Tracking Control of Wave-Adaptive Modular Unmanned Surface Vehicle Using Time-Delay Zeroing Neural Network by Pengfei Guo, Wenyue Zhang, Zheng Li, Zheng Zheng

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Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach by Saeid Jorkesh, Ryan Ahmed, Saeid Habibi, Reza Hosseininejad, Siyuan Xu

“…This paper investigates various state of charge (SOC) and state of health (SOH) estimation methods, presenting a novel hybrid neural network that combines Gate Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models. …”
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A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks by Ali Basem, Serikzhan Opakhai, Zakaria Mohamed Salem Elbarbary, Farruh Atamurotov, Natei Ermias Benti

“…A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. …”
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A Methodology for Calculating Greenhouse Effect of Aircraft Cruise Using Genetic Algorithm-Optimized Wavelet Neural Network by Yong Tian, Lina Ma, Songtao Yang, Qian Wang

“…With respect to both cruise strategies and wind factors, a genetic algorithm-optimized wavelet neural network topology is designed to model the fuel flow-rate and developed using the real flight records data. …”
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Monte Carlo Noise Reduction Algorithm Based on Deep Neural Network in Efficient Indoor Scene Rendering System by Xiwen Chen, Jianfei Shen

“…For this problem, we propose a Monte Carlo noise reduction algorithm based on deep neural networks and apply it to the efficient rendering of an indoor scene. …”
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Utilizing Artificial neural networks (ANN) to regulate Smart cities for sustainable Urban Development and Safeguarding Citizen rights by Zhen Kuang, Junyu Su, Ahmad Latifian, Sanli Eshraghi, Alireza Ghafari

“…However, unchanged or reduced regulations led to declines in information sharing. The neural network’s predictions showed acceptable error compared to experimental results.…”
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Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network by Hans Juwiantho, Esther Irawati Setiawan, Joan Santoso, Mauridhi Hery Purnomo

“…Hasil percobaan yang telah dilakukan dengan algoritme Deep Convolutional Neural Network memiliki nilai akurasi terbaik sebesar 76,40%.   …”
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Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 by Ozcan Ebru Ifakat

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Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network by Nabeel H. Alharthi, Sedat Bingol, Adel T. Abbas, Adham E. Ragab, Mohamed F. Aly, Hamad F. Alharbi

“…In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. …”
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GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning by Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang

Subjects: “…Graph neural networks…”
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A Gray-Box Equivalent Neural Network Circuit Small-Signal Modeling Applied to GaN Transistors by Anwar Jarndal

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Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout by I Gede Totok Suryawan, I Putu Agus Eka Darma Udayana

“…Salah satunya dengan menggunakan Deep learning yaitu Convolutional Neural Network (CNN) yang sudah terbukti merupakan salah satu metode yang dapat digunakan untuk melakukan skrining pasien dan mendeteksi COVID-19. …”
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Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints by Changhui Wang, Mei Liang, Yongsheng Chai

“…Combined with backstepping and adaptive technique, the unknown nonlinear uncertainties are approximated by the radial basis function neural network (RBF NN) in each step of the backstepping procedure. …”
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Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method by Wenlong Tian, Zhaoyong Mao, Fuliang Zhao, Zhicao Zhao

“…Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. …”
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Synchronization of Chaotic Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Sampled-Data Control by Ting Lei, Qiankun Song, Zhenjiang Zhao, Jianxi Yang

“…This paper investigates the synchronization problem for neural networks with leakage delay and both discrete and distributed time-varying delays under sampled-data control. …”
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A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer by Sherihan Aboelenin, Foriaa Ahmed Elbasheer, Mohamed Meselhy Eltoukhy, Walaa M. El-Hady, Khalid M. Hosny

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An Incremental Regularization Kernel Randomized Neural Network for Electrical Energy Output Prediction in Combined Cycle Power Plant by Xinlei Li, Henggong Yue, Fei Li, Zerui Yun, Zhenzhou Li, Yongjie Liu, Wei Shi, Yang Liu

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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments by Shixi Tang, Jinan Gu, Keming Tang, Wei Ding, Zhengyang Shang

“…Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. …”
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Finite-Time Nonfragile Dissipative Control for Discrete-Time Neural Networks with Markovian Jumps and Mixed Time-Delays by Ling Hou, Dongyan Chen, Chan He

“…This paper considers the stochastic finite-time dissipative (SFTD) control problem based on nonfragile controller for discrete-time neural networks (NNS) with Markovian jumps and mixed delays, in which the mode switching phenomenon, is described as Markov chain, and the mixed delays are composed of discrete time-varying delay and distributed delays. …”
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Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) by Khalissa Saada, Salah Amroune, Moussa Zaoui

“…Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's Modulus and stress). …”
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