scholarly journals Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

2019 ◽  
Vol 26 (3) ◽  
pp. 143-162 ◽  
Author(s):  
Marc Bocquet ◽  
Julien Brajard ◽  
Alberto Carrassi ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Deep Learning ◽  
Data Assimilation ◽  
Physical Model ◽  
High Dimensional ◽  
Dimensional Models ◽  
Using Data ◽  
Learning Software

Abstract. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model.

scholarly journals Data assimilation as a deep learning tool to infer ODE representations of dynamical models

2019 ◽  
Author(s):  
Marc Bocquet ◽  
Julien Brajard ◽  
Alberto Carrassi ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Assimilation ◽  
Physical Model ◽  
High Dimensional ◽  
Learning Techniques ◽  
Dimensional Models ◽  
Using Data ◽  
Learning Software ◽  
Bayesian Data Assimilation

Abstract. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to cope with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations on stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal to identify or improve the model dynamics, build a surrogate or reduced model, or produce forecasts from mere observations of the physical model.

scholarly journals APMFT: Anamoly Prediction Model for Financial Transactions Using Learning Methods in Machine Learning and Deep Learning

2021 ◽  
Author(s):  
R. Priyadarshini ◽  
K. Anuratha ◽  
N. Rajendran ◽  
S. Sujeetha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
General Pattern ◽  
High Dimensional ◽  
Learning Methods ◽  
Data Points ◽  
The Times ◽  
Financial Transactions ◽  
Journal Entries

Anamoly is an uncommon and it represents an outlier i.e, a nonconforming case. According to Oxford Dictionary of Mathematics anamoly is defined as an unusal and erroneous observation that usually doesn’t follow the general pattern of drawn population. The process of detecting the anmolies is a process of data mining and it aims at finding the data points or patterns that do not adapt with the actual complete pattern of the data.The study on anamoly behavior and its impact has been done on areas such as Network Security, Finance, Healthcare and Earth Sciences etc. The proper detection and prediction of anamolies are of great importance as these rare observations may carry siginificant information. In today’s finanicial world, the enterprise data is digitized and stored in the cloudand so there is a significant need to detect the anaomalies in financial data which will help the enterprises to deal with the huge amount of auditing The corporate and enterprise is conducting auidts on large number of ledgers and journal entries. The monitoring of those kinds of auidts is performed manually most of the times. There should be proper anamoly detection in the high dimensional data published in the ledger format for auditing purpose. This work aims at analyzing and predicting unusal fraudulent financial transations by emplyoing few Machine Learning and Deep Learning Methods. Even if any of the anamoly like manipulation or tampering of data detected, such anamolies and errors can be identified and marked with proper proof with the help of the machine learning based algorithms. The accuracy of the prediction is increased by 7% by implementing the proposed prediction models.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Hospitality Industry ◽  
Learning Models ◽  
Online Data ◽  
Social Media Platforms ◽  
Using Data ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Using Data Mining to Predict Possible Future Depression Cases

2014 ◽  
Vol 3 (4) ◽  
pp. 231
Author(s):  
Kevin Daimi ◽  
Shadi Banitaan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Synthetic Data ◽  
Considerable Difficulty ◽  
Symptoms Of Depression ◽  
Using Data ◽  
Learning Software

Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used.

scholarly journals Deep learning for predicting disease status using genomic data

2018 ◽  
Author(s):  
Qianfan Wu ◽  
Adel Boueiz ◽  
Alican Bozkurt ◽  
Arya Masoomi ◽  
Allan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Genomic Data ◽  
Disease Status ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Learning Approach ◽  
Low Dimensional

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature review. All four articles used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. This deep learning approach outperformed existing prediction approaches, such as prediction based on probe-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

scholarly journals Using Data Science Software to Address Health Disparities

2021 ◽  
Vol 6 (2) ◽  
pp. 45-58
Author(s):  
Jose O. Huerta ◽  
Gayle L. Prybutok ◽  
Victor R. Prybutok
Keyword(s):  
Machine Learning ◽  
Health Disparities ◽  
Data Science ◽  
Science Technology ◽  
Science Operations ◽  
Using Data ◽  
Learning Software ◽  
Computational Processes

The article assesses data science software to evaluate the usefulness of data science technology in addressing concerns such as health disparities. Data science software was analyzed using KDnuggets data related to analytics, data science, and machine learning software. Data science functionalities include computational processes and frameworks that are relevant for healthcare. This study demonstrates the importance of leading applications for conducting data science operations that can improve care in healthcare networks by addressing such factors as health disparities.

Non–human primate epidural ECoG analysis using explainable deep learning technology

2021 ◽  
Author(s):  
Hoseok Choi ◽  
Seokbeen Lim ◽  
Kyeongran Min ◽  
Kyoung-ha Ahn ◽  
Kyoung-Min Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Hand Movement ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Technology ◽  
3D Information

Abstract Objective: With the development in the field of neural networks, Explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach: We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results: The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance: As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

scholarly journals OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3047
Author(s):  
Xiaoyu Zhang ◽  
Yuting Xing ◽  
Kai Sun ◽  
Yike Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Personalised Medicine ◽  
High Dimensional ◽  
Survival Prediction ◽  
Omics Data ◽  
Learning Framework ◽  
Deep Embedding ◽  
Task Strategy

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.

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