# bib

id: cord-027732-8i8bwlh8
author: Boudaya, Amal
title: EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network
date: 2020-05-31
extension: .txt
mime: text/plain
words: 2339
sentence: 111
flesch: 44
cache: cache/cord-027732-8i8bwlh8.txt
txt: txt/cord-027732-8i8bwlh8.txt
summary:
  Given, its high temporal resolution, portability and reasonable cost, the present work focus on hypo-vigilance detection by analyzing EEG signal of various brain's functionalities using fourteen electrodes placed on the participant's scalp. In this paper, we propose a CNN hypo-vigilance detection method using EEG data in order to classify drowsiness and awakeness states.


id: cord-175846-aguwenwo
author: Chatsiou, Kakia
date: 2020-10-20
extension: .txt
mime: text/plain
words: 3191
sentence: 129
flesch: 39
cache: cache/cord-175846-aguwenwo.txt
txt: txt/cord-175846-aguwenwo.txt
title:
  Text Classification of Manifestos and COVID-19 Press Briefings using BERT and Convolutional Neural Networks

summary:
  We show thatCNN combined with transformers like BERT outperforms CNN combined with other embeddings (Word2Vec, Glove, ELMo) and that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training. We observe similar patterns in the accuracy and F1 scores, and additionally show that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training In the future, we aim to conduct similar experiments also considering the 'subdomain' categories of the Manifesto Corpus Annotations.


id: cord-266055-ki4gkoc8
author: Kikkisetti, S.
date: 2020-09-02
extension: .txt
mime: text/plain
words: 3436
sentence: 174
flesch: 45
cache: cache/cord-266055-ki4gkoc8.txt
txt: txt/cord-266055-ki4gkoc8.txt
title:
  Deep-learning convolutional neural networks with transfer learning accurately classify COVID19 lung infection on portable chest radiographs

summary:
  While there are already many papers describing prevalence and radiographic features on pCXR of COVID-19 lung infection (see reviews (25, 26) ), there is a few peer-reviewed AI papers (27-32) and non-peer reviewed papers (33-36) to classify CXRs of COVID-19 patients from CXR of normals or related lung infections. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model.


id: cord-002901-u4ybz8ds
author: Yu, Chanki
title: Acral melanoma detection using a convolutional neural network for dermoscopy images
date: 2018-03-07
extension: .txt
mime: text/plain
words: 3517
sentence: 141
flesch: 47
cache: cache/cord-002901-u4ybz8ds.txt
txt: txt/cord-002901-u4ybz8ds.txt
summary:
  Dermoscopy images of BN were divided into nine types, and AM images into three types according to the reference [15] , by two dermatologists. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.


id: cord-024491-f16d1zov
author: Qiu, Xi
date: 2020-04-17
extension: .txt
mime: text/plain
words: 3473
sentence: 199
flesch: 52
cache: cache/cord-024491-f16d1zov.txt
txt: txt/cord-024491-f16d1zov.txt
title:
  Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies

summary:
  In recent years, many researchers turn to deep neural networks for heartbeat classification. In [11] , Yildirim et al. designed a deep LTSM network with wavelet-based layers for heartbeat classification.


id: cord-028792-6a4jfz94
author: Basly, Hend
title: CNN-SVM Learning Approach Based Human Activity Recognition
date: 2020-06-05
extension: .txt
mime: text/plain
words: 3570
sentence: 144
flesch: 44
cache: cache/cord-028792-6a4jfz94.txt
txt: txt/cord-028792-6a4jfz94.txt
summary:
  Traditionally, to deal with such problem of recognition, researcher are obliged to anticipate their algorithms of Human activity recognition by prior data training preprocessing in order to extract a set of features using different types of descriptors such as HOG3D [1] , extended SURF [2] and Space Time Interest Points (STIPs) The main contribution of the present work is to propose a learning approach for human activity recognition based CNN and SVM able to classify activities from one shot.


