CAPTCHA Recognition Method Based on CNN with Focal Loss by Zhong Wang, Peibei Shi

“…The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. …”
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Multiple-Feature Construction for Image Segmentation Based on Genetic Programming by David Herrera-Sánchez, José-Antonio Fuentes-Tomás, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, José-Luis Morales-Reyes

“…Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. …”
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Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model by Shaodi Dong, Zhao Tang, Xiaosong Yang, Michelle Wu, Jianjun Zhang, Tao Zhu, Shoune Xiao

“…Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. …”
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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning by Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng

“…The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. …”
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A Systematic Review of the Current State of Transfer Learning Accelerated CNN-Based Plant Leaf Disease Classification by David J. Richter, Md Ilias Bappi, Shivani Sanjay Kolekar, Kyungbaek Kim

“…This paper presents statistics on model, dataset, hyperparameter, pre-processing, augmentation, and metrics usage. Also, different methods for improving transfer-learning accelerated convolutional neural network models and training are listed. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. …”
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Hybrid face recognition under adverse conditions using appearance‐based and dynamic features of smile expression by Murat Taskiran, Nihan Kahraman, Cigdem Eroglu Erdem

“…We evaluated the performances of three different state‐of‐the‐art pre‐trained deep convolutional neural networks (DCNNs) under a variety of severe image distortions with different parameters. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…First, investigated the effects of different global information extraction methods on the experimental results; second, analyzed the effects of different modules on the network effects; third, explored the impact of different scales on network performance, sequential cascade structure, and rationalization of hierarchical feature fusion; and fourth, verified the robustness of the enhancement modules designed by testing them on different backbone networks. …”
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Method of Non-Destructive Control of Single-Phase and Three-Phase Transformers's Condition on the Basis of Frequency Characteristics by I. L. Hramyka, V. N. Galushko

“…Nowadays, there are many different methods of transformer diagnostics. The analysis of used methods and diagnostic systems indicates that a certain complexity of further development of existing methods and diagnostic systems has been achieved. …”
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Transfer of Periodic Phenomena in Multiphase Capillary Flows to a Quasi-Stationary Observation Using U-Net by Bastian Oldach, Philipp Wintermeyer, Norbert Kockmann

“…However, since very small dimensions are characterized by phenomena that differ from those at macroscopic scales, a deep understanding of physics is crucial for effective device design. …”
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A Latent Multi-Scale Residual Transformer Approach for Cross-Modal Medical Image Synthesis by Xinmiao Zhu, Yang Li

“…This module consists of two layers of residual convolutional blocks and transformer blocks of different scales, where the transformer blocks assist the convolutional blocks in capturing contextual features, and lower-level blocks support higher-level blocks in learning high-dimensional global information. …”
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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts by Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi

“…ABSTRACT The study suggests using a hybrid convolutional neural networks‐support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. …”
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Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography by Amged Elsheikh Abdelgadir Ali, Dai Owaki, Mitsuhiro Hayashibe

“…Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. …”
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Influence of Target Surface BRDF on Non-Line-of-Sight Imaging by Yufeng Yang, Kailei Yang, Ao Zhang

“…The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. …”
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Recognition of Suspension Liquid Based on Speckle Patterns Using Deep Learning by Jinhua Yan, Ming Jin, Zhousu Xu, Lei Chen, Ziheng Zhu, Hang Zhang

“…Further recognition from three different food suspensions with unknown concentration was achieved with high accuracy of 99%. …”
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Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning by Xiaoyun Lei, Zhian Zhang, Peifang Dong

“…The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space. In different training stages, we dynamically adjust the starting position and target position. …”
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Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

“…Classification of brain gliomas is also essential because different types require different treatment approaches. …”
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Multimode Flex-Interleaver Core for Baseband Processor Platform by Rizwan Asghar, Dake Liu

“…Algorithmic level optimizations like 2D transformation and realization of recursive computation are applied, which appear to be the key to reach to an efficient hardware multiplexing among different interleaver implementations. The presented hardware enables the mapping of vital types of interleavers including multiple block interleavers and convolutional interleaver onto a single architecture. …”
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Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder by Zheng Zhang, Weixiong Zhang, Yu Meng, Zhitao Zhao, Ping Tang, Hongyi Li

“…Secondly, IncepTAE adopts one-branch architecture, which reinforces the interaction and congruity of different temporal information. Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. …”
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Application of deconvolutional networks for feature interpretability in epilepsy detection by Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li

“…Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. …”
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