Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image by Shiliang Lu, Juwei Zhang

“…The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.…”
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Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region by L. Aneesh Euprazia, A. Rajeswari, K. K. Thyagharajan, N. R. Shanker

“…The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. …”
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Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data by Wenchen Shan, Xianqiang Wang, Yubo Jiao

“…Three regression-based numerical models using multiple linear regression (MLR), back-propagation neural network (BPNN), and support vector regression (SVR) techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. …”
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Research and DSP Implementation of Speech Enhancement Technology Based on Dynamic Mixed Features and Adaptive Mask by Jie Yang, Yachun Tang

“…Then, an improved deep neural network model is designed to effectively improve the speech enhancement performance. …”
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Multiparameter Logging Evaluation of Chang 73 Shale Oil in the Jiyuan Area, Ordos Basin by Bobiao Liu, Chengqian Tan, Yinchao Huai, Yuhan Tan, Han Zhang, Zhao Feng

“…The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. …”
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Adaptation optimizes sensory encoding for future stimuli. by Jiang Mao, Constantin A Rothkopf, Alan A Stocker

“…We further tested our hypothesis by analyzing the internal sensory representations of a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). …”
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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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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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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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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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