Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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Towards fair financial services for all: A temporal GNN approach for individual fairness on transaction networks
Z Song, Y Zhang, I King. Cited by 26
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Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails
MH Chung, L Wang, L Sharon, Y Yuhong, C Giang, K Jerath, A Raman, D Lie, M Chignell. Cited by 3
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Hybrid Autoencoder-Multi-Task Lstm Model for Indian Stock Market Ohlc Prediction and Profitable Recommendations
D Chakraborty, S Ghosh, AK Ghosh
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Study of Data Augmentation Technique for Discharge Prediction Problems Including Meandering Parameters
T Khankhoje, S Ghosh
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Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data
YS Ong, LY Chew
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A Study on Deep Learning for Prognostics and Health Management Applications: An Evolutionary Convolutional Long Short-Term Memory Deep Neural Network Data-Driven Model for …
P Khumprom
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ELMVDP: extreme learning based virtual data position exploration and incorporation method for escalation of time series forecasting accuracy
SC Nayak, S Dehuri, SB Cho. Cited by 1
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ACE: A coarse-to-fine learning framework for reliable representation learning against label noise
C Zhang, X Yang, J Liang, B Bai, K Bai, I King, Z Xu. Cited by 1
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Single cell self-paced clustering with transcriptome sequencing data
P Zhao, Z Xu, J Chen, Y Ren, I King. Cited by 2
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Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks
D Chakraborty, S Ghosh, A Ghosh. Cited by 2
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Prediction of exchange rate using improved particle swarm optimised radial basis function networks
TN Pandey, AK Jagadev, S Dehuri, SB Cho. Cited by 3
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Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending
JY Kim, SB Cho. Cited by 5