Optimizing dental implant identification using deep learning leveraging artificial data by Shintaro Sukegawa, Kazumasa Yoshii, Takeshi Hara, Futa Tanaka, Yoshihiro Taki, Yuta Inoue, Katsusuke Yamashita, Fumi Nakai, Yasuhiro Nakai, Ryo Miyazaki, Takanori Ishihama, Minoru Miyake

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Review of models for estimating 3D human pose using deep learning by Sani Salisu, Kamaluddeen Usman Danyaro, Maged Nasser, Israa M. Hayder, Hussain A. Younis

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Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms by Reem A. Alzahrani, Malak Aljabri, Rami A. Mustafa Mohammad

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Efficient Monte Carlo simulation of streamer discharges with deep-learning denoising models by F M Bayo-Muñoz, A Malagón-Romero, A Luque

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Retracted: Deep Learning for Decision Element Extraction in Fact Description of Legal Documents by Advances in Multimedia

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Hardware Efficient Speech Enhancement With Noise Aware Multi-Target Deep Learning by Salinna Abdullah, Majid Zamani, Andreas Demosthenous

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Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks by Ajay. P, Nagaraj. B, Ruihang Huang

“…This study focuses on various deep learning strategies used in peer-to-peer communication networks. …”
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Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning by Richa Sharma, Anjali Thukral, Yatin Kapoor, Ashwani Varshney, Punam Bedi

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Clinically oriented automatic three-dimensional enamel segmentation via deep learning by Wenting Yu, Xinwen Wang, Huifang Yang

Subjects: “…Deep learning…”
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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

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Efficient and accurate identification of maize rust disease using deep learning model by Pei Wang, Pei Wang, Jiajia Tan, Yuheng Yang, Yuheng Yang, Tong Zhang, Pengxin Wu, Xinglong Tang, Hui Li, Xiongkui He, Xinping Chen

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Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT by Mateusz Koziński, Doruk Oner, Jakub Gwizdała, Catherine Beigelman-Aubry, Pascal Fua, Angela Koutsokera, Alessio Casutt, Argyro Vraka, Michele De Palma, John-David Aubert, Horst Bischof, Christophe von Garnier, Sahand Jamal Rahi, Martin Urschler, Nahal Mansouri

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Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data by Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, Jürgen Dammers

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Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment by Nan Zhao

“…There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. …”
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Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks by Chaoan Lai, Yina Wang, Jianhua Zhu, Xuequan Zhou

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Evaluation of Prediction Performances of Deep Learning Models for the Aerodynamic Characteristics of Flettner Rotors by Seo Janghoon, Park Jung Yoon, Ma Juhwan, Kim Young Bu, Park Dong-Woo

Subjects: “…deep learning model…”
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A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment by Jinyue Zhang, Lijun Zi, Yuexian Hou, Mingen Wang, Wenting Jiang, Da Deng

“…The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning-based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short-term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best-performing HAR approaches, namely, three-dimensional (3D) convolutional networks (ConvNets) and two-stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.…”
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Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) by Dongling Wu, Hongxiang Zhang, Yiying Yang

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Development of an autonomous chess robot system using computer vision and deep learning by Truong Duc Phuc, Bui Cao Son

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