Quantum Vision Theory in Deep Learning for Object Recognition by Cem Direkoglu, Melike Sah

“…Quantum-scale world looks different from our human-scale world. Attempts to relate the microscopic quantum world to our macroscopic world led to philosophical issues and questions. …”
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A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

“…This study proposes a vibration state prediction model based on Variational Mode Decomposition (VMD) and Temporal Convolutional Network (TCN), referred to as the VMD-TCN model. …”
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Automatic detection of developmental stages of molar teeth with deep learning by Ertuğrul Furkan Savaştaer, Berrin Çelik, Mahmut Emin Çelik

“…The stages of development of molar teeth were divided into 4 classes such as M1, M2, M3 and M4. 9 different convolutional neural network models, which were Cascade R-CNN, YOLOv3, Hybrid Task Cascade(HTC), DetectorRS, SSD, EfficientNet, NAS-FPN, Deformable DETR and Probabilistic Anchor Assignment(PAA), were used for automatic detection of these classes. …”
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A 3-dimension urban growth analysis using local climate zone mapping by Haojie Chen, Cheolhee Yoo, Jacqueline T. Y. Lo

“…This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. …”
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GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults by Proma Dutta, Kanchon Kanti Podder, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury, Amith Khandakar, Nasser Al-Emadi, Moajjem Hossain Chowdhury, M. Murugappan, Mohamed Arselene Ayari, Sakib Mahmud, S. M. Muyeen

“…This research presents GearFaultNet, a novel, lightweight 1D Convolutional Neural Network (CNN)-based network, designed to detect gearbox faults. …”
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Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning by Zihan Wang, Jinjin Wu, Chenbei Li, Bing Wang, Qingxia Wu, Lan Li, Huijie Wang, Chao Tu, Jianhua Yin

“…Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. …”
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Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest by Gulfem Ozlu Ucan, Omar Abboosh Hussein Gwassi, Burak Kerem Apaydin, Bahadir Ucan

“…<b>Methods:</b> Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. …”
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Global&#x2013;Local Multigranularity Transformer for Hyperspectral Image Classification by Zhe Meng, Qian Yan, Feng Zhao, Gaige Chen, Wenqiang Hua, Miaomiao Liang

“…Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. …”
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Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning by Antonio Pousibet-Garrido, Aurora Polo-Rodríguez, Juan Antonio Moreno-Pérez, Isidoro Ruiz-García, Pablo Escobedo, Nuria López-Ruiz, Noel Marcen-Cinca, Javier Medina-Quero, Miguel Ángel Carvajal

“…The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. …”
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A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data by Guobin Gu, Xin Sun, Benxiao Lou, Xiang Wang, Bingheng Yang, Jianqiu Chen, Dan Zhou, Shiqian Huang, Qingwei Hu, Chun Bao

“…The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. …”
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Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images by Long Gao, Liyuan Li, Jianing Yu, Xiaoxuan Zhou, Lu Zou, Nan Fang, Xiaofeng Su, Fansheng Chen

“…The results demonstrated SwinCloud's generalization capability across different bands.…”
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A method for intelligent information extraction of coal fractures based on µCT and deep learning by Zhazha HU, Xun ZHANG, Yi JIN, Linxian GONG, Wenhui HUANG, Jianji REN, Norbert Klitzsch

“…First, the µCT images of coal samples were preprocessed, including improving the image quality using the difference method and increasing the sample size using data augmentation techniques. …”
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MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task by Boshi Zheng, Jiabin Liu, Yunjie Li, Yan Li, Zhen Qin

“…However, recent research on adversarial examples focus on the vulnerability of convolutional neural network (CNN) models, while the attack on transformer models has not been extensively studied. …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…Then, a novel Multi-Scale Feature Aggregation (MSFA) layer is added to enhance feature extraction across various scales, effectively capturing more comprehensive features and fusing feature maps from different scales to improve final feature reuse. Lastly, a newly designed Enhanced Convolutional Block Attention Module (ECBAM) increases the model&#x2019;s focus on important information, allowing for the final extraction and summarization of features. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Quantification of CO₂ hotspot emissions from OCO-3 SAM CO₂ satellite images using deep learning methods by J. Dumont Le Brazidec, J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, G. Broquet, G. Kuhlmann, M. Bocquet

“…The results are very promising, showing a relative difference in the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. …”
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GaitRGA: Gait Recognition Based on Relation-Aware Global Attention by Jinhang Liu, Yunfan Ke, Ting Zhou, Yan Qiu, Chunzhi Wang

“…The major challenges in gait recognition include changes in viewing angle, occlusion, clothing changes, and significant differences in gait characteristics under different walking conditions. …”
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Deep learning-based accurate detection of insects and damage in cruciferous crops using YOLOv5 by Sourav Chakrabarty, Pathour Rajendra Shashank, Chandan Kumar Deb, Md. Ashraful Haque, Pradyuman Thakur, Deeba Kamil, Sudeep Marwaha, Mukesh Kumar Dhillon

“…A total of 2,730 images were captured from different fields and polyhouses using different smartphones and an SLR camera. …”
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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

“…The approach uses a convolutional neural network to automatically detect wood. …”
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