A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.…”
Get full text

Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…The transferability of these models across different regions remains a considerable challenge. …”
Get full text

Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data by Jian Liu, Xinzheng Xue, Pengbo Wen, Qian Song, Jun Yao, Shuguang Ge

“…SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. …”
Get full text

FSBNet: A Classifying Framework of Disaster Scene for Volcanic Lithology Through Deep-Learning Models by Lan Liu, Zhouyi Xiao, Jianpeng Hu, Jingxin Han, Jung Yoon Kim, Rohit Sharma, Chengfan Li

“…Specifically, we first visualize and recalculate the weights of volcanic lithology features in different channels using SE attention to enhance the network’s sensitivity for extracting key semantic features in disaster scenes. …”
Get full text

Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes by Carlos Toro, Walter Díaz, Gonzalo Reyes, Miguel Peña, Nicolás Caselli, Carla Taramasco, Pablo Ormeño-Arriagada, Eduardo Balladares

“…A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. …”
Get full text

Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN by Muhammad Farooq Siddique, Wasim Zaman, Saif Ullah, Muhammad Umar, Faisal Saleem, Dongkoo Shon, Tae Hyun Yoon, Dae-Seung Yoo, Jong-Myon Kim

“…These scalograms are subsequently fed into an autoencoder comprising convolutional and pooling layers to extract robust features. …”
Get full text

Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures by Diana Propst, Wael Joudi, Manuel Längle, Jacob Madsen, Clara Kofler, Barbara M. Mayer, David Lamprecht, Clemens Mangler, Lado Filipovic, Toma Susi, Jani Kotakoski

“…Abstract Defect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. …”
Get full text

Enhancing chronic wound assessment through agreement analysis and tissue segmentation by Ana C. Morgado, Rafaela Carvalho, Ana Filipa Sampaio, Maria J. M. Vasconcelos

“…Furthermore, the potential of transferring knowledge from open wound segmentation models trained on different available datasets and fine-tuning them for this specific task was investigated. …”
Get full text

Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…These objects often exhibit variations in scale, intricate interclass relationships, and are dispersed across different locations. These factors make it difficult to balance the contributions of different elements, such as bounding box centers and backgrounds during distillation. …”
Get full text

MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 by Xupeng Yin, Zikai Zhao, Liguo Weng

“…However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. …”
Get full text

Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network by Wenxuan Ge, Qinghui Mao, Wei Zhou, Zhixian Gui, Peng Wang

“…During the downhole microseismic monitoring for hydraulic fracturing, microseismic signals are constantly vulnerable to interference from different kinds of noise. Improving the signal-to-noise ratio of microseismic records is always beneficial for processing and interpreting microseismic data. …”
Get full text

Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…Results show that the hierarchical prediction method is an effective means of extracting flood features by addressing the variability of prediction parameters for different flood magnitudes. The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. …”
Get full text

ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset by Masum Shah Junayed, Md Baharul Islam, Afsana Ahsan Jeny, Arezoo Sadeghzadeh, Topu Biswas, A. F. M. Shahen Shah

“…In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. …”
Get full text

Exploring deep learning for landslide mapping: A comprehensive review by Zhi-qiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao

“…This study analyzed the structures of different DL networks, discussed five main application scenarios, and assessed both the advancements and limitations of DL in geological hazard analysis. …”
Get full text

An Efficient Aerial Image Detection with Variable Receptive Fields by Wenbin Liu, Liangren Shi, Guocheng An

“…This article presents VRF-DETR, a lightweight real-time object detection framework for aerial remote sensing images, aimed at addressing the challenge of insufficient receptive fields for easily confused categories due to differences in height and perspective. Based on the RT-DETR architecture, our approach introduces three key innovations: the multi-scale receptive field adaptive fusion (MSRF²) module replaces the Transformer encoder with parallel dilated convolutions and spatial-channel attention to adjust receptive fields for confusing objects dynamically; the gated multi-scale context (GMSC) block reconstructs the backbone using Gated Multi-Scale Context units with attention-gated convolution (AGConv), reducing parameters while enhancing multi-scale feature extraction; and the context-guided fusion (CGF) module optimizes feature fusion via context-guided weighting to resolve multi-scale semantic conflicts. …”
Get full text

Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images by Yi-Ching Cheng, Yi-Chieh Hung, Guan-Hua Huang, Tai-Been Chen, Nan-Han Lu, Kuo-Ying Liu, Kuo-Hsuan Lin

“…Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. …”
Get full text

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment by Anna Machrowska, Robert Karpiński, Marcin Maciejewski, Józef Jonak, Przemysław Krakowski, Arkadiusz Syta

“…The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. …”
Get full text

Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. …”
Get full text

Myocardial Iron Overload Assessment with Automatic Segmentation of Cardiac MR Images based on Deep Neural Networks by Mohamad Amin Bakhshali, Maryam Gholizadeh, Parvaneh Layegh, Saeid Eslami

“…Automatic LV segmentation was implemented with U-Net, an automatically adapted deep convolutional neural network based on U-Net. With the signal intensity of the LV segmented area, T2* value can be calculated at different echo times, a widely used and approved method to assess myocardial iron overload. …”
Get full text

Open-Circuit Fault Diagnosis Method of Energy Storage Converter Based on MFCC Feature Set by Bin YU, Xingrong SONG, Ting ZHOU, Linbo LUO, Hui LI, Liang CHE

“…Secondly, a fault state diagnosis model based on the Bayesian optimization algorithm (BOA) and one-dimensional convolutional neural network (CNN-1D) is constructed with a low-dimensional fault feature set as an input. …”
Get full text