A Blockchain-Based Federated Learning

2021 ◽  
pp. 158-177
Author(s):  
Ankit Khushal Barai ◽  
Robin Singh Bhadoria ◽  
Jyotshana Bagwari ◽  
Ivan A. Perl
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Data Sharing ◽  
Training Data ◽  
Local Data ◽  
Audit Trail ◽  
Blockchain Technology ◽  
Conventional Machine ◽  
Healthcare Finance ◽  
Access Policies

Conventional machine learning (ML) needs centralized training data to be present on a given machine or datacenter. The healthcare, finance, and other institutions where data sharing is prohibited require an approach for training ML models in secured architecture. Recently, techniques such as federated learning (FL), MIT Media Lab's Split Neural networks, blockchain, aim to address privacy and regulation of data. However, there are difference between the design principles of FL and the requirements of Institutions like healthcare, finance, etc., which needs blockchain-orchestrated FL having the following features: clients with their local data can define access policies to their data and define how updated weights are to be encrypted between the workers and the aggregator using blockchain technology and also prepares audit trail logs undertaken within network and it keeps actual list of participants hidden. This is expected to remove barriers in a range of sectors including healthcare, finance, security, logistics, governance, operations, and manufacturing.

scholarly journals Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 12461
Author(s):  
Chih-Chang Yu ◽  
Yufeng (Leon) Wu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
At Risk ◽  
At Risk Students ◽  
Training Data ◽  
Support Vector ◽  
Learning Models ◽  
Conventional Machine ◽  
Machine Learning Models

While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Beach State Recognition Using Argus Imagery and Convolutional Neural Networks

2020 ◽  
Vol 12 (23) ◽  
pp. 3953
Author(s):  
Ashley N. Ellenson ◽  
Joshua A. Simmons ◽  
Greg W. Wilson ◽  
Tyler J. Hesser ◽  
Kristen D. Splinter
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
New South ◽  
Equilibrium States ◽  
The Self ◽  
Training Data ◽  
Local Data ◽  
State Recognition ◽  
South Wales ◽  
Key Driver

Nearshore morphology is a key driver in wave breaking and the resulting nearshore circulation, recreational safety, and nutrient dispersion. Morphology persists within the nearshore in specific shapes that can be classified into equilibrium states. Equilibrium states convey qualitative information about bathymetry and relevant physical processes. While nearshore bathymetry is a challenge to collect, much information about the underlying bathymetry can be gained from remote sensing of the surfzone. This study presents a new method to automatically classify beach state from Argus daytimexposure imagery using a machine learning technique called convolutional neural networks (CNNs). The CNN processed imagery from two locations: Narrabeen, New South Wales, Australia and Duck, North Carolina, USA. Three different CNN models are examined, one trained at Narrabeen, one at Duck, and one trained at both locations. Each model was tested at the location where it was trained in a self-test, and the single-beach models were tested at the location where it was not trained in a transfer-test. For the self-tests, skill (as measured by the F-score) was comparable to expert agreement (CNN F-values at Duck = 0.80 and Narrabeen = 0.59). For the transfer-tests, the CNN model skill was reduced by 24–48%, suggesting the algorithm requires additional local data to improve transferability performance. Transferability tests showed that comparable F-scores (within 10%) to the self-trained cases can be achieved at both locations when at least 25% of the training data is from each site. This suggests that if applied to additional locations, a CNN model trained at one location may be skillful at new sites with limited new imagery data needed. Finally, a CNN visualization technique (Guided-Grad-CAM) confirmed that the CNN determined classifications using image regions (e.g., incised rip channels, terraces) that were consistent with beach state labelling rules.

scholarly journals Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

2018 ◽  
Vol 8 (12) ◽  
pp. 2663 ◽  
Author(s):  
Davy Preuveneers ◽  
Vera Rimmer ◽  
Ilias Tsingenopoulos ◽  
Jan Spooren ◽  
Wouter Joosen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Training Data ◽  
Learning Models ◽  
Traditional System ◽  
Blockchain Technology ◽  
Malicious Behavior ◽  
Machine Learning Models

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

scholarly journals NOVEL APPROACHES TO DEVELOPMENT OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE LUNG CANCER DIAGNOSTICS

2019 ◽  
pp. 8-18 ◽  
Author(s):  
A. A. Meldo ◽  
L. V. Utkin ◽  
T. N. Trofimova ◽  
M. A. Ryabinin ◽  
V. M. Moiseenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Neural Networks ◽  
Diagnostic System ◽  
Cancer Diagnostics ◽  
Point Of View ◽  
Training Data ◽  
Medical Community ◽  
Social Significance ◽  
Feature Dimension

