A deep learning based multiple RNA methylation sites prediction across species by Sajid Shah, Saima Jabeen, Mohammed ElAffendi, Ishrat Khan, Muhammad Almas Anjum, Mohamed A. Bahloul

“…Secondly, this work investigates the effect of different encoding techniques on model performance, including one-hot encoding, Gene2Vec, and position encoding, as well as their combinations using concatenation, summation, and multiplication. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

“…In our case study in central France with 10 tree species, we obtained an overall accuracy (OA) of around 95% and an F1-macro score of around 80% using three different benchmark DL architectures (fully connected, convolutional, and attention-based networks). …”
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3-D UXSE-Net for Seismic Channel Detection Based on Satellite Image Enhanced Synthetic Datasets by Xinke Zhang, Yihuai Lou, Naihao Liu, Daosheng Ling, Yunmin Chen

“…Our results show that 3-D UXSE-Net outperforms baseline methods, including the coherence-based approach and other DL models, and demonstrates strong generalization to field data even when trained solely on synthetic data. Comparisons of different methods highlight the effectiveness of the synthetic data generation approach for DL model training.…”
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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating by Meng Zhang, Renjian Li, Songnian Fu, Sunil Kumar, James Mcginty, Yuwen Qin, Lingling Chen

“…With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM (LS-LSFM) and conventional LSFM, this method achieves a three- to five-fold signal-to-noise ratio (SNR) improvement and ~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D (3D morphology + time) imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.…”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…We verified the performance of the proposed and known neural networks on different optimizers. It is shown that the proposed neural network accelerates the solving object detection problem by 44–52%. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This research enhances the precision of DR diagnosis by applying it to different publicly accessible datasets. It contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. …”
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The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, e... by Ni Geng, Guiliang Tian, Hengquan Zhang

“…Secondly, Grey relational analysis and obstacle degree (OD) model were developed to identify the major factors affecting the coordinated development of the FES system. Finally, the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was selected to predict the CCD under different scenarios. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

“…The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

“…The model demonstrates robust performance across different forecast horizons, particularly suitable for short-term predictions of 1-3 months. …”
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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

“…Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. …”
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Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach by Antonio Santo, Enrique Heredia, Carlos Viegas, David Valiente, Arturo Gil

“…Evaluated in structured (SemanticKITTI) and unstructured (Rellis-3D) environments, two different versions of the proposed method are studied, including a purely geometric method and a hybrid approach that exploits deep learning techniques. …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.…”
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TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion by Lei He, Haijun Wei, Cunxun Sun

“…Furthermore, multi-scale feature maps extracted by the backbone network are fed into the bidirectional feature pyramid network (BiFPN) for feature fusion to enhance the model’s ability to detect wear particle feature maps at different scales. Lastly, Ghost modules are introduced into both the backbone network and the neck network to reduce their complexity and improve detection speed. …”
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Mapping stains on flat roofs using semantic segmentation based on deep learning by Lara Monalisa Alves dos Santos, Leonardo Rabero Lescano, Gabriel Toshio Hirokawa Higa, Vanda Alice Garcia Zanoni, Lenildo Santos da Silva, Cesar Ivan Alvarez, Hemerson Pistori

“…During inspections of roofing systems, an inspector's field of vision differs from that of drones during overflights. As a result, traditional inspections might not always detect the presence and severity of stains, making maintenance on flat roofs a complex task. …”
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KANDiff: Kolmogorov–Arnold Network and Diffusion Model-Based Network for Hyperspectral and Multispectral Image Fusion by Wei Li, Lu Li, Man Peng, Ran Tao

“…To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. …”
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Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm by ZHU Yumin, SUN Guangling, MIAO Fei

“…The feature information of different scales is extracted through DWConv with different convolution kernels, and the CA and SA modules are combined to enhance the model’s feature expression ability. …”
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Classification of Satellite Image Time Series and Aerial Images Based on Multiscale Fusion and Multilevel Supervision by H. Kanyamahanga, M. Dorozynski, F. Rottensteiner

“…In this context, it is a challenge to train a classifier given the large difference in resolutions. We utilise convolutions to extract spatial information and consider self-attention in the temporal dimension for SITS. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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