Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification by Mouna Bouchane, Wei Guo, Shuojin Yang

“…The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). …”
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ABA-IDS: Attention-Based Autoencoder for Intrusion Detection in Assistive Mobility Robotic Network by Daniel Ayo Oladele, Elisha Didam Markus

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Automated Concrete Bridge Deck Inspection Using Unmanned Aerial System (UAS)-Collected Data: A Machine Learning (ML) Approach by Rojal Pokhrel, Reihaneh Samsami, Saida Elmi, Colin N. Brooks

“…According to the National Bridge Inventory (NBI) and the Federal Highway Administration (FHWA), the cost to repair U.S. bridges was recently estimated at approximately USD 164 billion. …”
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Deep learning approach for automated hMPV classification by Sivarama Prasad Tera, Ravikumar Chinthaginjala, Irum Shahzadi, Priya Natha, Safia Obaidur Rab

“…This study proposes a novel deep learning framework, referred to as hMPV-Net, which leverages Convolutional Neural Networks (CNNs) to facilitate the precise detection and classification of hMPV infections. …”
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A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement by Yanfang Lyu, Yunyang Zhang, Zhiqiang Gong, Xiao Kang, Wen Yao, Yongmao Pei

“…Therein, the fluid deep learning model consists of a wall shear stress model and an innovative flow field model with U-shaped architecture jointing the Fourier neural operator and modified convolution long-short term memory model. Two models effectively capture coupling interaction forces, and the latter has higher accuracy in modelling instantaneous flow fields compared with baseline Convolutional Neural Networks-based models with similar parameters. …”
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Single Infrared Super-Resolution via a Shifted Full-Scale Non-Local Network by Honghong Lu, Zhenhua Li

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A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization by Saja S Hanoon, Hasan F Khazal, Thamer M Jamel

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Comparative Study of Person Re-Identification Techniques Based on Deep Learning Models by Mossaab Idrissi Alami, Abderrahmane Ez-zahout, Fouzia Omary

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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…Graph convolutional networks (GCNs) exhibit remarkable capabilities in hyperspectral image (HSI) classification tasks, primarily due to their ability to establish long-range pixel correlations. …”
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Estimating Automobile Crash Characteristics from Images using Deep Learning by Daniel Silver, Hardik Manek, Matthew Kay, Paul Travis

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Acoustic fault diagnosis of traction motor bearing based on fusion feature by YANG Gang, WEI Yuqian, LI Fu

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Pig behavior recognition and disease warning based on compressed sensing and long-short term memory network by Ren Wang, Mingdong Zhao

“…The improved k-means clustering algorithm is combined with convolutional neural network and long and short term memory network to provide early warning for pig diseases. …”
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YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition by Ruikang Luo, Aman Anand, Farhana Zulkernine, Francois Rivest

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A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster by Tong Yang, Minan Tang, Hanting Li, Hongjie Wang, Chuntao Rao

“…In this work, to improve the accuracy of photovoltaic power prediction, a TCN-Wpsformer (temporal convolutional network-window probability sparse Transformer) day-ahead photovoltaic power prediction model based on combining data restoration and FCM (fuzzy C means) cluster is proposed. …”
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Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN by QIN Hao, SU Liwei, WU Guangbin, JIANG Chongying, XU Zhipeng, KANG Feng, TAN Huochao, ZHANG Yongjun

“…Accurate power supply service traffic prediction not only improves the quality of power customer service, but also effectively reduces the cost of customer service personnel. Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. …”
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From Simulation to Reality: Transfer Learning for Automating Pseudo‐Labeling of Real and Infrared Imagery by Jeffrey Choate, Derek Worth, Scott L. Nykl, Clark Taylor, Brett Borghetti, Christine Schubert Kabban, Ryan Raettig

“…Training a convolutional neural network (CNN) for real‐world applications is challenging due to the requirement of high‐quality labeled imagery. …”
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Enhancing skin lesion classification: a CNN approach with human baseline comparison by Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef, Karar Ali, Marwan A. Albahar

“…This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. …”
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Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion by Tieliu Jiang, Wenyue Liu, Jianqing Lin, Xu Jin, Zhongyan Liu

“…By integrating the local feature extraction capabilities of temporal convolutional networks (TCN), the global temporal modeling advantages of Bi-Mamba2, and the nonlinear fitting characteristics of Kolmogorov-Arnold networks (KAN), a multi-module collaborative prediction framework is constructed. …”
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A Comparative Study of Image Processing and Machine Learning Methods for Classification of Rail Welding Defects by Mohale Emmanuel Molefe, Jules Raymond Tapamo, Siboniso Sithembiso Vilakazi

“…However, the conventional defect investigation process from the obtained radiography images is costly, lengthy, and subjective as it is conducted manually by trained experts. …”
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Evaluation of Neural Networks for Improved Computational Cost in Carbon Nanotubes Geometric Optimization by Luis Josimar Vences-Reynoso, Daniel Villanueva-Vasquez, Roberto Alejo-Eleuterio, Federico Del Razo-López, Sonia Mireya Martínez-Gallegos, Everardo Efrén Granda-Gutiérrez

“…To address this challenge, this study utilized three deep-learning-based neural network architectures: Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), and 1D Convolutional Neural Networks (1D-CNNs). Simulations were performed using the CASTEP module in Material Studio to generate datasets for training the neural networks. …”
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