scholarly journals Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6049
Author(s):  
Ying-Ren Chien ◽  
Cheng-Hsuan Wu ◽  
Hen-Wai Tsao
Keyword(s):  
Neural Networks ◽  
Ensemble Learning ◽  
Characteristic Curve ◽  
Poor Quality ◽  
Machine Learning Algorithms ◽  
Physiological Data ◽  
Process Time ◽  
Learning Networks ◽  
Quality Sleep ◽  
Sleep Arousal

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

scholarly journals Comparison of Different Ensemble Methods in Credit Card Default Prediction

2021 ◽  
Vol 5 (2) ◽  
pp. 20-25
Author(s):  
Azhi Abdalmohammed Faraj ◽  
Didam Ahmed Mahmud ◽  
Bilal Najmaddin Rashid
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Credit Card ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Ensemble Methods ◽  
Research Problem ◽  
Machine Learning Algorithms ◽  
Default Prediction ◽  
Credit Card Default

Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance

scholarly journals Windows PE Malware Detection Using Ensemble Learning

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Nureni Ayofe Azeez ◽  
Oluwanifise Ebunoluwa Odufuwa ◽  
Sanjay Misra ◽  
Jonathan Oluranti ◽  
Robertas Damaševičius
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Internet Age ◽  
Stage Classification ◽  
End Stage

In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.

scholarly journals A deep learning-based algorithm for detection of cortical arousal during sleep

SLEEP ◽  
2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Ao Li ◽  
Siteng Chen ◽  
Stuart F Quan ◽  
Linda S Powers ◽  
Janet M Roveda
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Detection Algorithm ◽  
Physiological Data ◽  
Ecg Signal ◽  
Cortical Arousal ◽  
Eeg Data ◽  
Varying Length ◽  
End To End

Abstract Study Objectives The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep. Methods This study included 1,547 polysomnography records that met study inclusion criteria and were selected from the Multi-Ethnic Study of Atherosclerosis database. We developed an end-to-end deep learning model consisting of convolutional neural networks and recurrent neural networks which: (1) accepted varying length physiological data; (2) directly extracted features from the raw ECG signal; (3) captured long-range dependencies in the physiological data; and (4) produced arousal probability in 1-s resolution. Results We evaluated the model on a test set (n = 311). The model achieved a gross area under precision-recall curve score of 0.62 and a gross area under receiver operating characteristic curve score of 0.93. Conclusion This study demonstrated the end-to-end deep learning approach with a single-lead ECG has the potential to be used to accurately detect arousals in home sleep tests.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

scholarly journals Detecting cybersecurity attacks across different network features and learners

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Joffrey L. Leevy ◽  
John Hancock ◽  
Richard Zuech ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Impact Performance ◽  
Detection Model ◽  
Wide Range ◽  
Research Questions

AbstractMachine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to investigate ensemble feature selection on the performance of seven classifiers. CSE-CIC-IDS2018 is big data (about 16,000,000 instances), publicly available, modern, and covers a wide range of realistic attack types. Our contribution is centered around answers to three research questions. The first question is, “Does feature selection impact performance of classifiers in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and F1-score?” The second question is, “Does including the Destination_Port categorical feature significantly impact performance of LightGBM and Catboost in terms of AUC and F1-score?” The third question is, “Does the choice of classifier: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Catboost, LightGBM, or XGBoost, significantly impact performance in terms of AUC and F1-score?” These research questions are all answered in the affirmative and provide valuable, practical information for the development of an efficient intrusion detection model. To the best of our knowledge, we are the first to use an ensemble feature selection technique with the CSE-CIC-IDS2018 dataset.

scholarly journals An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Electronic Component ◽  
Support Vector ◽  
Learning Networks ◽  
Component Recognition ◽  
And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6   level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

scholarly journals Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

2021 ◽  
pp. 1-6
Author(s):  
Hannah Garcia Doherty ◽  
Roberto Arnaiz Burgueño ◽  
Roeland P. Trommel ◽  
Vasileios Papanastasiou ◽  
Ronny I. A. Harmanny
Keyword(s):  
Neural Networks ◽  
Feature Vector ◽  
Classification Performance ◽  
Input Image ◽  
Human Gait ◽  
Learning Networks ◽  
Class Label ◽  
Deep Convolutional Neural Networks ◽  
Network Layers ◽  
Feature Dimension

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

scholarly journals Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Changming Wu ◽  
Heshan Yu ◽  
Seokhyeong Lee ◽  
Ruoming Peng ◽  
Ichiro Takeuchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Change ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Phase Change Materials ◽  
Refractive Index Change ◽  
Optical Computing ◽  
Machine Learning Algorithms ◽  
Matrix Vector Multiplication

AbstractNeuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.

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