lihui Tang, Junjian Li, Wenming Lu, Peiqing Lian, Hao Wang, Hanqiao Jiang, Fulong Wang, Hongge Jia

“…This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. …”
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Accurate Recognition of Motion Patterns Based on Artificial Visual Neural Network by Meiqi Li

“…In order to improve the accuracy of motion pattern recognition, this paper combines the artificial visual neural network to construct a motion pattern recognition system. …”
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Designing neural networks for modeling biological data: A statistical perspective by Michele La Rocca, Cira Perna

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