Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices by Antonio Robles-Guerrero, Salvador Gómez-Jiménez, Tonatiuh Saucedo-Anaya, Daniela López-Betancur, David Navarro-Solís, Carlos Guerrero-Méndez

“…Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. …”
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A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition by Lianglian Gu, Guangzhi Di, Danju Lv, Yan Zhang, Yueyun Yu, Wei Li, Ziqian Wang

“…The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. To address this issue, we propose a multi-scale hybrid convolutional attention mechanism model (MUSCA). …”
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Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks by Weixuan Wang, Yating Wei, Longfei Hao, Zushuai Wei, Tianjie Zhao

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Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi, Yoshifumi Okada

“…Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. …”
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Rotation-Invariant Convolution With Point Sort and Curvature Radius for Point Cloud Classification and Segmentation by Zhao Shen, Xin Jia, Jinglei Zhang

“…(i) Similar distances and angles among different points would lead to ambiguous descriptions of local regions. …”
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Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network by Zikun Wang, Maxime Irene Dedo, Kai Guo, Keya Zhou, Fei Shen, Yongxuan Sun, Shutian Liu, Zhongyi Guo

“…The vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. Generally, atmospheric turbulence can distort the helical phase fronts of OAM beams, which presents a critical challenge to the effective recognition of OAM modes. …”
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Advanced Temporal Convolutional Network Framework for Intrusion Detection in Electric Vehicle Charging Stations by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, It Ee Lee, Muhammad Sheraz, Eng Eng Ngu, Teong Chee Chuah

“…The proposed Temporal Convolutional Network (TCN)-based Intrusion Detection System (IDS) architecture integrates four key innovations: multi-receptive fields, a gating mechanism, iterative dilation, and a self-attention mechanism combined with a Squeeze-and-Excitation (SE) block to recalibrate feature responses by explicitly modeling interactions between different channels. …”
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Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification by Xiangyue Yu, Ning Li, Di Wu, Zheng Li, Zhenyuan Wu, Ximing Ma

“…Specifically, a dual-layer multihop graph convolutional network is constructed within the GCN branch, which can take the features of superpixel at different segmentation scales as network nodes to effectively capture and fuse the superpixel features in HSI. …”
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A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification by Muhammad Zahid Hussain, Tariq Shahzad, Shahid Mehmood, Kainat Akram, Muhammad Adnan Khan, Muhammad Usman Tariq, Arfan Ahmed

“…In this research, we used three different pre-trained CNN based architectures (AlexNet, GoogleNet, and MobileNetV2) each implemented with several solvers (e.g. …”
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Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) by Nurilmiyanti Wardhani, Billy Eden William Asrul, Antonius Riman Tampang, Sitti Zuhriyah, Abdul Latief Arda

“…This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.…”
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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…While the results are promising, further research is necessary to assess how the model performs across different clinical scenarios. Future studies could focus on integrating additional data types, such as longitudinal imaging and multimodal techniques, to further enhance diagnostic accuracy and clinical utility. …”
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Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning by Farid, Proshanta Kumer Das, Monirul Islam, Ebna Sina

“…Finally, we have tested the proposed Bangladeshi vehicle detection system with different timing, lighting, and weather conditions in several areas of Dhaka city. …”
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Automatic Potato Crop Beetle Recognition Method Based on Multiscale Asymmetric Convolution Blocks by Jingjun Cao, Xiaoqing Xian, Minghui Qiu, Xin Li, Yajie Wei, Wanxue Liu, Guifen Zhang, Lihua Jiang

“…Specifically, it comprises several multiscale asymmetric convolution blocks, which are designed to extract features at multiple scales, mainly by integrating different-sized asymmetric convolution kernels in parallel. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. …”
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Application of convolutional neural networks trained on optical images for object detection in radar images by V.A. Pavlov, A.A. Belov, S.V. Volvenko, A.V. Rashich

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System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…In practice, it could differ from the declared one by no more than 10-15 percent. …”
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FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion by Rumei Li, Liyan Zhang, Zun Wang, Xiaojuan Li

“…Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. …”
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Multi-Scale Plastic Lunch Box Surface Defect Detection Based on Dynamic Convolution by Jing Yang, Gang Zhang, Yunwang Ge, Jingzhuo Shi, Yiming Wang, Jiahao Li

“…A multi-scale attention mechanism based on dynamic convolution is designed in this paper to solve the problems of large differences in surface defects of plastic lunch boxes and insensitive perception of multi-scale features. …”
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Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network by Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG

“…To solve problems of noise points and high segmentation error for image shadow brought by traditional Vibe+ algorithm, a novel background segmentation method (Vibe++) based on the improved Vibe+ was proposed.Firstly, binarization image was acquired by using traditional Vibe+ algorithm from surveillance video.The connected regions were marked based on the region-growing domain marker method.The area threshold was obtained with difference characteristics of boundary area, the connected regions below threshold were treated as disturbing points.Secondly, five different kernel functions were introduced to improve the traditional MeanShift clustering algorithm.After improving, this algorithm was fused effectively with partitioned convolutional neural network.Finally, program of classification of trailing area, non-trailing area and trailing edge area in the resulting image was performed.Position coordinates of the trailing area were calculated and confirmed, and the trailing area was quickly deleted to obtain the final segmentation result.This segmentation accuracy was greatly improved by using the proposed method.The experimental results show that the proposed algorithm can achieve segmentation accuracy of more than 98% and has good application effect and high practical value.…”
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Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…Furthermore, a lightweight detection head (LDH) is introduced to process feature information from different channels using efficient grouped convolutions. …”
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