Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. This method first constructed a convolutional gated linear unit based on the gating mechanism to dynamically modulate feature transmission. …”
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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

“…The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). …”
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Classifying early apple scab infections in multispectral imagery using convolutional neural networks by Alexander J. Bleasdale, J. Duncan Whyatt

“…Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. …”
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An Improved Scene Adaptive PRM Path Planning Algorithm by YANG Jingzhao, WANG Chao, ZHANG Yu

“…Aiming at the contradiction between fineness, dynamics and planning real-time when planning ground unmanned equipment paths in dynamic and complex environments, a ground unmanned equipment path planning algorithm based on risk fusion cost map and adaptive PRM is proposed. Firstly, the risk fusion cost map is established based on the multi-factor environment scene, the scene adaptive PRM probabilistic road map is constructed through the mechanisms of Gaussian convolution adaptive sampling, nonlinear probability enhancement and secondary sampling strategy, and the local planner is utilized to dynamically update the weights of the probabilistic road map, so as to realize the optimized search of global low-risk cost paths. …”
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PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport by Eunkyeong Lee, Hosik Choi, Do-Gyeong Kim

“…In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. …”
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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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Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features by Meng Wang, Juanle Wang, Mingming Yu, Fei Yang

“…Abstract Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. …”
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Reparameterized Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Image Classification by Cuiping Shi, Mengxiang Ding, Liguo Wang

“…In this study, a re-parameterized feature aggregation convolutional neural network (RepFACNN) is proposed. …”
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System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. …”
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ASCDet: cross-space UAV object detection method guided by adaptive sparse convolution by Gui Cheng, Xubin Feng, Yan Tian, Meilin Xie, Chaoya Dang, Qing Ding, Zhenfeng Shao

“…These masks guide cross-space object detection through sparse convolutions, while a global context enhancement strategy within the sparse convolution module enriches the contextual information, maintaining detection accuracy. …”
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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks by Can LI, Zengfu WANG, Xiaoxuan ZHANG, Quan PAN

“…We propose a Multi-Channel Graph Convolutional Networks (MC-GCN) for land-sea clutter classification. …”
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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 light of these, we put forward a new way of diagnosing AD using magnetic resonance imaging (MRI) scans and transfer learned convolutional neural networks (CNN). Transfer learning makes it easier to reduce the costs involved in training and improves performance because it allows the use of models which have been trained previously and which generalize very well even when there is very little training data available. …”
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Exploring Low-Cost Platforms for Automatic Chess Digitization by David Mallasén, María José Belda, Alberto A. del Barrio, Fernando Castro, Katzalin Olcoz, Manuel Prieto-Matias

“…In our study, we adapted these techniques specifically for cost-effective single-board computers like the Nvidia Jetson Nano. …”
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A multi-attention TCN based electricity price forecasting method considering high proportion of wind power fluctuation by LI Zikai, YANG Bo, ZHOU Zhongtang, LI Xin, CHEN Fengwei, JIAO Runhai

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Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network by Baoling Liu, Jun He, Xiaocui Yuan, Huiling Hu, Xuan Zeng, Zhifang Zhu, Jie Peng

“…To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. …”
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Using deep convolutional networks combined with signal processing techniques for accurate prediction of surface quality by Mohammad Zangane, Mohammad Shahbazi, Seyed Ali Niknam

“…Finally, ShuffleNet was identified as an optimal architecture for real-time monitoring due to its accuracy, noise resilience, and low computational cost. In summary, this study demonstrates the capability of deep convolutional networks combined with innovative signal encoding techniques to accurately predict surface roughness values and categories under various cutting conditions. …”
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Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network by Nandita Goyal, Munesh Chandra Trivedi

“…The major goals of cloud computing include maximization of computing resources with minimization of cost. But the truth is that everything has a price and cloud computing is no different. …”
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Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks by Julia Achatz, Mark Schubert

“…Sorting wood based on macroscopic images using convolutional neural networks (CNN) is a cost-effective and efficient approach. …”
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DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification by Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li

“…In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. …”
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Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology by Adonay S. Nunes, Matthew R. Patterson, Dawid Gerstel, Sheraz Khan, Christine C. Guo, Ali Neishabouri

“…Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. …”
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