The relevance of developing an intelligent automated diagnostic system (IADS) for lung cancer (LC) detection stems from the social significance of this disease and its leading position among all cancer diseases. Theoretically, the use of IADS is possible at a stage of screening as well as at a stage of adjusted diagnosis of LC. The recent approaches to training the IADS do not take into account the clinical and radiological classification as well as peculiarities of the LC clinical forms, which are used by the medical community. This defines difficulties and obstacles of using the available IADS. The authors are of the opinion that the closeness of a developed IADS to the «doctor’s logic» contributes to a better reproducibility and interpretability of the IADS usage results. Most IADS described in the literature have been developed on the basis of neural networks, which have several disadvantages that affect reproducibility when using the system. This paper proposes a composite algorithm using machine learning methods such as Deep Forest and Siamese neural network, which can be regarded as a more efficient approach for dealing with a small amount of training data and optimal from the reproducibility point of view. The open datasets used for training IADS include annotated objects which in some cases are not confirmed morphologically. The paper provides a description of the LIRA dataset developed by using the diagnostic results of St. Petersburg Clinical Research Center of Specialized Types of Medical Care (Oncology), which includes only computed tomograms of patients with the verified diagnosis. The paper considers stages of the machine learning process on the basis of the shape features, of the internal structure features as well as a new developed system of differential diagnosis of LC based on the Siamese neural networks. A new approach to the feature dimension reduction is also presented in the paper, which aims more efficient and faster learning of the system.

scholarly journals QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

2022 ◽  
Vol 9 ◽  
Author(s):  
Mahabubul Alam ◽  
Swaroop Ghosh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Architecture ◽  
Empirical Studies ◽  
Quantum Computers ◽  
Quantum Neural Network ◽  
Small Quantum ◽  
Conventional Machine ◽  
Near Term ◽  
The Impact

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. Existing QML models that use deep parametric quantum circuits (PQC) suffer from a large accumulation of gate errors and decoherence. To circumvent this issue, we propose a new QML architecture called QNet. QNet consists of several small quantum neural networks (QNN). Each of these smaller QNN’s can be executed on small quantum computers that dominate the NISQ-era machines. By carefully choosing the size of these QNN’s, QNet can exploit arbitrary size quantum computers to solve supervised ML tasks of any scale. It also enables heterogeneous technology integration in a single QML application. Through empirical studies, we show the trainability and generalization of QNet and the impact of various configurable variables on its performance. We compare QNet performance against existing models and discuss potential issues and design considerations. In our study, we show 43% better accuracy on average over the existing models on noisy quantum hardware emulators. More importantly, QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices.

Creep Rupture Forecasting

2014 ◽  
Vol 2 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Process Model ◽  
Production Systems ◽  
Learning Algorithms ◽  
Difficult Problem ◽  
Network Size ◽  
Creep Rupture ◽  
Machine Learning Algorithms ◽  
Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

scholarly journals Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

2019 ◽  
Vol 16 (11) ◽  
pp. 1992 ◽  
Author(s):  
Gebreab K. Zewdie ◽  
David J. Lary ◽  
Estelle Levetin ◽  
Gemechu F. Garuma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Land Surface ◽  
Deep Neural Networks ◽  
Airborne Pollen ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Ambrosia Pollen ◽  
Extreme Gradient Boosting

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ali Madani ◽  
Ahmed Bakhaty ◽  
Jiwon Kim ◽  
Yara Mubarak ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Plaque Rupture ◽  
Training Data ◽  
Von Mises Stress ◽  
And Performance ◽  
Von Mises

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of 12,172 2D finite element simulations modeling the hyperelastic behavior and resulting stress distribution. The arterial wall composition mimics vessels in atherosclerosis–a complex cardiovascular disease and one of the leading causes of death globally. We formulate the training data to predict the maximum von Mises stress, which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of contributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method, which has far-reaching implications for real-time and multiscale prediction tasks in biomechanics.

Emerging Technologies in a Modern Competitive Scenario

2021 ◽  
pp. 1-16
Author(s):  
George Leal Jamil ◽  
Alexis Rocha da Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Statistical Analysis ◽  
Differential Privacy ◽  
Social Issues ◽  
Training Data ◽  
Sensitive Data ◽  
Machine Learning Model ◽  
Highly Sensitive ◽  
Inference Attacks

Users' personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete nor restrict the purposes for which it is used. Learning how to machine learning that protects privacy, we can make a huge difference in solving many social issues like curing disease, etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, the authors introduce differential privacy, which ensures that different kinds of statistical analysis don't compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.

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