Damage identification based on the inner product matrix and parallel convolution neural network for frame structure by Yingying He, Ji Feng, Baogang Sun, Feixue Wang, Likai Zhang, Jidi Jiang

“…Mainstream machine learning techniques, such as convolutional neural networks (CNN), often rely on single-domain inputs, which may provide limited information for accurate damage identification. …”
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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers by Anupam Yadav, Rifat Hussain, Madhu Shukla, Jayaprakash B, Rishiv Kalia, S. Prince Mary, Chou-Yi Hsu, Manoj Kumar Mishra, Kashif Saleem, Mohammed El-Meligy

“…This study investigates the use of two human-inspired algorithms—teaching learning-based optimization (TLBO) and student psychology-based optimization (SPBO)—to optimize convolutional neural networks (CNNs) for EEG-based drowsiness detection. …”
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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…Today is possible to extract features specific to various fields of application with the application of modern machine learning techniques, such as Convolutional Neural Networks (CNN) on MultiSpectral Images (MSI). …”
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Hybrid Transformer–Convolutional Neural Network Approach for Non-Intrusive Load Analysis in Industrial Processes by Gengsheng He, Yu Huang, Ying Zhang, Yuanzhe Zhu, Yuan Leng, Nan Shang, Jincan Zeng, Zengxin Pu

“…This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. …”
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Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation by Sercan Yalcin, Zuhal Cetin Yalcin, Muhammed Yildirim, Bilal Alatas

“…This article introduces a novel multi-task advanced convolutional neural network (MTA-CNN) framework for ALL detection in medical imaging data by simultaneously performing, expression classification, and disease detection. …”
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SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing by Wangdan Liao, Fei Chen, Fei Chen, Changyue Liu, Weidong Wang, Weidong Wang, Weidong Wang, Hongyun Liu, Hongyun Liu

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Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets by Muhammad Tahir Naseem, Na-Hyun Kim, Haneol Seo, JaeMok Lee, Chul-Min Chung, Sunghoon Shin, Chan-Su Lee

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Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN–TCN Architecture with Integrated Entropy Regularization and Pooling by Jiangang Chen, Junbo Han, Pei Su, Gaoquan Zhou

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Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…In this paper, we propose a Multi Scale Spatial-Temporal Recurrent Graph Network (MSSTRG), focusing on local temporal, multi-scale temporal and dynamic spatial correlation. …”
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Classification and recognition of spatio-temporal behavior of ships based on deep learning of trajectory feature images by Yu ZHOU, Liang HUANG, Chunhui ZHOU, Yuanqiao WEN, Yamin HUANG, Jiaci WANG

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Improving State-of-Health Estimation for Lithium-Ion Batteries Based on a Generative Adversarial Network and Partial Discharge Profiles by Hangyu Zhang, Yi-Horng Lai

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Exploring Gait Recognition in Wild Nighttime Scenes by Haotian Li, Wenjuan Gong, Yutong Li, Yikai Wu, Kechen Li, Jordi Gonzàlez

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Decoding laying hen behavior and physiological status through acoustic biomarkers: temporal patterns, rooster-hen vocalization identification in group housing and environmental ada... by Xuanting Lin, Wanjun Zhu, Longshen Liu, Zhenlei Zhou

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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

Subjects: “…traffic prediction;graph neural networks;spatial-temporal correlation;synchronous convolution;graph at-tention networks…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

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MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification by Ling-Long Li, Guang-Zhong Cao, Yue-Peng Zhang, Wan-Chen Li, Fang Cui

“…To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. …”
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Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network by Hyeon-Ju Lee, Seok-Jun Buu

“…To address these challenges, we propose a Convolutional Transformer Network. This architecture integrates convolutional layers for localized spatial feature extraction and transformer layers for global temporal dependency modeling. …”
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Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions by Yuxuan Wang, Zhouyuan Zhang, Shu Pi, Haishan Zhang, Jiatian Pi

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Improving multi-talker binaural DOA estimation by combining periodicity and spatial features in convolutional neural networks by Reza Varzandeh, Simon Doclo, Volker Hohmann

“…Aiming to improve the accuracy of multi-talker DOA estimation for binaural hearing aids with a known number of active talkers, we investigate the usage of periodicity features as a footprint of speech signals in combination with spatial features as input to a convolutional neural network (CNN). In particular, we propose a multi-talker DOA estimation system employing a two-stage CNN architecture that utilizes cross-power spectrum (CPS) phase as spatial features and an auditory-inspired periodicity feature called periodicity degree (PD) as spectral features. …”
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