Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network by Zhiheng KONG, Chong TAN, Peiyao TANG, Chengbo HU, Min ZHENG

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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

“…Using convolutional neural networks (CNNs) to detect plant diseases has proven to reach high accuracy in the classification of infected and non-infected plant images. …”
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An enhanced light weight face liveness detection method using deep convolutional neural network by Swapnil R. Shinde, Anupkumar M. Bongale, Deepak Dharrao, Sudeep D. Thepade

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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers by Anupam Yadav, Rifat Hussain, Madhu Shukla, Jayaprakash B, Rishiv Kalia, S. Prince Mary, Chou-Yi Hsu, Manoj Kumar Mishra, Kashif Saleem, Mohammed El-Meligy

“…Despite minor overfitting, SPBO’s efficiency makes it a cost-effective solution. In general, our findings contribute to the advancement of driver monitoring systems and road safety while emphasizing the broader role of meta-heuristic techniques in deep learning optimization.…”
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Image-based yield prediction for tall fescue using random forests and convolutional neural networks by Sarah Ghysels, Bernard De Baets, Dirk Reheul, Steven Maenhout

“…The convolutional neural network outperformed the random forest method and exceeded the predictive power of the breeder’s eye. …”
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Hybrid Transformer–Convolutional Neural Network Approach for Non-Intrusive Load Analysis in Industrial Processes by Gengsheng He, Yu Huang, Ying Zhang, Yuanzhe Zhu, Yuan Leng, Nan Shang, Jincan Zeng, Zengxin Pu

“…Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…However, acquiring such images is challenging due to current technical limitations and cost constraints. Spatiotemporal fusion offers an effective and economical solution to achieve high spatial and temporal resolution simultaneously. …”
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Klasifikasi Kualitas Biji Kedelai Menggunakan Transfer Learning Convolutional Neural Network Dan SMOTE by Amanda Prawita Ningrum, Sri Winarno, Vincentius Praskatama

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A Novel ViT Model with Wavelet Convolution and SLAttention Modules for Underwater Acoustic Target Recognition by Haoran Guo, Biao Wang, Tao Fang, Biao Liu

“…By introducing the Wavelet Transform Convolution (WTConv) module and the Simplified Linear Attention (SLAttention) module, WS-ViT can effectively extract spatiotemporal complex features, enhance classification accuracy, and significantly reduce computational costs. …”
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Rolling Bearing Life Prediction Based on Improved Transformer Encoding Layer and Multi-Scale Convolution by Zhuopeng Luo, Zhihai Wang, Xiaoqin Liu, Yingming Yang

“…This approach offers a new solution for managing the fault risk of rotating machinery and reducing maintenance costs.…”
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Klasifikasi Penyakit pada Tanaman Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network by Denis Aji Pangestu, Okta Qomaruddin Aziz, Cahyo Crysdian

“…Plant disease identification typically requires experienced experts, but this process is time-consuming and costly. This research aims to develop a plant disease classification model using Convolutional Neural Network (CNN) to assist farmers in identifying diseases in rice, corn, tomato, and potato plants based on leaf images. …”
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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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A Novel Approach for Enhancing Code Smell Detection Using Random Convolutional Kernel Transform by Mostefai Abdelkader, Mekour Mansour

“… Context: In software engineering, the presence of code smells is closely associated with increased maintenance costs and complexities, making their detection and remediation an important concern. …”
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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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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.…”
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Computationally Efficient Single Layer Transformer Convolutional Encoder for Accurate Price Prediction of Agriculture Commodities by Caceja Elyca Anak Bundak, Mohd Amiruddin Abd Rahman, Nurin Syazwina Mohd Haniff, Nur Syaiful Afrizal, Khairul Adib Yusof, Muhammad Khalis Abdul Karim, Md Shuhazlly Mamat, Romi Fadillah Rahmat

“…To obtain accurate predictions, the process usually involves large and complex datasets, which would add to computational costs for developing a model with good performance. …”
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MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network by Xi QIAO, Yan-zhou LI, Guang-yuan SU, Hong-kun TIAN, Shuo ZHANG, Zhong-yu SUN, Long YANG, Fang-hao WAN, Wan-qiang QIAN

“…The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. …”
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Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…The model is trained end-to-end on 3D skeleton sequences and optimized for real-time efficiency. The computational cost is evaluated in terms of giga floating-point operations (GFLOPs), with the proposed model requiring only 6.2 GFLOPs per inference, over 60% less than the baseline ST-GCN. …”
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Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network by Atitaya Phoemsuk, Vahid Abolghasemi

“…The proposed method works based on a one-dimensional convolutional neural network (1D-CNN), offering a cost-effective alternative for sophisticated cardiac health monitoring. …”
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Bone Segmentation in Low-Field Knee MRI Using a Three-Dimensional Convolutional Neural Network by Ciro Listone, Diego Romano, Marco Lapegna

“…Low-field MRI offers advantages in cost, patient comfort, and accessibility but presents challenges related to lower signal quality. …”
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