TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING by Anastasia V. Kolmogorova, Qiuhua Sun

“…We applied the BERTopic neural network model to the collected data. As a result of the analysis, it was found that texts of 8 emotional classes contain an uneven number of topics, despite the fact that their number does not correlate directly with the amount of data: with a relatively small amount of data, there may be many topics, but in a voluminous corpus – few. …”
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Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction by G. Kajeeth Kumar, S. Muthurajkumar

“…Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. …”
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Deep Recurrent Model for Server Load and Performance Prediction in Data Center by Zheng Huang, Jiajun Peng, Huijuan Lian, Jie Guo, Weidong Qiu

“…Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. …”
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Automated Measurements of Tooth Size and Arch Widths on Cone-Beam Computerized Tomography and Scan Images of Plaster Dental Models by Thong Phi Nguyen, Jang-Hoon Ahn, Hyun-Kyo Lim, Ami Kim, Jonghun Yoon

“…The third step uses a decentralized convolutional neural network to calculate key points representing the parameters. …”
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Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector by A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M.G. Giammarchi, N. Giudice, R.M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S.M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A.C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M.D.C. Torri, A. Triossi, C. Tuvè, C. Venettacci, G. Verde, L. Votano

“…In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. …”
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Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

“…We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. …”
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Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion by Yue Hou, Zhiyuan Deng, Hanke Cui

“…This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. …”
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Сognitive Сomplaints with Unilateral Temporal Lobe Compression by M.U. Kaverina, U.V. Strunina, O.A. Krotkova

“…The phenomenology of neural network compression makes it possible to register hemispheric specificity in spontaneously generated thoughts and memories.…”
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A new band selection framework for hyperspectral remote sensing image classification by B. L. N. Phaneendra Kumar, Radhesyam Vaddi, Prabukumar Manoharan, L. Agilandeeswari, V. Sangeetha

“…Then, finally, a Convolutional Neural Network (CNN) is used for effective classification by incorporating three-dimensional convolutions. …”
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Spatiotemporal Traffic Flow Prediction with KNN and LSTM by Xianglong Luo, Danyang Li, Yu Yang, Shengrui Zhang

“…Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.…”
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Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. by Yumin Zheng, Haohan Wang, Yang Zhang, Xin Gao, Eric P Xing, Min Xu

“…Poly(A)-DG consists of a Convolution Neural Network-Multilayer Perceptron (CNN-MLP) network and a domain generalization technique. …”
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Prediction of Passive Torque on Human Shoulder Joint Based on BPANN by Shuyang Li, Paolo Dario, Zhibin Song

“…Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. …”
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Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases by K. A. Muthukumar, Dhruva Nandi, Priya Ranjan, Krithika Ramachandran, Shiny PJ, Anirban Ghosh, Ashwini M, Aiswaryah Radhakrishnan, V. E. Dhandapani, Rajiv Janardhanan

“…These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT’s spectral insights and EMD’s capability to capture nuanced data differences, offers a robust representation for CVD classification. …”
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Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism by Yu Tian, Yu Tian, Jingjie Liu, Shan Wu, Yucong Zheng, Rongye Han, Qianhui Bao, Lei Li, Lei Li, Tao Yang

“…Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. …”
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FlowMFD: Characterisation and classification of tor traffic using MFD chromatographic features and spatial–temporal modelling by Liukun He, Liangmin Wang, Keyang Cheng, Yifan Xu

“…In addition, FlowMFD utilises a cascaded model with a two‐dimensional convolutional neural network (2D‐CNN) and a bidirectional gated recurrent unit to capture spatial‐temporal dependencies between MFDCF. …”
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Implementing deep learning-based disruption prediction in a drifting data environment of new tokamak: HL-3 by Zongyu Yang, Wulyu Zhong, Fan Xia, Zhe Gao, Xiaobo Zhu, Jiyuan Li, Liwen Hu, Zhaohe Xu, Da Li, Guohui Zheng, Yihang Chen, Junzhao Zhang, Bo Li, Xiaolong Zhang, Yiren Zhu, Ruihai Tong, Yunbo Dong, Yipo Zhang, Boda Yuan, Xin Yu, Zongyuhui He, Wenjing Tian, Xinwen Gong, Min Xu

“…To address these challenges, innovative modules including predict-first neural network, data augmentation, and pseudo data placeholders are developed and implemented, which promotes the accuracy by up to 20%. …”
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High-Dose Neural Stem/Progenitor Cell Transplantation Increases Engraftment and Neuronal Distribution and Promotes Functional Recovery in Rats after Acutely Severe Spinal Cord Inju... by Taoyang Yuan, Qian Liu, Jie Kang, Hua Gao, Songbai Gui

“…At 8 weeks postgrafting, subjects that received the higher cell dose exhibited abundant nerve regeneration, extensive neuronal distribution, increased proportions of neurons and oligodendrocytes, and nascent functional neural network formation in the lesion area. Notably, a significant functional recovery was also observed. …”
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A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel by Ngoc-Long Tran, Trong-Ha Nguyen, Van-Tien Phan, Duy-Duan Nguyen

“…The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. …”
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A Convection Nowcasting Method Based on Machine Learning by Aifang Su, Han Li, Liman Cui, Yungang Chen

“…The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. …”
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Improving Proton Exchange Membrane Fuel Cell Operational Reliability Through Cabin-Based Fuzzy Control in Costal Standalone Observation Systems in Antarctica by Jin Wang, Yinke Dou, Guangyu Zuo, Bo Fan, Yuru Xing

“…A temperature-coordinated control system leveraging a BP neural network, fuzzy logic rules, and the fuzzy-based active disturbance rejection control (Fuzzy-ADRC) strategy are proposed to ensure that the temperature of the PEMFC and cabin can reach the optimal state rapidly and that the output voltage is stable. …”
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