A Contrastive Representation Learning Method for Event Classification in Φ-OTDR Systems by Tong Zhang, Xinjie Peng, Yifan Liu, Kaiyang Yin, Pengfei Li

“…Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. …”
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A Power Monitor System Cybersecurity Alarm-Tracing Method Based on Knowledge Graph and GCNN by Tianhao Ma, Juan Yu, Binquan Wang, Maosheng Gao, Zhifang Yang, Yajie Li, Mao Fan

“…Then, a GCNN with attention mechanisms is applied to sufficiently extract the topological features along alarms in KG so that it can precisely and effectively trace the massive alarms. Most importantly, to mitigate the influence of imbalanced alarms for tracing, a specialized data process and model ensemble strategy by adaptively weighted imbalance sample is proposed. …”
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Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning by Anand Shankar, Samarendra Dandapat, Shovan Barma

“…In addition, the <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.…”
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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

“…It was found, from the results, that the sagittal angles of hip and knee, and front angles of FPA and knee, provide the most discriminating results with accuracy above 94% between healthy and HOA 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

“…Our method automatically assesses contact patch complexity based on parameters associated with the object’s mesh to determine the most appropriate modeling technique by still ensuring accurate deformation simulation. …”
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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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Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection by Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas

“…Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic precision. …”
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Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation by Wanyu Wei, Xinsha Fu, Siqi Ma, Yaqiao Zhu, Ning Lu

“…Abstract Most state‐of‐the‐art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra‐dataset. …”
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Deep Learning for Automated Kellgren–Lawrence Grading in Knee Osteoarthritis Severity Assessment by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

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