Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model by Dechen Yao, Qiang Sun, Jianwei Yang, Hengchang Liu, Jiao Zhang

“…Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. …”
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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

“…Four machine learning models (artificial neural network (ANN), decision tree (DT), random forest (RF), and gradient boost tree (GBT).) were used to predict and compare actual and anticipated solar radiation values. …”
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Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm by Wenyu Zhang, Zhongyue Su, Hongli Zhang, Yanru Zhao, Zhiyuan Zhao

“…The present study investigated singular spectrum analysis (SSA) with a reduced parameter algorithm in three time series models, the autoregressive integrated moving average (ARIMA) model, the support vector machine (SVM) model, and the artificial neural network (ANN) model, to forecast the wind speed in Shandong province, China. …”
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Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

“…However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. …”
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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception by Changjie Wang, Zhihua Li, Benjamin Sarpong

“…First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. …”
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Research on cutting mechanism and process optimization method of gear skiving by Peng Wang, Yuanchao Ni, Xiaoqiang Wu, Jiaxue Ji, Geng Li, Jiahao Wu

“…Furthermore, a prediction model of cutting force and cutting temperature is established using a neural network optimized by genetic algorithm. This prediction model allows for the construction of a multi-objective optimization model for the process parameters. …”
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A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure by Wenmao Wu, Zhizhou Yu, Jieyue He

“…SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of discriminations. …”
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Optimization of Photonic Nanocrystals for Invisibility Using Artificial Intelligence by Z. Dorrani

“…Therefore, this paper employs the deep neural network architecture ResNet to optimize photonic crystals. …”
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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials by Soham Mandal, Prabal K. Maiti

“…Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity (κ) of CALF-20. …”
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Analyzing the speed of sound in neutron star with machine learning by Sagnik Chatterjee, Harsha Sudhakaran, Ritam Mallick

“…In this work, we use a neural network approach to study matter at intermediate densities to analyze the variation of the speed of sound and the measure of trace anomaly considering astrophysical constraints of mass–radius measurement of 18 neutron stars. …”
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A Repeatable Optimization for Kinematic Energy System with Its Mobile Manipulator Application by Ying Kong, Ruiyang Zhang, Yunliang Jiang, Xiaoyun Xia

“…To remedy deviations between initial position and desired position of each kinematic joint angle, a special kind of repeatable optimization for kinematic energy minimization based on terminal-time Zhang neural network (TTZNN) with finite-time convergence is proposed for inverse kinematics of mobile manipulators. …”
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Designing of Neuro-Fuzzy Controllers for Brushless DC Motor Drives Operating with Multiswitch Three-Phase Topology by Ch. Vinay Kumar, G. Madhusudhana Rao, A. Raghu Ram, Y. Prasanna Kumar

“…The fuzzy logic controller is implanted to adjust the speed of the neural network and is also designed for the analysis of the stability of the system. …”
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An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder by Fengtao Wang, Bosen Dun, Xiaofei Liu, Yuhang Xue, Hongkun Li, Qingkai Han

“…Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. …”
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Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning by Jing An, Ping Ai, Dakun Liu

“…This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. …”
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Analytical Redundancy of Variable Cycle Engine Based on Proper Net considering Multiple Input Variables and the Whole Engine’s Degradation by Zihao Zhang, Xianghua Huang, Tianhong Zhang

“…Experiments were compared with performance of analytical redundancy based on Proper net, seven convolutional neural network topological structures, and five shallow learning methods. …”
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Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model by Jiaqi Deng, Xin Liu, Gang Du, Xu Yang, Liangzhong Jiang, Chenglong Sun

“…These experiments demonstrate that the EMPViT model surpasses traditional neural network models such as CNN and ViT in both accuracy and performance, achieving a peak accuracy of 99.98%.…”
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Forecasting financial distress for organizational sustainability: An empirical analysis by Soumya Ranjan Sethi, Dushyant Ashok Mahadik

“…This study aims to assess the predictive capabilities of Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) in predicting a company's bankruptcy. …”
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Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel by Tien-Thinh Le, Hang Thi Pham, Hiep Khac Doan, Panagiotis G. Asteris

“…Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose.…”
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Urban Traffic Flow Forecast Based on FastGCRNN by Ya Zhang, Mingming Lu, Haifeng Li

“…To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. …”
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Dual-Vehicle Heterogeneous Collaborative Scheme with Image-Aided Inertial Navigation by Zi-Ming Wang, Chun-Liang Lin, Chian-Yu Lu, Po-Chun Wu, Yang-Yi Chen

“…Our approach harnesses advanced image feature-recognition techniques, employing an enhanced scale-invariant feature transform algorithm and a neural network model. The recognition of prominent scene features is prioritized, thus improving recognition speed and precision. …”
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