Convolutional neural network for the early identification of weeds: A technological support to biodiversity and yield losses mitigation by Claudio Russo, Valerio Cirillo, Marco Esposito, Matteo Lentini, Nausicaa Pollaro, Albino Maggio

“…Chemical management, the most utilized method for weed control, has serious drawbacks. …”
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Convolutional block attention gate-based Unet framework for microaneurysm segmentation using retinal fundus images by C. B. Vanaja, P. Prakasam

“…The addition of CBAM introduces channel and spatial attention mechanisms, allowing the network to concentrate on the most useful elements while reducing the less relevant ones. …”
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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

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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

“…With the development of the global economy, PM2.5 fine particulate matter concentration has emerged as a major environmental issue worldwide, significantly impacting human health. However, most existing research methods largely ignore the spatial characteristics of PM2.5 concentrations. …”
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iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism by Qian Zhou, Jie Meng, Hao Luo

“…In this study, iPro-CSAF, a convolutional spiking neural network combined with spiking attention mechanism is designed for promoter recognition. …”
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Impact of the Radar Image Resolution of Military Objects on the Accuracy of their Classification by a Deep Convolutional Neural Network by I. F. Kupryashkin

“…Introduction. Deep convolutional neural networks are considered as one of the most promising tools for classifying small-sized objects on radar images. …”
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Precision Recognition of Rock Thin Section Images With Multi‐Head Self‐Attention Convolutional Neural Networks by Pengfei Lv, Weiying Chen, Xinyu Zou

“…Traditional manual methods rely on expert experience, being subjective and time‐consuming. Convolutional neural network (CNN)‐based automated classification has potential but is less effective with more rock types and limited training data, restricting its applications. …”
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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

“…Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis, a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network (MCMI-GCFN) is proposed in this paper. …”
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Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders by Thanat Payatsuporn, Pittipol Kantavat, Nichthida Tangnuntachai, Nopporn Tipparawong, Waratchanok Techapapa, Boonserm Kijsirikul, Somboon Keelawat

“…Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Currently, pathologists diagnose the PTC by interpreting their nuclei. …”
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Advancements and outlooks in utilizing Convolutional Neural Networks for plant disease severity assessment: A comprehensive review by Douglas Leite, Alisson Brito, Gregorio Faccioli

“…Once limited to disease detection, Convolutional Neural Networks (CNNs) applications now exhibit automatic severity calculation and classification solutions. …”
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p-im2col: Simple Yet Efficient Convolution Algorithm With Flexibly Controlled Memory Overhead by Anton V. Trusov, Elena E. Limonova, Dmitry P. Nikolaev, Vladimir V. Arlazarov

“…Convolution is the most time-consuming operation in modern deep artificial neural networks, so its performance is crucial for fast inference. …”
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Adaptive Disconnector States Diagnosis Method Based on Adjusted Relative Position Matrix and Convolutional Neural Networks by Peifeng Yan, Chenzhang Chang, Dong Hua, Haomin Huang, Suisheng Liu, Peiyi Cui

“…Due to long-term outdoor working, High-Voltage Disconnectors (HVDs) are prone to potential faults. Currently, most studies on HVD state diagnosis methods have tested only one type of HVD, and the generalization capability of these methods for other HVDs has not been verified. …”
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Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture by Chin-Cheng Wu, Cheng-Wei Tsai, Fei-En Wu, Chi-Hsuan Chiang, Jin-Chern Chiou

“…Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. …”
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BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images by D. E. Martina Jaincy, V. Pattabiraman

“…Here, Breast Cancer Deep Convolutional Neural Network (BCDCNN) is presented for Breast Cancer Detection (BCD) using MRI images. …”
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SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things by Mimonah Al Qathrady, Safi Ullah, Mohammed S. Alshehri, Jawad Ahmad, Sultan Almakdi, Samar M. Alqhtani, Muazzam A. Khan, Baraq Ghaleb

“…This paper proposes a self‐attention convolutional neural network (SACNN) architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features. …”
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RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research by Yajun Zhang, Congcong Wang, Feng Li, Weiqian Yu, Yuankang Wang, Jingying Chen

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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…These techniques are integrated with an Efficient Depthwise Convolutional Neural Network (ED-CNN) architecture that employs depth-separable convolutions, dramatically reducing computational complexity while maintaining high segmentation accuracy. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. …”
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Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network by Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P. Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen

“…To address these challenges, we propose a novel multi-modal convolutional neural network (CNN) architecture designed specifically for pixel-level peatland classification. …”
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