Early Electrophysiological Disintegration of Hippocampal Neural Networks in a Novel Locus Coeruleus Tau-Seeding Mouse Model of Alzheimer’s Disease by A. Ahnaou, C. Walsh, N. V. Manyakov, S. A. Youssef, W. H. Drinkenburg

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Fully automatic fossa ovalis segmentation from computed tomography images using deep neural network with atlas-based localization by Gakuto Aoyama, Toru Tanaka, Yukiteru Masuda, Naoki Matsuki, Ryo Ishikawa, Masahiko Asami, Kiyohide Satoh, Takuya Sakaguchi

“…Methods: Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN. …”
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Prediction and Classification of Financial Criteria of Management Control System in Manufactories Using Deep Interaction Neural Network (DINN) and Machine Learning by Amir Yousefpour, Hamid Mazidabadi Farahani

“…In this study, we presented a novel method named deep interaction neural network to predict the performance of the manufacturing companies based on their leading competitors using features cost leadership and differentiation strategies. …”
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Multi-task learning load time series situational prediction based on gated recurrent neural networks considering spatial correlations by Mei Huang, Qian Ai

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Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network by Nabeel H. Alharthi, Sedat Bingol, Adel T. Abbas, Adham E. Ragab, Ehab A. El-Danaf, Hamad F. Alharbi

“…In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. …”
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Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study by Michael Axenhus, Anna Wallin, Jonas Havela, Sara Severin, Ablikim Karahan, Max Gordon, Martin Magnéli

“…We aimed to train and evaluate a convolutional neural network (CNN) model to diagnose metacarpal and phalangeal fractures using plain radiographs according to the AO/OTA classification system and custom classifiers. …”
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Extraction of Catechins from Aegle marmelos Fruit Pulp: Statistical Optimization Using Response Surface Methodology and Artificial Neural Networks by Divya Kurimilla, Meena Vangalapati, Venkata Ratnam Myneni

“…Additionally, MATLAB’s artificial neural network (ANN) was used to accurately estimate the extraction yield. …”
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Corrigendum to “Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network” by Yang Yang, Lin Yang, Bo Wu, Gang Yao, Hang Li, Robert Soltys

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Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing by A. Moosavian, H. Ahmadi, A. Tabatabaeefar, M. Khazaee

“…This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. …”
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Compressive Strength Prediction of Alkali-Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE) by Xiaoyu Qin, Qianmin Ma, Rongxin Guo, Zhigang Song, Zhiwei Lin, Haoxue Zhou

“…Compressive strength of alkali-activated slag (AAS) concrete is influenced by multi-factors in a nonlinear way. Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3-day (3 d) and 28-day (28 d) compressive strength of AAS were established in this study by using the data reported in related literature, where alkali concentration of activator (Na2O%), modulus of activator (Ms), water/binder ratio (W/B), surface area of slag (SA), and basicity index of slag (Kb) were taken as input parameters. …”
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Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression by Paolo Di Barba, Arash Ghafoorinejad, Maria Evelina Mognaschi, Fabrizio Dughiero, Michele Forzan, Elisabetta Sieni

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Prediction of Radiation Belts Electron Fluxes at a Low Earth Orbit Using Neural Networks With PROBA‐V/EPT Data by Edith Botek, Viviane Pierrard, Alexandre Winant

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A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 by Miao Fang

“…This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. …”
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Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network by Rong Zou, Yu Zhang, Junlan Gu, Jin Chen

“…In this paper, a method based on low-cost TOF continuous-wave modulation and deep convolutional neural network technology is proposed. The distance detection between transparent material surfaces is converted to the problem of solving the intersection of the optical path and the transparent material’s front and rear surfaces. …”
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Medium-long-term Forecast of the Runoff of the Xinfengjiang Reservoir in Dry Season Based on the Random Forest and RBF Artificial Neural Network by ZHENG Yanhui, ZHANG Lilan, TIAN Zhaowei, CHEN Xiaohong

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The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture. by Xiying Wang

“…This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. …”
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Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter by Amar Rezoug, Jamshed Iqbal, Abdelkrim Nemra

“…This research introduces a novel dual Fast Grey Wolf Optimizer (FGWO) combined with Radial Basis Function Neural Networks (RBFNN) for a Fractional-Order PID (FOPID) controller applied to a helicopter simulator. …”
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Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm by Li Wang, Shimin Lin, Jingfeng Yang, Nanfeng Zhang, Ji Yang, Yong Li, Handong Zhou, Feng Yang, Zhifu Li

“…From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. …”
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MEMPSEP‐I. Forecasting the Probability of Solar Energetic Particle Event Occurrence Using a Multivariate Ensemble of Convolutional Neural Networks by Subhamoy Chatterjee, Maher A. Dayeh, Andrés Muñoz‐Jaramillo, Hazel M. Bain, Kimberly Moreland, Samuel Hart

“…MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP‐III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full‐disc magnetogram‐sequences and in situ data from different sources to forecast the occurrence (MEMPSEP‐I—this work) and properties (MEMPSEP‐II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. …”
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Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network by Seng-Chi Chen, Van-Sum Nguyen, Dinh-Kha Le, Nguyen Thi Hoai Nam

“…A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. …”
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