Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model by Jide Jia, Jianmin Mei, Chuang Sun, Fengjuan Yang

“…To eliminate the noise components, VMD was used to decompose the vibration signal into different frequency bands to obtain the intrinsic mode functions (IMFs), which were further represented by SDP images, and feature images were extracted according to the noncorrelation coefficient. …”
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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification. by Hong-Jie Liang, Ling-Long Li, Guang-Zhong Cao

“…Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. …”
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Instantaneous 2D extreme wind speed prediction using the novel Wind Gust Prediction Net based on purely convolutional neural mechanism by Zeguo Zhang, Jianchuan Yin

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Sequential Hybrid Integration of U-Net and Fully Convolutional Networks with Mask R-CNN for Enhanced Building Boundary Segmentation from Satellite Imagery by Rojgar Qarani Ismael, Haval Abduljabbar Sadeq

“…The present algorithms, such as Convolutional Neural Network (CNN) are unable to detect buildings in challenging urban areas like occlusions. …”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…Additionally, we present a newly collected multi-modal dataset, HumcareV1.0, comprising different activities in smart-home-like scenarios. …”
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CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection by Dikun Hu, Weidong Gao, Kai Keng Ang, Mengjiao Hu, Rong Huang, Gang Chuai, Xiaoyan Li

“…It employs a Movement Artifact and Rollover Identification (MARI) module to detect critical rollover events and extracts multi-scale spatiotemporal features using six sub-convolution networks with different-length adjacent segments. …”
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CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments by Naveed Ahmad, Mariam Akbar, Eman H. Alkhammash, Mona M. Jamjoom

“…By leveraging the global context understanding of ViTs and the local feature extraction capabilities of CNNs, the model learns a multi-scale attention mechanism that dynamically focuses on fire regions at different scales, thereby improving accuracy and robustness. …”
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Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks by Fatima Rauf, Muhammad Attique Khan, Hussain M. Albarakati, Kiran Jabeen, Shrooq Alsenan, Ameer Hamza, Sokea Teng, Yunyoung Nam

“…Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. …”
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Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network by Guilherme Macedo, Miguel José Mascarenhas Saraiva, João Afonso, Tiago Ribeiro, João Ferreira, Helder Cardoso, Ana Patricia Andrade, Marco Parente, Renato Natal, Miguel Mascarenhas Saraiva

“…CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…The binary segmentation models were trained using a high-resolution soybean crop dataset (733 images), which consisted of data from fifteen distinct locations in Canada, collected during different growth phases. LabelMe and albumentation tools were used to generate a segmentation dataset, followed by data augmentation techniques to enhance data generalization and robustness. …”
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Automated Loudness Growth Prediction From EEG Signals Using Autoencoder and Multi-Target Regression by D. Rama Harshita, Nitya Tiwari, Himanshu Padole, K. S. Nataraj

“…The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. An ablation study was conducted to analyze the impact of different autoencoder configurations on feature extraction performance. …”
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Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study by Valeria Sorgente, Dante Biagiucci, Mario Cesarelli, Luca Brunese, Antonella Santone, Fabio Martinelli, Francesco Mercaldo

“…Results: We evaluate our approach by exploiting six different datasets. We observe notable results, demonstrating the ability of Deep Convolutional GAN to generate realistic synthetic images for some specific bioimages. …”
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Rapid detection of acetolactate synthase inhibitor–resistant weeds using novel full-spectrum imaging and a hyperparameter-tuned convolutional neural network by Pauline Victoria Estrada, John Benedict Estrada, Jennifer Valdez-Herrera, Anil Shrestha

“…This novel approach exploits the subtle differences in the spectral signature of ALS inhibitor-resistant and ALS inhibitor-susceptible common chickweed plants as they react differently to the ALS-inhibiting herbicide treatments. …”
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Early detection of thermal image based T1 breast cancer using enhanced multiwavelet denoised convolution neural network with region based analysis by P. Geetha, S. UmaMaheswari

“…However, T1-stage cancer cells are smaller and have small temperature differences undetected with thermography. In this paper, a T1-stage cancer cell is heated by an external source; then, thermal images are acquired for earlier detection of small-size cancer cells. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…Conventional mammography methods still have some limitations in differentiating between healthy and tumorous tissue, particularly in cases with low-density differences. This study investigates the potential of combining ultrahigh-sensitivity Talbot-Lau interferometry with Convolutional Neural Networks (CNNs) to enhance breast tumor segmentation from scattering images. …”
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Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks by S. Süme, K.-M. Ponomarjova, T. M. Wendt, S. J. Rupitsch

“…In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). …”
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Novel non-destructive authentication of nine Dendrobium species using residual convolutional neural network relying on plant images and FT-NIR spectral information by Yulin Xu, Lian Li, Yuanzhong Wang, Qiang Hu

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Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models by Ali Mohammadjafari, Maohua Lin, Min Shi

“…This study aims to investigate the capability of deep learning approaches to detect glaucoma from clinical notes based on a real-world dataset including 10,000 patients. Different popular models are explored to predict the binary glaucomatous status defined from a comprehensive vision function assessment. …”
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Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data by Sheng Miao, Chuanlong Wang, Guangze Kong, Xiuhe Yuan, Xiang Shen, Chao Liu

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A GPSR-Based Sparse ECG Signal Recovery Approach for Hybrid Compressive Sensing Framework and Its Classification Using Deep Convolution Neural Network by Subramanyam Shashi Kumar, Prakash Ramachandran

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