Multimodel Modeling and Predictive Control for Direct-Drive Wind Turbine with Permanent Magnet Synchronous Generator by Lei Wang, Tao Shen, Chen Chen

“…In this strategy, wind turbine with direct-drive permanent magnet synchronous generator is modeled and a backpropagation artificial neural network is designed to estimate the wind speed loaded into the turbine model in real time through the estimated turbine shaft speed and mechanical power. …”
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Single-cell RNA-seq data augmentation using generative Fourier transformer by Nima Nouri

“…Moreover, comparisons of scGFT with leading neural network-based GMs highlight its superior performance, driven by its analytical mechanism. …”
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Differentiable architecture search-based automatic modulation recognition for multi-carrier signals by LI Jie, LI Jing, LYU Lu, GONG Fengkui

“…The time-frequency images, which were insensitive to modulation parameters, were selected as feature vectors to train the neural network. Secondly, DARTS was employed to automatically search the optimal network architecture. …”
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Predictive Modeling of Tool Life in Turning Using ANN-Taguchi Hybridization by Shrishail Sollapur B., Shubham R. Suryawanshi, Mitali Mhatre S., Dipak K. Dond, Ganesh Chate, Abhijit Bhowmik

“…In this research, we delve into the complex relationship between tool lifespan and cutting speed through experiments guided by the Taguchi method and artificial neural network (ANN) models. Several case studies have been conducted to test the practicality and effectiveness of this method in representing complex tool lifespan-cutting speed relationships.…”
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Enhanced Brain Tumor Classification through Gamma Correction in Deep Learning by Muhammad Naufal, Harun Al Azies, Rivaldo Mersis Brilianto

“…This research compares Gamma Correction with Convolutional Neural Network (CNN) in the classification of brain tumor types. …”
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Brain Tumor Detection Using a Deep CNN Model by Sonia Ben Brahim, Samia Dardouri, Ridha Bouallegue

“…Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. …”
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Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data by Zhu Zhimeng

“…Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image classification and recognition, face challenges such as unclear boundary detection, limited generalization, and lower computational efficiency in detecting brain tumors. …”
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An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style by Xiahan Liu

“…Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. …”
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AI vs. Human in Screenwriting: Is AI the Future Screenwriter? by Kemal Çelik

“…The AI model used to generate the script content is Bing Chat, which is powered by ChatGPT 4, a conversational neural network known for its ability to produce natural and coherent text. …”
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A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm by Dongguo Zhou, Yangjie Wu, Hong Zhou

“…Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. …”
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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…Then, to solve the problem of parameter optimization of the traditional GRU algorithm, the neural network architecture search was used to improve the GRU algorithm, and the N-GRU model was obtained. …”
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Social Cognition Deficits: Current Position and Future Directions for Neuropsychological Interventions in Cerebrovascular Disease by Progress Njomboro

“…These functions recruit a widely distributed neural network, making them vulnerable in most cerebrovascular diseases. …”
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Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain by Alexander Uzhinskiy

“…Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. …”
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Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather by Li Lu, Xin Lai

“…Firstly, the artificial neural network intelligent model was used to predict the thunderstorm movement track. …”
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Research on image generation technology based on deep learning by Li Jinchen

“…Deep learning can automatically learn the intrinsic features of images， reaching the goal of generating high-quality images by utilizing multi-layer neural network models. In recent years, deep learning-based image generation technology has made significant progress. …”
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Detection of Data Integrity Attack Using Model and Data-Driven-Based Approach in CPPS by G. Y. Sree Varshini, S. Latha

“…The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). …”
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Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines by Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen

“…This study utilizes a one-dimensional convolutional neural network (1-D CNN) as the deep learning model for fault classification. …”
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Writer identification and writer retrieval based on NetVLAD with Re‐ranking by Shervin Rasoulzadeh, Bagher BabaAli

“…A novel pipeline is proposed for the problem at hand by employing a unified neural network architecture consisting of the ResNet‐20 as a feature extractor and an integrated NetVLAD layer, inspired by the vector of locally aggregated descriptors (VLAD), in the head of the latter part. …”
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3D Simulation Design and Application of Traditional Hanfu Based on Internet of Things by Li Lin, Wen Gan

“…In this study, the feasibility of the 3D simulation design of Hanfu was fully studied by combining the Internet of Things technology and the convolutional neural network method. The research results show that the Internet of Things technology can efficiently and accurately collect the characteristics of patterns, colors, shapes, and historical information of Hanfu. …”
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Reinforcement learning-based real-time video streaming control and on-device training research by Huanhuan ZHANG, Anfu ZHOU, Huadong MA

“…Service platforms centered on the Internet of things and mobile Internet are in accelerating process.Hundreds of millions of end-users communicate through network real-time video services, which have become an irreplaceable core tool in human’s digital life.However, the Internet is becoming dynamic, and heterogeneous, which imposes stringent requirements on real-time video streaming control technology.Moreover, the QoE of real-time video is not satisfactory.An adaptive reinforcement learning-based video intelligent transmission algorithm was designed, which can deal with heterogeneous network environment.And then, an effective end-to-end on-device training framework was designed to decrease server overhead, and a detailed evaluation and analysis on the neural network design and structure was provided.Experimental results show that the proposed algorithm can effectively predict heterogeneous network bandwidth, and reduces the bandwidth prediction error by 48.48%, comparing with the representative streaming control algorithm.The effective bandwidth prediction can further improve the user QoE, such as improving the video fluency by 60.65%, and improving the video quality by 16.52%.Besides, the analysis can provide empirical insights for further study, and holds potential to push the development of intelligent video applications.…”
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