Boosting Automated Sleep Staging Performance in Big Datasets using Population Sub-grouping

SLEEP ◽  
2021 ◽  
Author(s):  
Samaneh Nasiri ◽  
Gari D Clifford
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Learning Approaches ◽  
Sleep Staging ◽  
Learning Framework ◽  
Sensitivity Score ◽  
Instrument Noise ◽  
Novel Method ◽  
Clustering Approach ◽  
Statistical Relationships

Abstract Current approaches to automated sleep staging from the electroencephalogram (EEG) rely on constructing a large labeled training and test corpora by aggregating data from different individuals. However, many of the subjects in the training set may exhibit changes in the EEG that are very different from the subjects in the test set. Training an algorithm on such data without accounting for this diversity can cause underperformance. Moreover, test data may have unexpected sensor misplacement or different instrument noise and spectral responses. This work proposes a novel method to learn relevant individuals based on their similarities effectively. The proposed method embeds all training patients into a shared and robust feature space. Individuals that share strong statistical relationships and are similar based on their EEG signals are clustered in this feature space before being passed to a deep learning framework for classification. Using 994 patient EEGs from the 2018 Physionet Challenge (≈ 6,561 hours of recording), we demonstrate that the clustering approach significantly boosts performance compared to state-of-the-art deep learning approaches. The proposed method improves, on average, a precision score from 0.72 to 0.81, a sensitivity score from 0.74 to 0.82, and a Cohen’s Kappa coefficient from 0.64 to 0.75 under 10-fold cross-validation.

scholarly journals DeepLPC: A Deep Learning Approach to Augmented Kalman Filter-Based Single-Channel Speech Enhancement

2021 ◽  
Author(s):  
Sujan Kumar Roy ◽  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Linear Prediction ◽  
Single Channel ◽  
Power Spectra ◽  
Learning Approaches ◽  
Learning Framework ◽  
Listening Tests ◽  
Whitening Filter ◽  
Prediction Coefficient

Current deep learning approaches to linear prediction coefficient (LPC) estimation for the augmented Kalman filter (AKF) produce bias estimates, due to the use of a whitening filter. This severely degrades the perceived quality and intelligibility of enhanced speech produced by the AKF. In this paper, we propose a deep learning framework that produces clean speech and noise LPC estimates with significantly less bias than previous methods, by avoiding the use of a whitening filter. The proposed framework, called DeepLPC, jointly estimates the clean speech and noise LPC power spectra. The estimated clean speech and noise LPC power spectra are passed through the inverse Fourier transform to form autocorrelation matrices, which are then solved by the Levinson-Durbin recursion to form the LPCs and prediction error variances of the speech and noise for the AKF. The performance of DeepLPC is evaluated on the NOIZEUS and DEMAND Voice Bank datasets using subjective AB listening tests, as well as seven different objective measures (CSIG, CBAK, COVL, PESQ, STOI, SegSNR, and SI-SDR). DeepLPC is compared to six existing deep learning-based methods. Compared to other deep learning approaches to clean speech LPC estimation, DeepLPC produces a lower spectral distortion (SD) level than existing methods, confirming that it exhibits less bias. DeepLPC also produced higher objective scores than any of the competing methods (with an improvement of 0.11 for CSIG, 0.15 for CBAK, 0.14 for COVL, 0.13 for PESQ, 2.66\% for STOI, 1.11 dB for SegSNR, and 1.05 dB for SI-SDR, over the next best method). The enhanced speech produced by DeepLPC was also the most preferred by listeners. By producing less biased clean speech and noise LPC estimates, DeepLPC enables the AKF to produce enhanced speech at a higher quality and intelligibility.

scholarly journals A Deep Learning Framework for Malware Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 90-108
Author(s):  
Mahmoud Kalash ◽  
Mrigank Rochan ◽  
Noman Mohammed ◽  
Neil Bruce ◽  
Yang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Superior Performance ◽  
Traditional Learning ◽  
Security Threats ◽  
Learning Approaches ◽  
Learning Framework ◽  
Malware Classification ◽  
New Strategies

In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.

scholarly journals Simulation-based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning

2021 ◽  
Author(s):  
Ricardo Peres ◽  
Magno Guedes ◽  
Fábio Miranda ◽  
José Barata
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Data Availability ◽  
Training Data ◽  
Learning Approaches ◽  
Automotive Parts ◽  
Novel Method ◽  
The Cost ◽  
Structural Adhesive ◽  
Data Context

<div>The advent of Industry 4.0 has shown the tremendous transformative potential of combining artificial intelligence, cyber-physical systems and Internet of Things concepts in industrial settings. Despite this, data availability is still a major roadblock for the successful adoption of data-driven solutions, particularly concerning deep learning approaches in manufacturing. Specifically in the quality control domain, annotated defect data can often be costly, time-consuming and inefficient to obtain, potentially compromising the viability of deep learning approaches due to data scarcity. In this context, we propose a novel method for generating annotated synthetic training data for automated quality inspections of structural adhesive applications, validated in an industrial cell for automotive parts. Our approach greatly reduces the cost of training deep learning models for this task, while simultaneously improving their performance in a scarce manufacturing data context with imbalanced training sets by 3.1% ([email protected]). Additional results can be seen at https://git.io/Jtc4b.</div>

scholarly journals A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening

2021 ◽  
Author(s):  
Yuanyuan Jiang ◽  
Jiali Guo ◽  
Yjing Liu ◽  
Yanzhi Guo ◽  
Menglong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Molecular Descriptors ◽  
Molecular Graph ◽  
Feature Space ◽  
Reliable Data ◽  
Feature Representation ◽  
Data Set ◽  
Learning Framework ◽  
Priori Knowledge

