Sudden Intrabulbar Amyloid Increase Simultaneously Disrupts Olfactory Bulb Oscillations and Odor Detection by Rebeca Hernández-Soto, Keila Dara Rojas-García, Fernando Peña-Ortega

“…Moreover, this loss of smell appears to be related to alterations in neural network activity in several olfactory-related circuits, including the OB, as has been observed in anesthetized animals and brain slices. …”
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Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map by Guojun Xu, Ke Qu, Zhanglong Li, Zixuan Zhang, Pan Xu, Dongbao Gao, Xudong Dai

“…A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid.…”
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Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images by Yu Liu, Du-Gang Xi, Zhao-Liang Li, Chun-Xiang Shi

“…First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. …”
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A novel machine learning approach for numerical simulation on the hybrid nanofluid flow past a converging/diverging channel: Properties of tantalum and alumina nanoparticles by Shilpa B, Jasgurpreet Singh Chohan, N Beemkumar, Ankur Kulshreshta, Refka Ghodhbani, Nashwan Adnan Othman, Barno Abdullaeva, M Ijaz Khan

“…Also, the Laguerre Polynomial-based Physics Informed Neural Network (L-PINN) model is adopted to analyze the fluid flow, heat, and mass transfer characteristics. …”
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Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics by Achmad Widodo, Djoeli Satrijo, Toni Prahasto, Gang-Min Lim, Byeong-Keun Choi

“…This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. …”
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A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks by Rongmei Zhao, Xi Xiong, Xia Zu, Shenggen Ju, Zhongzhi Li, Binyong Li

“…Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. …”
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Acoustic Trauma Changes the Parvalbumin-Positive Neurons in Rat Auditory Cortex by Congli Liu, Tao Xu, Xiaopeng Liu, Yina Huang, Haitao Wang, Bin Luo, Jingwu Sun

“…Parvalbumin-containing neurons (PV neurons), a subset of GABAergic neurons, greatly shape and synchronize neural network activities. However, the change of PV neurons following acoustic trauma remains to be elucidated. …”
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From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing by Bashayr Alqahtani, Hanrui Li, Abdul Momin Syed, Nazek El-Atab

“…The reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.…”
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Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine by Hang-cheong Wong, Pak-kin Wong, Chi-man Vong

“…This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). …”
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Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning by Maryam Viqar, Erdem Sahin, Elena Stoykova, Violeta Madjarova

“…For reconstruction, two encoder–decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. …”
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A New Evaluation Method of Total Organic Carbon for Shale Source Rock Based on the Effective Medium Conductivity Theory by Jian Fu, Xuesong Li, Yonghe Sun, Qiuli Huo, Ting Gao, Li Fu, Yuchen Liu, Suxing Dong, Haijun Fan

“…At present, the commonly used methods for assessing TOC include △logR and neural network method. However, practice shows that these methods have limitations in the application of unconventional intervals of sand-shale interbeds, and they cannot sufficiently reflect the variation of TOC in the vertical direction. …”
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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). Following methodological refinement, it becomes apparent that ensemble learning approaches, exemplified by XGB and LGBM, exhibit markedly superior predictive performance, surpassing conventional models with an accuracy rate exceeding 85%. …”
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Coincidence Detection Using Spiking Neurons with Application to Face Recognition by Fadhlan Kamaruzaman, Amir Akramin Shafie, Yasir M. Mustafah

“…We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. …”
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Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection by Zhang Kun, Li Hongren, Wang Xin, Xie Daxing, Sun Xiaokai

“…The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. …”
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Supervised Convolutional Encoder-Decoder With Gated Linear Units for Detecting Fetal R-Peaks by Yao Chen, Jian Wang, Jing Zhang, Junkun Zhang, Zhentao Qin, Xinran Liu

“…Specifically, the encoder is a stacked convolutional layer equipped with a gating linear unit (GLU), and the decoder is a recurrent neural network. The GLU convolutional layer can effectively extract and aggregate the features to improve the generalization ability. …”
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Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning by Sresakoolchai Jessada, Cheputeh Ni-Asri

“…The machine learning technique used in the study is the convolutional neural network. The indicator used to evaluate the machine learning model’s performance is the accuracy. …”
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Emotional recognition of EEG signals utilizing residual structure fusion in bi-directional LSTM by Yue Xu, Yunyuan Gao, Zhengnan Zhang, Shunlan Du

“…The experimental results demonstrated that the novel neural network model proposed in this paper had outperformed currently available methods in emotion recognition tasks.…”
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Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques by Suk-Ju Hong, Shin-Joung Rho, Ah-Yeong Lee, Heesoo Park, Jinshi Cui, Jongmin Park, Soon-Jung Hong, Yong-Ro Kim, Ghiseok Kim

“…Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. …”
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Weakly-Supervised Deep Shape-From-Template by Sara Luengo-Sanchez, David Fuentes-Jimenez, Cristina Losada-Gutierrez, Daniel Pizarro, Adrien Bartoli

“…WS-DeepSfT addresses the limitations of existing SfT techniques by combining a weakly-supervised deep neural network (DNN) for registration and a classical As-Rigid-As-Possible (ARAP) algorithm for 3D reconstruction. …”
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Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation by Yongqi Guo, Yuxu Lu, Yu Guo, Ryan Wen Liu, Kwok Tai Chui

“…To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. …”
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