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

“…These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. By experimenting with different combinations of multi-parameters with the improved U-Net architectures, we estimate CC and validate the results using airborne Light Detection and Ranging (LiDAR) CC data. …”
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Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet by Carlos Javier Morales-Perez, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez, Martin Valtierra-Rodriguez

“…This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. …”
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Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction by ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang

“…First, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. …”
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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. …”
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Efficient neural network training method for unsteady flow field prediction based on data pool by Qiuchi Min, Tianyu Li, Guanxiong Li, Shuyuan Liu, Laiping Zhang, Xiaogang Deng

“…A detailed comparative study was conducted on different training methods within the architecture of convolutional neural networks. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. …”
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Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. …”
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A Simple but Effective Way to Handle Rotating Machine Fault Diagnosis With Imbalanced-Class Data: Repetitive Learning Using an Advanced Domain Adaptation Model by Donghwi Yoo, Minseok Choi, Hyunseok Oh, Bongtae Han

“…By employing pseudo-labeling, weighted random sampling, and time-shifting, the proposed repetitive learning method generates pseudo-augmented source and target fault data. Deep convolutional domain adaptation networks are followed to extract features by minimizing different losses. …”
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A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Information by Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

“…The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability to perceive local features through residual convolutional blocks and squeeze-and-excitation (SE) mechanisms. …”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. …”
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Citrus Disease Classification Model Based on Improved ConvNeXt by Jichi Yan, Yongbin Mo, Yannan Yu, Shiqing Dou, Rongfeng Yang

“…Secondly, the multi-scale feature fusion module is incorporated to improve the model’s adaptability to disease features at different scales and improve the network classification performance. …”
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Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique by Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

“…This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. …”
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A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition by Qingjun Song, Shirong Sun, Qinghui Song, Bingrui Wang, Zihao Liu, Haiyan Jiang

“…To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals. …”
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Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images by Peter Sykora, Patrik Kamencay, Roberta Hlavata, Robert Hudec

“…In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. …”
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Impact of Channel and System Parameters on Performance Evaluation of Frequency Extrapolation Using Machine Learning by Michael Neuman, Daoud Burghal, Andreas F. Molisch

“…This paper presents an extensive investigation of the impact of various parameters, both of the system and of the underlying channels, on the performance of three types of ML algorithms, namely K-nearest neighbor (KNN), convolutional multilayer perceptron (CNN/MLP), and autoencoder structures (AE). …”
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Leveraging deep feature learning for handwriting biometric authentication by Parvaneh Afzali, Abdoreza Rezapour, Ahmad Rezaee Jordehi

“…In this paper, we propose a Customized Siamese Convolutional Neural Network (CSCNN) for offline writer verification based on handwritten words. …”
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Combined CNN-LSTM for Enhancing Clean and Noisy Speech Recognition by Noussaiba Djeffal, Djamel Addou, Hamza Kheddar, Sid Ahmed Selouani

“… This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) approach for Automatic Speech Recognition (ASR) using deep learning techniques on the Aurora-2 dataset. …”
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Bearing Fault Diagnosis in Induction Motors Using Low-Cost Triaxial ADXL355 Accelerometer and a Hybrid CWT-DCNN-LSTM Model by Muhammad Ahsan, Jose Rodriguez, Mohamed Abdelrahem

“…This paper presents a novel approach for bearing fault diagnosis in induction motor utilizing an improved hybrid Continuous Wavelet Transform-Deep Convolutional Neural Network-Long Short-Term Memory (CWT-DCNN-LSTM) model. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”
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MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion by Mustaqeem Khan, Phuong-Nam Tran, Nhat Truong Pham, Abdulmotaleb El Saddik, Alice Othmani

“…However, these models come at a computational cost. In contrast, convolutional neural networks are faster but struggle with capturing these long-range relationships. …”
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