Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance by Ren-Yi Kung, Nai-Hsin Pan, Charles C.N. Wang, Pin-Chan Lee

“…However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). …”
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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

“…A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. …”
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Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data by Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

“…Methods: High-dimensional metering data from a city energy provider is processed using a Convolutional Autoencoder (CAE) to extract deep features and reduce dimensionality. …”
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Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…However, current models for automatic detection can learn new faults at the cost of forgetting concepts previously learned. This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. …”
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Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods by Da Huang, Akram Al-Hourani

“…Accordingly, we utilize the generative adversarial network (GAN) and convolutional neural network (CNN) for spoof detection and authentication. …”
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A spatial scene reconstruction framework in emergency response scenario by Nan Zheng, Danhuai Guo

“…Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. …”
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A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition by Zixiang Ma, Bang Wu, Stefan Poslad

“…First, an access point selection based on a genetic algorithm is applied to reduce the positioning computational cost and increase the positioning accuracy. Second, Kendall tau correlation coefficient and a convolutional neural network are applied to extract the ranking features for estimating locations. …”
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Efficient slice anomaly detection network for 3D brain MRI Volume. by Zeduo Zhang, Yalda Mohsenzadeh

“…Especially for 3D brain MRI data, all the state-of-the-art models are reconstruction-based with 3D convolutional neural networks which are memory-intensive, time-consuming and producing noisy outputs that require further post-processing. …”
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Data-Enabled Intelligence in Complex Industrial Systems Cross-Model Transformer Method for Medical Image Synthesis by Zebin Hu, Hao Liu, Zhendong Li, Zekuan Yu

“…However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. …”
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AI-Driven Non-Invasive Diagnosis of Diverse Medical Conditions Through Nail Image Analysis with High-Performance Ensemble Classifier by Abeer Alshiha, Wai Woo

“…The aim was to establish a non-invasive, automatic diagnosis tool for different nail conditions, utilizing deep convolutional neural networks (CNNs) for feature extraction. …”
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Multi-scale feature pyramid network with bidirectional attention for efficient mural image classification. by Shulan Wang, Siyu Liu, Mengting Jin, Pingmei Fan

“…This study provides a cost-effective solution for large-scale mural digitization in resource-constrained environments.…”
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A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

“…Convolutional neural networks (CNNs) have evolved into essential components for a wide range of embedded applications due to their outstanding efficiency and performance. …”
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Explainable artificial intelligence to diagnose early Parkinson’s disease via voice analysis by Matthew Shen, Pouria Mortezaagha, Arya Rahgozar

“…We applied a hybrid model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Multiple Kernel Learning (MKL), and Multilayer Perceptron (MLP) to a dataset of 81 voice recordings. …”
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MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…Abstract The UNet architecture, based on convolutional neural networks (CNN), has demonstrated its remarkable performance in medical image analysis. …”
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ILViT: An Inception-Linear Attention-Based Lightweight Vision Transformer for Microscopic Cell Classification by Zhangda Liu, Panpan Wu, Ziping Zhao, Hengyong Yu

“…DIC combines dynamic and Inception-style convolutions to replace large kernels with fewer parameters. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

“…Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…Coral reefs, among the most vulnerable ecosystems, traditionally employ monitoring techniques that are labor-intensive and costly, prompting the exploration of remote sensing as a cost-effective alternative. …”
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MRTMD: A Multi-Resolution Dataset for Evaluating Object Detection in Traffic Monitoring Systems by Mark Bugeja, Matthias Bartolo, Matthew Montebello, Dylan Seychell

“…Real-time flow tracking, anomaly detection, and efficient management are key. Convolutional Neural Networks (CNNs) have become integral due to their compact size and easy deployment. …”
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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity by Abdulrahman Alzahrani, Patty Kostkova, Hamoud Alshammari, Safa Habibullah, Ahmed Alzahrani

“…The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. …”
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