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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Vehicle classification algorithm based on binary proximity sensors and neural networks by ZHANG Wei, TAN Guo-zhen, DING Nan, SHANG Yao

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Inception neural network for human activity recognition using wearable sensor by Duo CHAI, Cheng XU, Jie HE, Shao-yang ZHANG, Shi-hong DUAN, Yue QI

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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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Meta-path convolution based heterogeneous graph neural network algorithm by QIN Zhilong, DENG Kun, LIU Xingyan

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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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Research on a complaint prediction model utilizing joint neural networks by Xiaoliang MA, Ying LIU, Jie GAO

“…By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and their combined modeling methods.The Logistic regression model identified five major influencing factors, predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors, achieving a prediction accuracy of 90.6%.On this basis, a combined model was constructed with an accuracy rate of up to 92.8%.After practical application in a provincial telecom operator, the repeat complaint rate decreased by 3.2%, demonstrating a significant impact.Strong support is provided for improving the service quality of telecom operators and reducing repeat complaints, which is of great significance for the development of the telecom industry in China.…”
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A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation by Karn N. Watcharasupat, Chih-Wei Wu, Yiwei Ding, Iroro Orife, Aaron J. Hipple, Phillip A. Williams, Scott Kramer, Alexander Lerch, William Wolcott

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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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An intrusion detection model based on convolution neural network for Internet of vehicles by ZHANG Rui

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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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Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks by Jeyson A. Castillo, Yenny C. Granados, Carlos Augusto Fajardo Ariza

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Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients. by Mikkel Bonde, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, Martin Sillesen

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Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks by Kang Xie, Yixian Yang, Ling Zhang, Maohua Jing, Yang Xin, Zhongxian Li

“…In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. …”
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ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK by ZHANG LiZhi, XU WeiXiao, JING LuYang, TAN JiWen

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Secure UAV-Based System to Detect Small Boats Using Neural Networks by Moisés Lodeiro-Santiago, Pino Caballero-Gil, Ricardo Aguasca-Colomo, Cándido Caballero-Gil

“…The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. …”
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