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Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness [article]

Faxi Yuan, Chao Fan, Hamed Farahmand, Natalie Coleman, Amir Esmalian, Cheng-Chun Lee, Flavia I. Patrascu, Cheng Zhang, Shangjia Dong, Ali Mostafavi
2021 arXiv   pre-print
First, we demonstrate the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks.  ...  ; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities.  ...  The authors would also like to acknowledge INRIX, Inc. and SafeGraph for providing data.  ... 
arXiv:2111.06461v2 fatcat:2ugdb6geivdsjp5ypzkspvldey

Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and  ...  -Deep learning: including interpretable neural networks to visualize financial texts; link prediction for credit scoring by recurrent neural networks, graph neural networks and autoencoders; sequential  ...  , regression and graph neural networks and deep transfer learning models for representing macro to micro-level financial data or cross-sectional stock representation for portfolio prediction and strategy  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4

A Novel Machine Learning Approach to Credit Card Fraud Detection

L.U. Oghenekaro, C. Ugwu
2016 International Journal of Computer Applications  
temporal patterns.  ...  The resulting model performed online learning and recorded higher percentage accuracy of 91% and beyond in detecting fraudulent transactions as compared to the Neural Network model that recorded 89.6%,  ...  Ghosh and Reilly (1994) , proposed a fraud detection system for credit card using a three-layer approach of feed forward neural network.  ... 
doi:10.5120/ijca2016909316 fatcat:kxuoqshqmnbidgtqm6i2ka5iny

Table of contents

2020 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)  
An FPGA-Based Implementation Method for Quadratic Spiking Neuron Model 0621 21.3 1570680454 A Machine Learning Approach to Temporal Traffic-Aware Energy- Efficient Cellular Networks 0628 21.4  ...  Correlation 0143 05.4 1570675862 A Novel Framework for Data Center Risk Assessment 0148 05.5 1570675375 PAARS: Privacy Aware Access Regulation System 0155 SESSION 6: CLOUD AND VIRTUAL NETWORKS  ... 
doi:10.1109/uemcon51285.2020.9298057 fatcat:p4v3pn2m2zaaxdgobcaw5db76m

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

Zan Li, Madeline Fields, Fedor Panov, Saadi Ghatan, Bülent Yener, Lara Marcuse
2021 Frontiers in Neurology  
The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal  ...  The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined).  ...  of neural network architectures.  ... 
doi:10.3389/fneur.2021.705119 pmid:34867707 pmcid:PMC8632629 fatcat:f6jgzanbnvc77hurvgyvakro4m

A Review on Graph Neural Network Methods in Financial Applications [article]

Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song
2022 arXiv   pre-print
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks.  ...  We first categorize the commonly-used financial graphs and summarize the feature processing step for each node.  ...  , graph autoencoders, and spatial-temporal graph neural networks.  ... 
arXiv:2111.15367v2 fatcat:o7gfnhlmrnetnl2s2sccjoquje

Table of Contents

2021 IEEE Signal Processing Letters  
Wang Spike-Event-Driven Deep Spiking Neural Network With Temporal Encoding . . . . . . . . . . . . . . . . . . ..Z. Zhang and Q.  ...  Li Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2021.3134551 fatcat:ab4b4tb5rrcu5cq6aifdekrizq

2020-2021 Index IEEE Transactions on Computers Vol. 70

2021 IEEE transactions on computers  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TC Dec. 2021 2015-2028 Spatio-Temporal Optimization of Deep Neural Networks for Reconfigurable FPGA SoCs.  ...  Kwon, H., +, TC Sept. 2021 1401-1411 Credit Risk Analysis Using Quantum Computers.  ... 
doi:10.1109/tc.2021.3134810 fatcat:p5otlsapynbwvjmqogj47kv5qa

Machine Learning for Financial Risk Management: A Survey

Akib Mashrur, Wei Luo, Nayyar A. Zaidi, Antonio Robles-Kelly
2020 IEEE Access  
Graph neural networks [58] , [59] , [60] , [61] , [62] , [63] are a special type of neural network that can be used for modelling data with complex graph structures.  ...  However, the use of temporal sequence-learning neural networks (for example, recurrent neural networks) to completely replace the traditional methods is still limited [26] , [165] . 4) Insurance underwriting  ... 
doi:10.1109/access.2020.3036322 fatcat:44z5jx3b2ff5xc6pcw3pwniyua

A Review on Graph Neural Network Methods in Financial Applications

Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song
2022 Journal of Data Science  
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks.  ...  We first categorize the commonly-used financial graphs and summarize the feature processing step for each node.  ...  Gcnext: Graph convolutional network with expanded balance theory for fraudulent user detection.  ... 
doi:10.6339/22-jds1047 fatcat:lpkkobcferal3p7l5wanydm7ay

BRIGHT – Graph Neural Networks in Real-Time Fraud Detection [article]

Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang
2022 arXiv   pre-print
The Lambda Neural Network decouples inference into two stages: batch inference of entity embeddings and real-time inference of transaction prediction.  ...  risk propagation in a transaction graph.  ...  • (Q2) How could we design a graph neural network architecture that is efficient for online inference?  ... 
arXiv:2205.13084v2 fatcat:lkrfeoshafeqrcxqfysdm6ggha

Data science and AI in FinTech: an overview

Longbing Cao, Qiang Yang, Philip S. Yu
2021 International Journal of Data Science and Analytics  
The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, Trade-Tech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and  ...  , regression and graph neural networks and deep transfer learning models for representing macro to micro-level financial data or cross-sectional stock representation for portfolio prediction and strategy  ...  networks, graph neu-ral networks, neural language models like Transformer and BERT variants, image nets, attention networks, memory networks, adversarial learning, and autoencoders, etc.).  ... 
doi:10.1007/s41060-021-00278-w fatcat:4qo3swacjbaaxh56p5bvhmjzqa

A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving [article]

Florin Leon, Marius Gavrilescu
2019 arXiv   pre-print
For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported.  ...  For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search.  ...  Acknowledgements We kindly thank Continental AG for their great cooperation within Proreta 5, which is a joint research project of the Technical University of Darmstadt, University of Bremen, Technical  ... 
arXiv:1909.07707v1 fatcat:h2ttehcuzrc2dnnmqzigilcyri

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Florin Leon, Marius Gavrilescu
2021 Mathematics  
Approaches based on deep neural networks and others, especially stochastic techniques, are reported.  ...  ., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating  ...  Acknowledgments: We kindly thank Continental AG for their great cooperation within Proreta 5, which is a joint research project of the Technical University of Darmstadt, University of Bremen, "Gheorghe  ... 
doi:10.3390/math9060660 fatcat:qvikrr32tzd7fnjzs22u3ago4m

Graph Neural Networks in IoT: A Survey [article]

Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E. Barnes, Mehdi Boukhechba
2022 arXiv   pre-print
Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art  ...  ., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data.  ...  332]presented a context-aware deep neural network simultaneously modeling the spatial, temporal, contextual information by GCN, RNN and Autoencoder.  ... 
arXiv:2203.15935v2 fatcat:jkqg5ukg5fezbewu5mr5hqsp4e
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