Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques by Gunho Jung, Sun-Yong Choi

“…Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. …”
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Unlocking Potential Score Insights of Sentimental Analysis with a Deep Learning Revolutionizes by Ibrahim R. Alzahrani

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Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement by Johannes Gruen, Tobias Bauer, Theodor Rüber, Thomas Schultz

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Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation by Xiang He, Shuzhe Fu

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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

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Retracted: Deep-Learning-Based 3D Reconstruction: A Review and Applications by Applied Bionics and Biomechanics

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Cerberus: cross-site social bot detection system based on deep learning by TANG Jiawei, LIU Yushan, GAO Min, GONG Qingyuan, WANG Xin, CHEN Yang

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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…However, due to its time-consuming nature, there is a need for a fast and reliable automatic segmentation algorithm. In recent years, deep learning methods applied to brain tumor segmentation have shown promising results. …”
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Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals by Hongmei Liu, Lianfeng Li, Jian Ma

“…The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. …”
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Exploring Deep Learning–Based Models for Sociocultural African Food Recognition System by Grace Ataguba, Mona Alhasani, James Daniel, Emeka Ogbuju, Rita Orji

“…We present the design and evaluation of a sociocultural app for African food recognition using deep learning models such as transfer learning. Deep learning models have multiple processing layers that make them robust in image recognition. …”
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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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UTR-Insight: integrating deep learning for efficient 5′ UTR discovery and design by Saichao Pan, Hanyu Wang, Hang Zhang, Zan Tang, Lianqiang Xu, Zhixiang Yan, Yong Hu

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Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications by Fangyuan Lei, Jun Cai, Qingyun Dai, Huimin Zhao

“…Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. …”
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An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection by Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi

“…To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. …”
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Deep learning powered single-cell clustering framework with enhanced accuracy and stability by Yi Zhang, Xi Feng, Yin Wang, Kai Shi

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Speech-based personality prediction using deep learning with acoustic and linguistic embeddings by Martin Lukac

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BenthicNet: A global compilation of seafloor images for deep learning applications by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg

“…These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. …”
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Rating pome fruit quality traits using deep learning and image processing by Nhan H. Nguyen, Joseph Michaud, Rene Mogollon, Huiting Zhang, Heidi Hargarten, Rachel Leisso, Carolina A. Torres, Loren Honaas, Stephen Ficklin

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Retracted: Real-Time Analysis of Basketball Sports Data Based on Deep Learning by null Complexity

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Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning by Yuzhong Zhou, Linda-Sophie Schneider, Yipeng Sun, Andreas K. Maier

“…To address these challenges, this paper proposes two approaches, the first approach enhances the traditional FBP algorithm by using deep learning to train an optimized filter before reconstruction. …”
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