Novel algorithm for multifocus image fusion: integration of convolutional neural network and partial differential equation by Gargi J Trivedi, Rajesh Sanghvi

“…This paper presents a novel method for Multifocus image fusion that combines anisotropic diffusion PDE filtering and convolutional neural network (CNN) feature extraction. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. …”
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A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction by Yunyang Huang, Hongyu Yang, Zhen Yan

“…Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. …”
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Pruning convolution neural networks using filter clustering based on normalized cross-correlation similarity by Niaz Ashraf Khan, A. M. Saadman Rafat

“…However, model pruning can resolve this issue for Convolutional Neural Networks (CNNs), since it is one of the most popular approaches to reducing computational complexities. …”
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Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks by Tinuk Agustin, Indrawan Ady Saputro, Mochammad Luthfi Rahmadi

“…Objective: This study evaluates the effectiveness of data augmentation techniques, specifically classical augmentation and Deep Convolutional Generative Adversarial Networks (DCGAN), in improving CNN performance for rice disease classification. …”
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TECHNOLOGIES FOR DEVELOPING DECISION SUPPORT SYSTEMS FOR THE DIAGNOSIS OF BLOOD DISORDERS USING CONVOLUTIONAL NEURAL NETWORKS by U. V. Maslikova, A. A. Supilnikov

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Diagnosis Anthracnose of Chili Pepper Using Convolutional Neural Networks Based Deep Learning Models by Hae-In Kim, Ju-Yeon Yoon, Ho-Jong Ju

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Fast and Accurate Identification of Kiwifruit Diseases Using a Lightweight Convolutional Neural Network Architecture by Kangchen Liu, Li Li, Xiujun Zhang

“…This study utilizes deep learning to enhance kiwifruit disease identification by evaluating eight advanced convolutional neural network (CNN) architectures on real-world field data. …”
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Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations by Ying Ren, Siyuan Wang, Bisheng Xia, Biesheng Xia

“…Abstract Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. …”
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Imaging Through Turbid Media With Vague Concentrations Based on Cosine Similarity and Convolutional Neural Network by Lina Zhou, Yin Xiao, Wen Chen

“…We can exploit the classified density to automatically select the most appropriate datasets to train a CNN model and then make predictions in real time with the trained CNN model. …”
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Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN by Marcos Villar García, José-Benito Bouza-Rodríguez, Alberto Comesaña-Campos

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DC-WUnet: An Underwater Ranging Signal Enhancement Network Optimized with Depthwise Separable Convolution and Conformer by Xiaosen Liu, Juan Li, Jingyao Zhang, Yajie Bai, Zhaowei Cui

“…However, the assumption of Gaussian noise underlying most time–frequency domain algorithms limits their effectiveness in mitigating non-Gaussian interference. …”
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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. …”
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Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm. by Afira Aslam, Syed Muhammad Usman, Muhammad Zubair, Amanullah Yasin, Muhammad Owais, Irfan Hussain

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Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices by Vincent, Garry Darian, Nico Surantha

“…Skin cancer is one of the most common and life-threatening diseases. In the current era, early detection remains a significant challenge, particularly in remote and underserved regions with limited internet access. …”
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Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks by Chieh Fu Hsiao, Georg Feyrer, Anthony Stein

“…However, most of the existing researches are based on RGB images due to the availability and the comparably low cost of image collection. …”
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Convolutional neural networks-based red blood cell detection and tracking with automatic data generation by Kohei TOYAMA, Tomoki MIZUNO, Takuto ARAKI, Toru HYAKUTAKE

“…Quantification of the RBC partitioning in bifurcating channels is necessary; however, most measurements are often performed manually, which has limitations in terms of labor intensive and reproducibility. …”
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Revealing job accessibility supported by public transit services using a graph convolutional network by Meihan Jin, Leiyu Liu, Yongxi Gong, Yu Liu, Christophe Claramunt

“…Public transit is currently one of the most popular modes of transportation for commuting in metropolitan areas. …”
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A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces by Qiancheng Hao, Wenjing Liu, Wenze Gao, Xianpeng Wang

“…The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. …”
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PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things by Auwal Sani Iliyasu, Abdul Jabbar Siddiqui, Houbing Song, Fahad Jibrin Abdu

“…However, the high computational and storage requirements make these impractical for most IoT devices. To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. …”
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