Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model by Liang Guo, Yuxin Dong, Deyong Zhang, Xinrong Pan, Xinjie Jin, Xinyu Yan, Yin Lu

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Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm by Manivannan Ragupathi, Rengaraj Ramasubbu

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Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials by Dario Milone, Dario Santonocito

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Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems by Ming Yang, Hao Ma, Bomin Chen, Guangtao Dong

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Research progress of deep learning-based object detection of optical remote sensing image by Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI

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The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning by Chuan Mou

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A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning by Zhongshan XIAO, Chunqi WANG, Daquan FENG

“…Cooperative spectrum sensing is a key technology in cognitive radio.A data-driven intelligent cooperative spectrum sensing system was proposed with spatial diversity running on a smart contract to address issues of security, privacy, incentive and hidden terminals in cooperative spectrum sensing.Specifically, a motivated spectrum sensing system was designed by taking advantage of the decentralization of blockchain technology and the immutability of data.Secondly, a deep learning-based approach was proposed to identify malicious users in the system.In addition, to achieve higher accuracy in recruiting sensing nodes more efficiently in the system, a hard decision cooperative spectrum sensing fusion algorithm based on performance weights and spatial diversity was designed.The experimental results indicate that the proposed solution outperforms traditional cooperative spectrum sensing algorithms in terms of security, privacy, motivation, and sensing accuracy.…”
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Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams by Ammar Akram Abdulrazzaq, Asaad T. Al-Douri, Abdulsattar Abdullah Hamad, Mustafa Musa Jaber, Zelalem Meraf

“…In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). …”
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Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning by Maryam Viqar, Erdem Sahin, Elena Stoykova, Violeta Madjarova

“…We propose a streamlined and computationally efficient approach based on Deep Learning (DL) which enables reconstructing speckle-reduced OCT images directly from the wavelength (λ) domain. …”
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Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma by Laurens S. Ter Maat, Rob A. J. De Mooij, Isabella A. J. Van Duin, Joost J. C. Verhoeff, Sjoerd G. Elias, Tim Leiner, Wouter A. C. van Amsterdam, Max F. Troenokarso, Eran R. A. N. Arntz, Franchette W. P. J. Van den Berkmortel, Marye J. Boers-Sonderen, Martijn F. Boomsma, Fons J. M. Van den Eertwegh, Jan Willem de Groot, Geke A. P. Hospers, Djura Piersma, Art Vreugdenhil, Hans M. Westgeest, Ellen Kapiteijn, Ardine A. De Wit, Willeke A. M. Blokx, Paul J. Van Diest, Pim A. De Jong, Josien P. W. Pluim, Karijn P. M. Suijkerbuijk, Mitko Veta

“…The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. …”
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Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data by N.V. Prajna, MD, Jad Assaf, MD, Nisha R. Acharya, MD, MS, Jennifer Rose-Nussbaumer, MD, Thomas M. Lietman, MD, J. Peter Campbell, MD, MPH, Jeremy D. Keenan, MD, MPH, Xubo Song, PhD, Travis K. Redd, MD, MPH

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Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method by Xiangyu Ge, Xia Zhu, Gang Bi, Hao Zheng, Qing Li

“…In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. …”
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Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques by Marwa K. Elteir

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A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning by Zhen Yuan, Jinfeng Liu

“…With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. …”
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Deep learning‐based unmanned aerial vehicle detection in the low altitude clutter background by Zeyang Wu, Yuexing Peng, Wenbo Wang

Subjects: “…deep learning…”
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Key technology research and model validation of text classification system based on deep learning by Shaomin WANG, Di YANG, Hua REN

Subjects: “…deep learning…”
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Solar photovoltaic panel cells defects classification using deep learning ensemble methods by H. Tella, A. Hussein, S. Rehman, B. Liu, A. Balghonaim, M. Mohandes

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A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors by Lucia Palazzo, Vladimiro Suglia, Sabrina Grieco, Domenico Buongiorno, Antonio Brunetti, Leonarda Carnimeo, Federica Amitrano, Armando Coccia, Gaetano Pagano, Giovanni D’Addio, Vitoantonio Bevilacqua

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Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends by Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke

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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

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