Low-Cost Hyperspectral Imaging in Macroalgae Monitoring by Marc C. Allentoft-Larsen, Joaquim Santos, Mihailo Azhar, Henrik C. Pedersen, Michael L. Jakobsen, Paul M. Petersen, Christian Pedersen, Hans H. Jakobsen

“…Using a one-dimensional convolutional neural network, we reached a high average classification precision, recall, and F1-score of 99.9%, 89.5%, and 94.4%, respectively, demonstrating the effectiveness of our custom low-cost HSI setup. …”
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A Convolutional Neural Network-Based Stress Prediction Method for Airfoil Structures by Wendi Jia, Quanlong Chen

“…However, conventional stress calculation methods often encounter significant computational costs and lengthy analysis times when addressing highly nonlinear and complex geometries. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. …”
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Leveraging Deep Learning and Convolutional Neural Network for Digital Waste Image Classification by Fauzan Naufal Anis, Sukmasetya Pristi, Nuryanto Nuryanto

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Automatic plant disease detection using computationally efficient convolutional neural network by Muhammad Rizwan, Samina Bibi, Sana Ul Haq, Muhammad Asif, Tariqullah Jan, Mohammad Haseeb Zafar

“…The manual approach, where plant pathologists inspect fields, is costly, error‐prone, and time‐consuming. Alternatively, automatic approaches utilize 2D plant images processed through machine learning. …”
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Convolutional spatio-temporal sequential inference model for human interaction behavior recognition by Lizhong Jin, Rulong Fan, Xiaoling Han, Xueying Cui

“…Existing methods, including skeleton sequence-based and RGB video-based models, have achieved impressive accuracy but often suffer from high computational costs and limited effectiveness in modeling human interaction behaviors.MethodsTo address these limitations, we propose a lightweight Convolutional Spatiotemporal Sequence Inference Model (CSSIModel) for recognizing human interaction behaviors. …”
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Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear by WANG Shuyu, LI Hao, MA Gang, YUAN Yubo, BU Qiangsheng, YE Zhigang

“…[Methods] This paper presents a multistep prediction model for photovoltaic power generation based on a temporal convolutional network （TCN） and DLinear combined model. …”
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An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks by Hoanh Nguyen

“…However, the high computational cost due to complex structures prevents these methods from being deployed in real-world applications. …”
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Detecting ear lesions in slaughtered pigs through open-source convolutional neural networks by Matteo D’Angelo, Domenico Sciota, Anastasia Romano, Alfonso Rosamilia, Chiara Guarnieri, Chiara Cecchini, Alberto Olivastri, Giuseppe Marruchella

“…This study aims to train open-source convolutional neural networks for detecting ear biting lesions in slaughtered pigs, as a pre-requisite for a systematic and cost-effective welfare monitoring. …”
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Sign language recognition based on dual-channel star-attention convolutional neural network by Jing Qin, Mengjiao Wang

“…However, in practical applications, sign language recognition devices must balance portability, energy consumption, cost, and user comfort, while vision-based sign language recognition must confront the challenge of model stability. …”
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Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network by Jun Zhong, Jian Xu

“…Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. …”
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EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION by Fauzi Nafi'udin, Hasih Pratiwi, Etik Zukhronah

“…In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. …”
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Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions by Qun Li, Juntao Zhang, Licheng Hua, Songyin Fu, Chenjie Gu

“…To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. …”
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A dual-branch model combining convolution and vision transformer for crop disease classification. by Qingduan Meng, Jiadong Guo, Hui Zhang, Yaoqi Zhou, Xiaoling Zhang

“…Here, the convolutional branch is utilized to capture the local features while the Transformer branch is utilized to handle global features. …”
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Detection of fasting blood sugar using a microwave sensor and convolutional neural network by Mohammad Amir Sattari, Mohsen Hayati

“…Microwave sensing—particularly through microstrip-based sensors—has recently gained attention as a promising technique for blood glucose monitoring, offering advantages such as low cost, high sensitivity, real-time response capability, and suitability for compact and wearable systems. …”
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Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction by Xianlin Ma, Long Zhang, Jie Zhan, Shilong Chang

“…By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. …”
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Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…Non-invasive condition monitoring methods based on electrical signatures of machine in an electromechanical system, are considered as simple and cost-effective approaches for the fault detection process. …”
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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
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A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention by Ziyun Zhang, Peng Liu, Chen Su, Shoufeng Tong

“…Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. …”
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A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…However, most of these methods rely on complex model with heavy parameters, leading to high computational costs that hinder their adaptation to real-time detection environments. …”
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