id: cord-286887-s8lvimt3
author: Nour, Majid
date: 2020-07-28
extension: .txt
mime: text/plain
words: 3688
sentence: 178
flesch: 48
cache: cache/cord-286887-s8lvimt3.txt
txt: txt/cord-286887-s8lvimt3.txt
title:
  A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

summary:
  Building and deploying a medical AI system in four weeks Deep Transfer Learning based Classification Model for COVID-19 Disease COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images Automated detection of COVID-19 cases using deep neural networks with X-ray Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT Deep Learning COVID-19 Features on CXR using Limited Training Data Sets Data Augmentation Using Auxiliary Classifier GAN for Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders Essentials for Radiologists on COVID-19: Dataset [22] , COVID-19 positive chest X-ray images from different articles and lastly chest X-ray


id: cord-354819-gkbfbh00
author: Islam, Md. Zabirul
date: 2020-08-15
extension: .txt
mime: text/plain
words: 3681
sentence: 167
flesch: 49
cache: cache/cord-354819-gkbfbh00.txt
txt: txt/cord-354819-gkbfbh00.txt
title:
  A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

summary:
  In this study, a combined method was developed to automatically detect COVID-19 cases using three types of X-ray images. Clinical features of patients infected with 2019 novel coronavirus in Coronavirus Disease Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients Recovery COVID-19): Role of Chest CT in Diagnosis and Management Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR A Survey on Computer Vision for Assistive Medical Diagnosis from Faces Prediction of breast cancer using support vector machine and K-Nearest neighbors Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder Mathematical model development to detect breast cancer using multigene genetic programming Diabetes Prediction: A Deep Learning Approach Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques Developing IoT Based Smart Health Monitoring Systems: A Review, Rev. d'Intelligence Artif Development of Smart Healthcare Monitoring System in IoT Environment Feature extraction for image recognition and computer vision Optimal deep learning model for classification of lung cancer on CT images Bone suppression of chest radiographs with cascaded convolutional networks in wavelet domain Deep back propagation-long shortterm memory network based upper-limb sEMG signal classification for automated rehabilitation A New Modified Deep Convolutional Neural Network for Detecting COVID-19 from X-ray Images Covid-2019 Detection Using X-Ray Images And Artificial Intelligence Hybrid Systems Within the Lack of Chest COVID-19 Xray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Machine Learning Approach for Confirmation of COVID-19 Cases:


id: cord-135296-qv7pacau
author: Polsinelli, Matteo
title: A Light CNN for detecting COVID-19 from CT scans of the chest
date: 2020-04-24
extension: .txt
mime: text/plain
words: 3836
sentence: 166
flesch: 53
cache: cache/cord-135296-qv7pacau.txt
txt: txt/cord-135296-qv7pacau.txt
summary:
  Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. Moreover, a large number of CT images have to be evaluated in a very short time thus increasing the probability of misclassifications.


id: cord-296359-pt86juvr
author: Polsinelli, Matteo
title: A Light CNN for detecting COVID-19 from CT scans of the chest
date: 2020-10-03
extension: .txt
mime: text/plain
words: 3898
sentence: 167
flesch: 50
cache: cache/cord-296359-pt86juvr.txt
txt: txt/cord-296359-pt86juvr.txt
summary:
  Moreover, a large number of CT images have to be evaluated in a very short time thus increasing the probability of misclassifications. This justifies the use of intelligent approaches that can automatically classify CT images of the chest.


id: cord-168974-w80gndka
author: Ozkaya, Umut
title: Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
date: 2020-04-07
extension: .txt
mime: text/plain
words: 3588
sentence: 226
flesch: 57
cache: cache/cord-168974-w80gndka.txt
txt: txt/cord-168974-w80gndka.txt
summary:
  Convolution neural network layers use a matrix parameter with a matrix parameter that includes a different kinds of link between each input unit and each output unit. Convolution neural networks has some layer such as convolution, ReLU, Pooling, normalization, fully connected and softmax layer.


id: cord-317643-pk8cabxj
author: Masud, Mehedi
title: Convolutional neural network-based models for diagnosis of breast cancer
date: 2020-10-09
extension: .txt
mime: text/plain
words: 4152
sentence: 218
flesch: 49
cache: cache/cord-317643-pk8cabxj.txt
txt: txt/cord-317643-pk8cabxj.txt
summary:
  Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. Transfer learning techniques using pre-trained models have shown promising results in different medical diagnosis, such as chest X-ray image analysis for pneumonia and COVID-19 patients' identification


id: cord-127759-wpqdtdjs
author: Qi, Xiao
date: 2020-11-06
extension: .txt
mime: text/plain
words: 3915
sentence: 203
flesch: 47
cache: cache/cord-127759-wpqdtdjs.txt
txt: txt/cord-127759-wpqdtdjs.txt
title:
  Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network

summary:
  Although numerous studies have shown the capability of CNNs in effective identification of COVID-19 from CXR images, none of these studies investigated local phase CXR image features as multi-feature input to a CNN architecture for improved diagnosis of COVID-19 disease. In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images.


