Deep(er) reconstruction of imaging Cherenkov detectors with swin transformers and normalizing flow models by C Fanelli, J Giroux, J Stevens

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Deep learning for enhanced risk management: a novel approach to analyzing financial reports by Xiangting Shi, Yakang Zhang, Manning Yu, Lihao Zhang

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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

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Experimental assessment of аdversarial attacks to the deep neural networks in medical image recognition by D. M. Voynov, V. A. Kovalev

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A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction by Weili Zeng, Juan Li, Zhibin Quan, Xiaobo Lu

“…To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. …”
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Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments by Estrella Montero, Nabih Pico, Mitra Ghergherehchi, Ho Seung Song

“…The proposed method takes the relative states of humans within a limited FoV and sensor range as input into the neural network. …”
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Sonographic and Clinical Features of Upper Extremity Deep Venous Thrombosis in Critical Care Patients by Michael Blaivas, Konstantinos Stefanidis, Serafim Nanas, John Poularas, Mitchell Wachtel, Rubin Cohen, Dimitrios Karakitsos

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Application of deep learning techniques for analysis and prediction of particulate matter at Kota city, India by Lovish Sharma, Hajari Singh, Mahendra Pratap Choudhary

“…Air pollution significantly threatens human health and the environment, making accurate prediction of pollutant concentrations crucial for effective mitigation. This study leverages deep learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, to predict concentrations of PM10 and PM2.5. …”
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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

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Human Trajectory Imputation Model: A Hybrid Deep Learning Approach for Pedestrian Trajectory Imputation by Deb Kanti Barua, Mithun Halder, Shayanta Shopnil, Md. Motaharul Islam

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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

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Analysis of pressure-maintaining coring process in deep coal seams and gas content determination methods by Jian WU, Songhang ZHANG, Tengfei JIA, Weiwei CHAO, Wenchun PENG, Shilong LI

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A Deep Learning Enabled Chest X-Ray Abnormality Detection Model for Radiology Assistance. by Ssempeebwa, Phillip, Ainembabazi, Patience

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Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation by Muhammad Roman, Abdul Shahid, Shafiullah Khan, Lisu Yu, Muhammad Asif, Yazeed Yasin Ghadi

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A low functional redundancy-based network slimming method for accelerating deep neural networks by Zheng Fang, Bo Yin

“…Deep neural networks (DNNs) have been widely criticized for their large parameters and computation demands, hindering deployment to edge and embedded devices. …”
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Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections by Jupeng Xie, Huajun Zhang, Linfan Liu, Mengchuan Li, Yixin Su

“…To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. …”
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SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren, Ali Nawaz, Muhammad Yasar Khan, Ayman Altameem

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Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition by Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen, Hamed Alqahtani, Asma Alshuhail, Menwa Alshammeri, Wafa Sulaiman Almukadi

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A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction by Jiangeng Li, Xingyang Shao, Rihui Sun

“…In this paper, for the purpose of improve prediction accuracy of air pollutant concentration, a deep neural network model with multitask learning (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. …”
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