Bearing Small Sample Fault Diagnosis based on Data Generation and Transfer Learning by Dinghui Wu, Qin Fang, Chuyi Wu

“…Then， according to the time series correlation of the data and the small sample application scenario， a transfer learning method based on one-dimensional convolutional neural network （1DCNN） and bidirectional gated recurrent unit （BiGRU） is proposed. …”
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Image denoising algorithm based on multi-channel GAN by Hongyan WANG, Xiao YANG, Yanchao JIANG, Zumin WANG

“…Aiming at the issue that the noise generated during image acquisition and transmission would degrade the ability of subsequent image processing, a generative adversarial network (GAN) based multi-channel image denoising algorithm was developed.The noisy color image could be separated into red-green-blue (RGB) three channels via the proposed approach, and then the denoising could be implemented in each channel on the basis of an end-to-end trainable GAN with the same architecture.The generator module of GAN was constructed based on the U-net derivative network and residual blocks such that the high-level feature information could be extracted effectively via referring to the low-level feature information to avoid the loss of the detail information.In the meantime, the discriminator module could be demonstrated on the basis of fully convolutional neural network such that the pixel-level classification could be achieved to improve the discrimination accuracy.Besides, in order to improve the denoising ability and retain the image detail as much as possible, the composite loss function could be depicted by the illustrated denoising network based on the following three loss measures, adversarial loss, visual perception loss, and mean square error (MSE).Finally, the resultant three-channel output information could be fused by exploiting the arithmetic mean method to obtain the final denoised image.Compared with the state-of-the-art algorithms, experimental results show that the proposed algorithm can remove the image noise effectively and restore the original image details considerably.…”
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Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling by Mohsen Abbod, Ahmad Mohammad

“…QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. …”
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A METHOD FOR MONITORING RICE SEED LOSS BASED ON WOA-BP ALGORITHM by Jin Chen, Ting Shi, Yaoming Li, Yahui Zhu, Caoyuan Niu

“…Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). The loss rate monitoring device consisted of a piezoelectric ceramic sensor module, charge amplification circuit, band-pass filter circuit, analog-to-digital (AD) converter, main control unit, etc. …”
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Research on Mechanical Properties and Parameter Identification of Beam-Column Joint with Gusset Plate Angle Using Experiment and Stochastic Sensitivity Analysis by Xian Dong, Yadi Wang

“…An improved chaotic particle swarm optimization (ICPSO) neural network algorithm was used to study the stochastic sensitivity. …”
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A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira, Jessica N. A. Campbell

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”
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Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine by Doddy Prayogo, Yudas Tadeus Teddy Susanto

“…The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). …”
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Novel distinguisher for SM4 cipher algorithm based on deep learning by Huijiao WANG, Xin ZHANG, Yongzhuang WEI, Lingchen LI

“…A method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were constructed for SM4 algorithm.Drawing inspiration from the idea that ciphertext difference could improve the performance of distinguishers, a new input data format for neural distinguisher was designed by using partial difference information between ciphertext pairs as part of the training data.The residual neural network model was used to construct the neural distinguisher.The training dataset for large blocks was preprocessed.Additionally, an improved strategy for model relearning was proposed to address the high specificity and low sensitivity of the constructed distinguisher.Experimental results show that the proposed deep learning model for SM4 can achieve 9 rounds neural distinguisher.The accuracy of 4～9 rounds distinguishers can reach up to 100%, 76.14%, 65.20%, 59.28%, 55.89% and 53.73% respectively.The complexity and accuracy of the constructed differential neural distinguisher are significantly better than those of traditional differential distinguishers, and it is currently the best neural distinguisher for the block cipher SM4 to our knowledge.It also proves that the deep learning method is effective and feasible in the security analysis of block cipher of large block.…”
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IoT-based approach to multimodal music emotion recognition by Hanbing Zhao, Ling Jin

“…The proposed CGF-Net model combines a 3D Convolutional Neural Network (3D-CNN), Gated Recurrent Unit (GRU), and Fully Connected Network (FCN). …”
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Unsupervised Learning in a Ternary SNN Using STDP by Abhinav Gupta, Sneh Saurabh

“…This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. …”
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APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS by Nguyen Huu Tho

“…The research methodology of the RSM Box-Behnken DOE method, ANN (Artificial neural network), and ANFIS (Adaptive neuro-fuzzy inference systems) has been used to determine the optimum process 3D printing parameters. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Quantitative analysis demonstrates that the CO-RF model surpasses other techniques, Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network, and standalone Random Forest (RF), both in the training and testing phases of SR prediction. …”
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Cross-ViT based benign and malignant classification of pulmonary nodules. by Qinfang Zhu, Liangyan Fei

“…There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. …”
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SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm by Yancheng Song, Jiaqi Lu, Huai Zhang, Guangjun Liu

“…The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). …”
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Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield. by Guibin Zhao, Fateh Bouchaala, Mohamed S Jouini, Umair Bin Waheed

“…To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network. The abundance of seismic features leads to many feature combinations, making the training and testing of machine learning models challenging. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. …”
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Dynamic tracking of objects in the macaque dorsomedial frontal cortex by Rishi Rajalingham, Hansem Sohn, Mehrdad Jazayeri

“…We test the mental simulation hypothesis by combining a naturalistic ball-interception task, large-scale electrophysiology in non-human primates, and recurrent neural network modeling. We find that neurons in the monkeys’ dorsomedial frontal cortex (DMFC) represent task-relevant information about the ball position in a multiplexed fashion. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…The research investigates the effectiveness of different architectural configurations, including variations in fuzzy layer membership functions (triangular, trapezoidal, Gaussian) and neural network architectures (1D ANN, 2D ANN, 1D LSTM, 2D LSTM). …”
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Artificial Intelligence-Based Real-Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks by Yu Li, Peihua Liu

“…Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron (MLP) model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. …”
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Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN by Ali Reza Ghanizadeh, Mansour Fakhri

“…Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. …”
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