Deep learning framework for subject-independent emotion detection using wireless signals

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67% for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.

Download Full-text

A Deep Learning Framework for Malware Classification

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2020010105 ◽

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.

Download Full-text

Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

Download Full-text

HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome ◽

10.1186/s40168-021-01002-3 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Yu Li ◽

Zeling Xu ◽

Wenkai Han ◽

Huiluo Cao ◽

Ramzan Umarov ◽

...

Keyword(s):

Antibiotic Resistance ◽

Deep Learning ◽

Resistance Genes ◽

Antibiotic Resistance Genes ◽

Third Party ◽

Superior Performance ◽

Beta Lactamase ◽

Learning Framework ◽

Sequence Encoding ◽

Global Threat

Abstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.

Download Full-text

PlotMI: visualization of pairwise interactions and positional preferences learned by a deep learning model from sequence data

10.1101/2021.03.14.435285 ◽

2021 ◽

Author(s):

Tuomo Hartonen ◽

Teemu Kivioja ◽

Jussi Taipale

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sequence Data ◽

Predictive Performance ◽

Learning Model ◽

Biological Research ◽

Learning Approaches ◽

Learning Models ◽

Model Interpretation ◽

Pairwise Interactions

Deep learning models have in recent years gained success in various tasks related to understanding information coded in the DNA sequence. Rapidly developing genome-wide measurement technologies provide large quantities of data ideally suited for modeling using deep learning or other powerful machine learning approaches. Although offering state-of-the art predictive performance, the predictions made by deep learning models can be difficult to understand. In virtually all biological research, the understanding of how a predictive model works is as important as the raw predictive performance. Thus interpretation of deep learning models is an emerging hot topic especially in context of biological research. Here we describe plotMI, a mutual information based model interpretation strategy that can intuitively visualize positional preferences and pairwise interactions learned by any machine learning model trained on sequence data with a defined alphabet as input. PlotMI is freely available at https://github.com/hartonen/plotMI.

Download Full-text

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

SLEEP ◽

10.1093/sleep/zsab027 ◽

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.

Download Full-text

Channel Self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-Lead ECG Signals

10.23919/cinc53138.2021.9662886 ◽

2021 ◽

Author(s):

Apoorva Srivastava ◽

Ajith Hari ◽

Sawon Pratiher ◽

Sazedul Alam ◽

Nirmalya Ghosh ◽

...

Keyword(s):

Deep Learning ◽

Cardiac Abnormality ◽

Ecg Signals ◽

Learning Framework

Download Full-text

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3850 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2618-2625

Author(s):

R. T. Subhalakshmi ◽

S. Appavu Alias Balamurugan ◽

S. Sasikala

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Recurrent Neural Network ◽

Automatic Segmentation ◽

Ct Images ◽

Superior Performance ◽

Multi Scale ◽

Proposed Model ◽

Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

Download Full-text

Deep learning-based approach for segmentation of glioma sub-regions in MRI

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-02-2020-0013 ◽

2020 ◽

Vol 13 (4) ◽

pp. 389-406

Author(s):

Jiten Chaudhary ◽

Rajneesh Rani ◽

Aman Kamboj

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Data Augmentation ◽

Treatment Plan ◽

Superior Performance ◽

Content Type ◽

Proposed Model ◽

Life Threatening ◽

Tumor Region ◽

Magnetic Resonance Imaging Mri

PurposeBrain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.Design/methodology/approachIn this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.FindingsThe proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.Practical implicationsThe model can be used by doctors to identify the exact location of the tumorous region.Originality/valueThe proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

Download Full-text

Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network

Sensors ◽

10.3390/s20061593 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1593 ◽

Cited By ~ 1

Author(s):

Yanlei Gu ◽

Huiyang Zhang ◽

Shunsuke Kamijo

Keyword(s):

Deep Learning ◽

Pose Estimation ◽

Body Orientation ◽

Learning Approaches ◽

The Public ◽

Learning Network ◽

Proposed Model ◽

Body Boundary ◽

Public Dataset ◽

Deep Learning Network

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.

Download Full-text

Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2018100102 ◽

2018 ◽

Vol 9 (4) ◽

pp. 33-51

Author(s):

Rostom Mennour ◽

Mohamed Batouche

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Big Data Analytics ◽

Learning Approaches ◽

Ecg Signals ◽

Research Areas ◽

Gradient Descent Algorithm ◽

Big Data Technologies ◽

Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

Download Full-text