Foreign Object Detection on Insulators Based on Improved YOLO v3 by Huankun ZHANG, Junyi LI, Bin ZHANG

“…In addition, we amplify the training set to improve the training effect of the network and propose a wrong detection cost function to measure the risk of false detection. …”
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Portable motorized telescope system for wind turbine blades damage detection by Alejandro Carnero, Cristian Martín, Manuel Díaz

“…Abstract Wind turbines are among the fastest‐growing sources of energy production and the maintenance operations include regular inspection of their blades, causing considerable downtime and cost. In addition, the manual inspection process involves a great risk. …”
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Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data by Jiazhi Dai, Mario Rotea, Nasser Kehtarnavaz

“…This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating.…”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…ObjectiveRecently, the identification of cracks in river levees primarily relies on manual inspections, which suffer from low efficiency, high costs, and significant safety risks. Although deep learning technologies have made notable progress in enhancing detection automation, several challenges remain, including insufficient detection accuracy, poor adaptability, and a lack of precise localization capabilities. …”
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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. …”
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Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification by Ming Sun, Jie Zhang, Xiaoou He, Yihe Zhong

“…The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…The model incorporates an enhanced backbone architecture that integrates the lightweight Shuffle_Block algorithm and the Multi-Scale Dilated Attention (MSDA) mechanism, enabling effective small object feature extraction while significantly reducing parameter size and computational cost without compromising detection accuracy. Additionally, a lightweight inverted bottleneck structure, C2f_UIB, along with the GhostConv module, replaces the conventional C2f and standard convolutional layers. …”
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An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Secondly, a Diverse Branch Block (DBB) is embedded in the feature extraction–fusion module, which enriches the feature space by enhancing the representation capability of single-convolution operations, thereby improving model feature extraction performance without increasing inference time costs. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB.…”
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Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery by Lei Chen, TingTing Yang, ZhiQiang Wu, XinLong Li, YanZhen Lin, Yi Lian

“…This study integrates multispectral imagery with enhanced U-Net models (U-Net, U-Net++, U-Net3+) to achieve cost-effective large-scale CC estimation. These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. …”
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Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction by Naif Almusallam, Farman Ali, Harish Kumar, Tamim Alkhalifah, Fahad Alturise, Abdullah Almuhaimeed

“…However, the detection of FGF remains challenging due to the high cost and time required by traditional methods. To address this issue, we propose the first sequence-based computational method, FGF-MERCNN, for identifying FGFs using deep learning. …”
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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…This shows that the proposed approach can make fast decisions with low-cost computation and high accuracy.…”
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Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network by Min Pang, Erhu Du, Chunmiao Zheng

“…However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational cost, and inability to retain longer-term dependencies. …”
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AI-Based Point Cloud Upsampling for Autonomous Driving Systems by Nicolás Salomón, Claudio A. Delrieux, Damián A. Morero, Leandro E. Borgnino

“…This highlights the viability and scalability of our approach in realizing cost-effective yet high-performance autonomous driving systems. …”
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Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes. by Ellen L White, Paul R White, Jonathan M Bull, Denise Risch, Susanna Quer, Suzanne Beck

“…There is an increasing reliance on passive acoustic monitoring (PAM) as a cost-effective method for monitoring cetaceans, necessitating robust and efficient automated tools for extracting species presence. …”
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A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare by Yehia Ibrahim Alzoubi, Ahmet E. Topcu, Ersin Elbasi

“…A comprehensive performance analysis is presented across five dimensions: energy consumption, latency, accuracy, complexity, and cost. The review highlights mLZW as promising for energy efficiency, complexity, and cost, OFA for low-latency deployment, and Hybrid Quantum Classical Optimization for diagnostic accuracy. …”
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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. …”
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Enhancing energy consumption forecasting for electric vehicle charging stations with Time Series Dense Encoder (TiDE) by Amril Nazir, Abdul Khalique Shaikh, Aftab Ahmed Khan, Abdul Salam Shah, Nadia Khalique

“…The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the Temporal Convolutional Network. However, they still need help with issues, specifically the higher computation cost and larger dataset for the training process. …”
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