Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism by Jianjian Yang, Haifeng Han, Xuan Dong, Guoyong Wang, Shaocong Zhang

“…Furthermore, it innovatively introduces the Channel-Based Multi-Head Attention (CBMA) mechanism for the efficient fusion of features from different modalities, maximizing the complementarity between signals. …”
Get full text

Diagnosis of array antennas based on near-field data using Faster R-CNN by Boguang Yang, Yulun Wei, Jixiang Shi, Tao Hong, Liangyu Li, Kai-Da Xu

“…In this paper, a source reconstruction method for detecting failures in array antenna elements using near-field data based on Faster region-convolutional neural network (Faster R-CNN) is introduced. …”
Get full text

Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks by Haoyu Wang, Sheng Cui, Changjian Ke, Chenglong Yu, Zi Liang, Deming Liu

“…We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. …”
Get full text

Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism by Yahong Ma, Zhentao Huang, Yuyao Yang, Shanwen Zhang, Qi Dong, Rongrong Wang, Liangliang Hu

“…DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network’s performance. …”
Get full text

A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images by Sudhakar Sengan, Praveen Gugulothu, Roobaea Alroobaea, Julian L. Webber, Abolfazl Mehbodniya, Amr Yousef

“…Abstract Multi-Modal Medical Image Fusion (MMMIF) has become increasingly important in clinical applications, as it enables the integration of complementary information from different imaging modalities to support more accurate diagnosis and treatment planning. …”
Get full text

Advancements in Landmine Detection: Deep Learning-Based Analysis With Thermal Drones by Daniel Heuschmid, Oliver Wacker, Yannick Zimmermann, Pascal Penava, Ricardo Buettner

Get full text

3-D–2-D Hybrid Lightweight CNN Model: Enhancing Canopy Feature Retrieval in Hyperspectral Imaging for Accurate Plant Species Classification by Chinsu Lin, Hung-Yi Chien, Keng-Hao Liu

“…Deep learning (DL), particularly convolutional neural networks (CNNs), has been widely used to identify images of plant organs and canopies from various sensor-derived images. …”
Get full text

Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
Get full text

Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery by Giuliana Bilotta, Luigi Bibbò, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

Get full text

PDCNet: A Polarimetric Data-Enhanced Contrastive Learning Network for PolSAR Land Cover Classification by Bo Ren, Chaoyue Hua, Biao Hou, Jian Lv, Chen Yang, Licheng Jiao, Jocelyn Chanussot

“…Specifically, the encoder of PDCNet is designed as an extraction module for a real-convolutional composite complex convolutional network. …”
Get full text

Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

Get full text

Dataset Dependency in CNN-Based Copy-Move Forgery Detection: A Multi-Dataset Comparative Analysis by Potito Valle Dell’Olmo, Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Christian Napoli, Cristian Randieri

“…Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. …”
Get full text

Temporal Segment Method in Sign Word Recognition Using a Pretrained CNN-LSTM Network by Seungju Lee, Irina Polyakova

“…Experiments included a comparative analysis of different pretrained ResNet models (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152), resulting in the identification of the optimal configuration. …”
Get full text

Reconfigurable and Scalable Artificial Intelligence Acceleration Hardware Architecture With RISC-V CNN Coprocessor for Real-Time Seizure Detection by Shuenn-Yuh Lee, Ming-Yueh Ku, Sing-Yu Pan, Chou-Ching Lin

“…Thus, the accelerator can execute different deep-learning models to fit various wearable applications for biomedical acquisition systems.…”
Get full text

Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset by Ricardo Salvador Luna Lozoya, Humberto de Jesús Ochoa Domínguez, Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas, Karina Núñez Barragán

“…Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 <inline-formula><math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m. …”
Get full text

Novel deep learning for multi-class classification of Alzheimer’s in disability using MRI datasets by Sumaiya Binte Shahid, Maleeha Kaikaus, Md. Hasanul Kabir, Mohammad Abu Yousuf, A. K. M. Azad, A. S. Al-Moisheer, Naif Alotaibi, Salem A. Alyami, Touhid Bhuiyan, Mohammad Ali Moni, Mohammad Ali Moni, Mohammad Ali Moni

“…Next, by utilizing the modified ResNet152V2 as a feature extractor, a Convolutional Neural Network based model, namely, the ‘IncepRes’, is proposed by fusing the Inception and ResNet architectures for multiclass classification of AD categories. …”
Get full text

Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning by Qiyuan Tian, Chanon Ngamsombat, Hong-Hsi Lee, Daniel R. Berger, Yuelong Wu, Qiuyun Fan, Berkin Bilgic, Ziyu Li, Dmitry S. Novikov, Els Fieremans, Bruce R. Rosen, Jeff W. Lichtman, Susie Y. Huang

“…This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.…”
Get full text

Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill, Mark Christopher

“…Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. …”
Get full text

Field-level Comparison and Robustness Analysis of Cosmological N-body Simulations by Adrian E. Bayer, Francisco Villaescusa-Navarro, Sammy Sharief, Romain Teyssier, Lehman H. Garrison, Laurence Perreault-Levasseur, Greg L. Bryan, Marco Gatti, Eli Visbal

“…We follow this with a statistical out-of-distribution (OOD) analysis to quantify distributional differences between simulations, revealing insights not captured by the traditional metrics. …”
Get full text

Development of approach to an automated acquisition of static street view images using transformer architecture for analysis of Building characteristics by Seunghyeon Wang

Get full text