A. Ozbayoglu
Management Science and Operations Research · 2007–2020
A researcher in the Science corpus: 13 papers; trusts 525 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Management Science and Operations Research — 295 citations into it; #1,436 of 11,946 by trust here, #2,241 by citations
- Artificial Intelligence — 217 citations into it; #15,305 of 35,345 by trust here, #20,836 by citations
- Time-Series & Forecasting — 181 citations into it; #54 of 381 by trust here, #84 by citations
- Finance — 107 citations into it; #2,446 of 10,518 by trust here, #4,732 by citations
- Economics & Operations Research — 106 citations into it
- Economics and Econometrics — 104 citations into it; #8,287 of 23,481 by trust here, #16,755 by citations
- Computer Vision and Pattern Recognition — 100 citations into it; #8,059 of 21,575 by trust here, #15,162 by citations
- Electrical and Electronic Engineering — 95 citations into it; #3,805 of 16,539 by trust here, #10,154 by citations
Whom A. Ozbayoglu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Omer Berat Sezer
- Alex Krizhevsky
- Yann LeCun
- Diederik P. Kingma
- Ilya Sutskever
- J. Urgen Schmidhuber
- Kaiming He
- Karen Simonyan
Who trusts A. Ozbayoglu overall
- Omer Berat Sezer
- M. Ugur Gudelek
- Weiwei Jiang
- Longbing Cao
- Cihan H. Dagli
- Mustafa Ucar
- Ilknur Ucar
- Shree Krishna Sharma
- Fei Wang
- Dalin Zhang
Papers from A. Ozbayoglu's perspective
Ordered by how strongly the researchers A. Ozbayoglu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities (2018) — 309 citations
- A deep learning based stock trading model with 2-D CNN trend detection (2017) — 121 citations
- Stock Market Technical Indicator Optimization by Genetic Algorithms (2010) — 9 citations
- Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach (2018) — 387 citations
- Deep Learning for Financial Applications : A Survey (2020) — 513 citations
- Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019) — 59 citations
- Neural Based Technical Analysis in Stock Market Forecasting (2007) — 4 citations
- An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework (2017) — 66 citations
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019) — 1,431 citations
- A Two-level Cascade Evolutionary Computation based Covered Call Trading Model (2013) — 7 citations
- Review of tool condition monitoring in machining and opportunities for deep learning (2020) — 313 citations
- Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018) — 386 citations
- Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms (2015) — 11 citations