RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…First, this paper innovatively introduces Receptive-Field Attention Convolution (RFAConv) to improve the backbone and neck network of YOLOv8. …”
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An Intelligent Weed Recognition Method Based on Optical Patrol Image by Guoliang YUE, Yanqiao LU, Hao CHANG, Cuiying SUN

“…Then the network is connected to the improved image classification network to obtain a final convolutional neural network model. …”
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A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen by Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori

“…Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. …”
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A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy by Omneya Attallah

“…The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. …”
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Monitoring Critical Health Conditions in the Elderly: A Deep Learning-Based Abnormal Vital Sign Detection Model by Murad A. Rassam, Amal A. Al-Shargabi

“…By combining Temporal Convolutional Networks and attention mechanisms, the HATCN-AD model effectively monitors elderly patients’ vital signs.…”
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A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions by Usman Idris Ismail, Hui Na Chua, Rosdiadee Nordin, Muhammed Kabir Ahmed

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The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. …”
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Deep Learning for Multi-Tissue Cancer Classification of Gene Expressions (GeneXNet) by Tarek Khorshed, Mohamed N. Moustafa, Ahmed Rafea

“…We introduce a new Convolutional Neural Network architecture called Gene eXpression Network (GeneXNet), which is specifically designed to address the complex nature of gene expressions. …”
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Transformers for Vision: A Survey on Innovative Methods for Computer Vision by Balamurugan Palanisamy, Vikas Hassija, Arpita Chatterjee, Arpita Mandal, Debanshi Chakraborty, Amit Pandey, G. S. S. Chalapathi, Dhruv Kumar

“…Transformers have emerged as a groundbreaking architecture in the field of computer vision, offering a compelling alternative to traditional convolutional neural networks (CNNs) by enabling the modeling of long-range dependencies and global context through self-attention mechanisms. …”
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Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model by Furqan Rustam, Muhammad Abubakar Siddique, Hafeez Ur Rehman Siddiqui, Saleem Ullah, Arif Mehmood, Imran Ashraf, Gyu Sang Choi

“…This study adopts a deep neural network approach and proposes a model name BIR (bleedy image recognizer) that combines the MobileNet with a custom-built convolutional neural network (CNN) model to classify WCE bleedy images. …”
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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

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River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model by Kecong Wu, Lirong Chen, Yalige Bai, Xinhang Wang, Danzeng Pingcuo, Zhongpeng Han, Chengshan Wang

“…These latent objects require the analysis of their internal structures using unsupervised methods. Convolutional kernels in convolutional neural networks capture local spatial structures, and their size is essential for accurately analyzing internal structures. …”
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Fault diagnosis of MMC-MTDC based on traveling wave characteristics and KOA-CNN-BiGRU-AM by Bo YU, Xuejun GAO, Can WANG, Ruiling LI, Yanbin XU, Mengjie RONG

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Fault Diagnosis of Doubly-Fed Induction Generators Based on Electromechanical Signal Fusion by WANG Xuan, WAN Shuting, YAN Huili, CHEN Yijie

“…Secondly, these current, voltage, and stator radial vibration signals are separately classified leveraging a multi-scale convolutional neural network (CNN). …”
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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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MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang, Yuan Li

“…By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. …”
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Short-term train arrival delay prediction: a data-driven approach by Qingyun Fu, Shuxin Ding, Tao Zhang, Rongsheng Wang, Ping Hu, Cunlai Pu

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Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation by Asmaa Abdelbaki, Robert Milewski, Mohammadmehdi Saberioon, Katja Berger, José A. M. Demattê, Sabine Chabrillat

“…A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. …”
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Evaluation of Post Hoc Uncertainty Quantification Approaches for Flood Detection From SAR Imagery by Jakob Ludwig, Ronny Hansch

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Computing the Deep Semantics of Visual Communications by Trpimir Jeronim Ježić, Marko Maričević, Ivana Pavlović, Miroslav Mikota

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