<p>Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.</p><br>

scholarly journals Deep-Droid: Deep Learning for Android Malware Detection

2020 ◽  
Vol 9 (12) ◽  
pp. 122-125
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Learning Approaches ◽  
Android Malware ◽  
Detection Systems ◽  
Learning Framework ◽  
The Past ◽  
Android Malware Detection ◽  
Android Os

Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years. In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware. Hence, our Deep-Droid model is a deep learner that outperforms exiting cutting-edge machine learning approaches. All experiments performed on two datasets (Drebin-215 & Malgenome-215) to assess our Deep-Droid model. The results of experiments show the effectiveness and robustness of Deep-Droid. Our Deep-Droid model achieved accuracy over 98.5%.

scholarly journals Speaker Identification using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1216-1218
Keyword(s):  
Deep Learning ◽  
Speaker Identification ◽  
Frequency Assignment ◽  
Learning Approaches ◽  
Speech Separation ◽  
Rest Point ◽  
Time Frequency ◽  
Learning Framework ◽  
Frequency Representation ◽  
Permutation Problem

Whatever the modern achievement of deep learning for several terminology processing tasks, single-microphone, speaker-independent speech separation remains difficult for just two main things. The rest point is that the arbitrary arrangement of the goal and masker speakers in the combination (permutation problem), and also the following is the unidentified amount of speakers in the mix (output issue). We suggest a publication profound learning framework for speech modification, which handles both issues. We work with a neural network to project the specific time-frequency representation with the mixed-signal to a high-dimensional categorizing region. The time-frequency embeddings of the speaker have then made to an audience around corresponding attractor stage that is employed to figure out the time-frequency assignment with this speaker identifying a speaker using a blend of speakers together with the aid of neural networks employing deep learning. The purpose function for your machine is standard sign renovation error that allows finishing functioning throughout both evaluation and training periods. We assessed our system with all the voices of users three and two speaker mixes and also document similar or greater performance when compared with another advanced level, deep learning approaches for speech separation.

scholarly journals SIA: Selection Inference Using the Ancestral Recombination Graph

2021 ◽  
Author(s):  
Hussein A Hejase ◽  
Ziyi Mo ◽  
Leonardo Campagna ◽  
Adam Siepel
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Full Range ◽  
Genomic Diversity ◽  
Demographic Model ◽  
Ancestral Recombination Graph ◽  
Learning Framework ◽  
Novel Method ◽  
Rich Information ◽  
The Rich

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.

scholarly journals A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening

2021 ◽  
Author(s):  
Yuanyuan Jiang ◽  
Jiali Guo ◽  
Yjing Liu ◽  
Yanzhi Guo ◽  
Menglong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Molecular Descriptors ◽  
Molecular Graph ◽  
Feature Space ◽  
Reliable Data ◽  
Feature Representation ◽  
Data Set ◽  
Learning Framework ◽  
Priori Knowledge

<p>Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.</p><br>

scholarly journals An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5811
Author(s):  
Syed Muslim Jameel ◽  
Manzoor Ahmed Hashmani ◽  
Mobashar Rehman ◽  
Arif Budiman
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Image Classification ◽  
The Internet ◽  
Learning Approaches ◽  
Dynamic Image ◽  
Learning Framework ◽  
Internet Of Things Environment ◽  
Future Work ◽  
The Internet Of Things

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

scholarly journals Simulation-based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning

2021 ◽  
Author(s):  
Ricardo Peres ◽  
Magno Guedes ◽  
Fábio Miranda ◽  
José Barata
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Data Availability ◽  
Training Data ◽  
Learning Approaches ◽  
Automotive Parts ◽  
Novel Method ◽  
The Cost ◽  
Structural Adhesive ◽  
Data Context

<div>The advent of Industry 4.0 has shown the tremendous transformative potential of combining artificial intelligence, cyber-physical systems and Internet of Things concepts in industrial settings. Despite this, data availability is still a major roadblock for the successful adoption of data-driven solutions, particularly concerning deep learning approaches in manufacturing. Specifically in the quality control domain, annotated defect data can often be costly, time-consuming and inefficient to obtain, potentially compromising the viability of deep learning approaches due to data scarcity. In this context, we propose a novel method for generating annotated synthetic training data for automated quality inspections of structural adhesive applications, validated in an industrial cell for automotive parts. Our approach greatly reduces the cost of training deep learning models for this task, while simultaneously improving their performance in a scarce manufacturing data context with imbalanced training sets by 3.1% ([email protected]). Additional results can be seen at https://git.io/Jtc4b.</div>

Sign in / Sign up

Export Citation Format

Share Document