Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

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Improving YOLOv11 for marine water quality monitoring and pollution source identification by Fang Wang

“…Additionally, Multi-scale Feature Fusion (MFF) combines Convolutional Neural Networks (CNN) and Transformer-based feature extraction to enhance robustness in complex environments. …”
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METHOD FOR EVALUATING EXCAVATION SHORING DESIGN CONCEPTS by Yu. G. Zheglova, B. P. Titarenko

“…The study is based on dynamic programming methods based on the principle of Bellman optimality. Logical convolution matrices are applied according to the dichotomy method.Results. …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

“…Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. …”
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Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques by Farid Amirouche, Aashik Mathew Prosper, Majd Mzeihem

“…Methods We enhanced the YOLOv8-AM model to improve pediatric wrist fracture detection, maintaining the YOLOv8 backbone while integrating attention mechanisms such as the Convolutional Block Attention Module (CBAM) and the Global Context (GC) block. …”
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Physical bases for determination of scattering kernels from incomplete data in grid-less X-ray imaging by A. Yu. Danyk, O. O. Sudakov

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Early detection of Wheat Stripe Mosaic Virus using multispectral imaging with deep-learning by Malithi De Silva, Dane Brown

“…Further, the K590 filter showed the most significant precision values with most of the tested Convolutional Neural Networks, Vision Transformers, and hybrid and Swin Transformer models. …”
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Algorithms for Railway Embedded Control Devices for Safety Manoeuvres by Beinaroviča Anna, Gorobetz Mikhail, Alps Ivars

“…The authors propose an algorithm for the traffic light recognition by using a convolutional neural network (CNN) and traffic light indicator recognition. …”
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CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

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A Review of Vehicle Accident Detection and Notification Systems Based on Machine Learning Techniques by Duaa Hadi Nassar, Jamal Mustafa Al-Tuwaijari

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An Optimized 1-D CNN-LSTM Approach for Fault Diagnosis of Rolling Bearings Considering Epistemic Uncertainty by Onur Can Kalay

“…Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. …”
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Target recognition in diverse synthetic aperture radar image datasets with low size weight and power processing hardware by Richard O. Lane, Wendy J. Holmes, Timothy Lamont‐Smith

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Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty by Roel Pantonial, Milan Simic

“…Based on the human gait cycle’s features, a hybrid Long Short-Term Memory–Convolutional Neural Network (HLSTM-CNN) is designed for the classification of healthy/HOA/THA gaits. …”
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FMCNN: Raw-Data Type Identification Using Feature Matrix and CNN by Eunsu Lee, Hoon Yoo

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A hybrid elastic-hyperelastic approach for simulating soft tactile sensors by Berith Atemoztli De la Cruz Sánchez, Jean-Philippe Roberge

“…To address this, we propose a hybrid approach that combines elastic and hyperelastic finite element simulations, complemented by convolutional neural networks (CNNs), to generate synthetic tactile maps of a soft capacitive tactile sensor. …”
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Application of Seq2Seq models for predicting the development of thunderstorm activity to enhance the pilot’s situational awareness in flight by G. V. Kovalenko, I. A. Yadrov

“…The results showed that convolutional recurrent neural networks (ConvRNN, ConvLSTM, ConvGRU) outperform classical recurrent models and improve the thunderstorm forecast by 25–30% in terms of RMSE (root mean square error) metric compared to the baseline model, which always selects the most recent radar image available at the time of prediction. …”
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Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets by Megha Sunil Borse, Murali Prasad R, Tummala Ranga Babu

“…Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology. …”
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Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems by Viktoria Afxentiou, Tanya Vladimirova

“…This paper introduces a novel evaluation methodology for classifying AWC images using Convolutional Neural Network (CNN) models, with the goal of assessing their effectiveness for use in ADSs. …”
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Adaptive Weighted CNN Features Integration for Correlation Filter Tracking by Chunbao Li, Bo Yang

“…Most existing CNN-based trackers track the object by leveraging high-level semantic features of the highest convolutional layer, which may lead to low-spatial resolution feature maps and degrade the localization precision of tracking. …”
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TFCNet: A Hybrid Architecture for Multi-Task Restoration of Complex Underwater Optical Images by Shengya Zhao, Xiufen Ye, Xinkui Mei, Shuxiang Guo, Haibin Qi

“…TFCNet combines the benefits of the Transformer in capturing long-range dependencies with the local feature extraction potential of convolutional neural networks, resulting in enhanced restoration results. …”
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