id: cord-249065-6yt3uqyy
author: Kassani, Sara Hosseinzadeh
date: 2020-04-22
extension: .txt
mime: text/plain
words: 4295
sentence: 178
flesch: 40
cache: cache/cord-249065-6yt3uqyy.txt
txt: txt/cord-249065-6yt3uqyy.txt
title:
  Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

summary:
  The public dataset of X-ray images was provided by Dr. Joseph Cohen [22] investigated the diagnostic uncertainty and interpretability of deep learning-based methods for COVID-19 detection in X-ray images.


id: cord-325235-uupiv7wh
author: Makris, A.
title: COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
date: 2020-05-24
extension: .txt
mime: text/plain
words: 5445
sentence: 257
flesch: 47
cache: cache/cord-325235-uupiv7wh.txt
txt: txt/cord-325235-uupiv7wh.txt
summary:
  Deep learning models require a large amount of data in order to perform accurate feature extraction and classification. Transfer learning of class decomposed medical images in convolutional neural networks Deep learning-based detection for covid-19 from chest ct using weak label Covid-resnet: A deep learning framework for screening of covid19 from radiographs Deep convolutional neural networks for image classification: A comprehensive review Gradient-based learning applied to document recognition Deep residual learning for image recognition Utilizing pretrained deep learning models for automated pulmonary tuberculosis detection using chest radiography Imagenet classification with deep convolutional neural networks Very deep convolutional networks for large-scale image recognition Going deeper with convolutions Provable bounds for learning some deep representations Rethinking the inception architecture for computer vision Inception-v4, inception-resnet and the impact of residual connections on learning Inception-v4, inception-resnet and the impact of residual connections on learning Xception:


id: cord-202184-hh7hugqi
author: Wang, Jun
date: 2020-10-10
extension: .txt
mime: text/plain
words: 5294
sentence: 226
flesch: 37
cache: cache/cord-202184-hh7hugqi.txt
txt: txt/cord-202184-hh7hugqi.txt
title:
  Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network

summary:
  The pascal visual object classes (voc) challenge International conference on machine learning Proceedings of the IEEE conference on computer vision and pattern recognition I. j. o. R. s. A survey of image classification methods and techniques for improving classification performance Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CNN automatically learns image features from multiple dimensions on a large image dataset, which is applied to identify or classify structures and is therefore applicable in multiple automated image-recognition biomedical areas 26, 27 .


id: cord-258170-kyztc1jp
author: Shorfuzzaman, Mohammad
date: 2020-11-05
extension: .txt
mime: text/plain
words: 5374
sentence: 198
flesch: 44
cache: cache/cord-258170-kyztc1jp.txt
txt: txt/cord-258170-kyztc1jp.txt
title:
  Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

summary:
  Fan et al. (2020) presented a similar approach to social distancing monitoring with an autonomous surveillance quadruped robot that could promote social distancing in complex urban environments. Single shot multibox detector Low-cost implementation of bird's-eye view system for camera-on-vehicle Enabling and emerging technologies for social distancing: A comprehensive survey-rob Monitoring covid-19 social distancing with person detection and tracking via fine-tuned YOLOv3 and deepsort techniques Detection and Brain Mapping Visualization Inception U-Net architecture for semantic segmentation to identify nuclei in microscopy cell images Crowd analysis for congestion control early warning system on foot over bridge Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation Data-driven dynamic clustering framework for mitigating the adverse economic impact of COVID-19 lockdown practices Yolo9000: better, faster, stronger Faster R-CNN: Towards real-time object detection with region proposal networks DeepSOCIAL: Social distancing monitoring and infection risk assessment in COVID-19 pandemic, medRxiv preprint The use of drones during mass events COVID-Robot: Monitoring social distancing constraints in crowded scenarios Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities Very deep convolutional networks for large-scale image recognition Automatic Visual Concept Learning for Social Event Understanding Deep Relative Attributes The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission Rethinking the inception architecture for computer vision Tensor2Tensor for neural machine translation Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV).


id: cord-308219-97gor71p
author: Elzeiny, Sami
title: Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images
date: 2020-09-17
extension: .txt
mime: text/plain
words: 5699
sentence: 229
flesch: 45
cache: cache/cord-308219-97gor71p.txt
txt: txt/cord-308219-97gor71p.txt
summary:
  After that, spatial images were generated from the extracted matrices and converted to frequency domain images for stress classification models using deep convolutional neural networks. Images processing using frequency domain images can perform better than spatial domain images, provide more features, and reduce the computation time.


id: cord-190424-466a35jf
author: Lee, Sang Won
title: Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening
date: 2020-07-22
extension: .txt
mime: text/plain
words: 5688
sentence: 246
flesch: 47
cache: cache/cord-190424-466a35jf.txt
txt: txt/cord-190424-466a35jf.txt
summary:
  This may also eliminate the need for chemical assays or FACs for cell analysis when used in conjunction with a benchtop microscope. For CNN II cell classification, AlexNet yielded the lowest test set accuracy of 0.96 for the test set.


id: cord-330239-l8fp8cvz
author: Oyelade, O. N.
date: 2020-11-03
extension: .txt
mime: text/plain
words: 6450
sentence: 253
flesch: 44
cache: cache/cord-330239-l8fp8cvz.txt
txt: txt/cord-330239-l8fp8cvz.txt
title:
  Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN

summary:
  Prospective Predictions Are the Future ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases Automated detection of COVID19 cases using deep neural networks with X-ray images COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks Coronet: The Lancet infectious diseases A Machine Learning Solution Framework for Combatting COVID-19 in Smart Cities from Multiple Dimensions Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review Deep Convolutional Neural Networks based Classification model for COVID-19 Infected Patients using Chest X-ray Images Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks Coronavirus (covid-19) classification using ct images by machine learning methods Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images The experimental results of multiclass classification for four (4) experimental scenarios show that most scenarios have more than 99% accuracy.


id: cord-275258-azpg5yrh
author: Mead, Dylan J.T.
date: 2019-07-26
extension: .txt
mime: text/plain
words: 6335
sentence: 282
flesch: 48
cache: cache/cord-275258-azpg5yrh.txt
txt: txt/cord-275258-azpg5yrh.txt
title:
  Visualization of protein sequence space with force-directed graphs, and their application to the choice of target-template pairs for homology modelling

summary:
  As the taxonomical distance increases, production of high quality homology models becomes more difficult. Human-infective virus Importance to human health NCBI RefSeq annotated genome Easy retrieval of high quality RdRP sequence RdRP located at the 3 0 end of polyprotein or on its own segment Eliminates unconventional RdRPs


id: cord-102774-mtbo1tnq
author: Sun, Yuliang
title: Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
date: 2020-05-20
extension: .txt
mime: text/plain
words: 6419
sentence: 267
flesch: 54
cache: cache/cord-102774-mtbo1tnq.txt
txt: txt/cord-102774-mtbo1tnq.txt
summary:
  Ubiquitous gesture sensing with millimeter wave radar Hand gesture recognition based on radar micro-Doppler signature envelopes Hand gesture recognition using micro-Doppler signatures with convolutional neural network Sparsity-driven micro-Doppler feature extraction for dynamic hand gesture recognition Interacting with Soli: Hand gesture recognition enables users to interact with machines in a more natural and intuitive manner than conventional touchscreen-based and button-based human-machine-interfaces


id: cord-032684-muh5rwla
author: Madichetty, Sreenivasulu
title: A stacked convolutional neural network for detecting the resource tweets during a disaster
date: 2020-09-25
extension: .txt
mime: text/plain
words: 6989
sentence: 350
flesch: 51
cache: cache/cord-032684-muh5rwla.txt
txt: txt/cord-032684-muh5rwla.txt
summary:
  The classes are (i) Need of resource tweet (ii) Availability of resource tweet and (iii). Only a few existing works [1, 3, 11] are only focused on extracting the need and availability of resource tweets during the disaster.


id: cord-256756-8w5rtucg
author: Manimala, M. V. R.
title: Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach
date: 2020-08-11
extension: .txt
mime: text/plain
words: 6710
sentence: 336
flesch: 51
cache: cache/cord-256756-8w5rtucg.txt
txt: txt/cord-256756-8w5rtucg.txt
summary:
  CS achieves high quality of MR image reconstruction by utilizing the transform domain sparsity in the encoded MR acquisitions and thereby adapting non-linear reconstruction. To reconstruct the image patch, consider posterior distribution X(a|b) , which can be represented by Bayes' rule as Image reconstruction can be modelled by employing MAP estimation applying log on both sides of the above equation: log X(a) and log X(a|b) represents the priori term and information probability respectively.


id: cord-131094-1zz8rd3h
author: Parisi, L.
title: QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
date: 2020-10-15
extension: .txt
mime: text/plain
words: 7577
sentence: 218
flesch: 36
cache: cache/cord-131094-1zz8rd3h.txt
txt: txt/cord-131094-1zz8rd3h.txt
summary:
  Furthermore, it is worth noting the proposed quantum AFs led to improved classification outcomes as compared to recent advances in ReLU AFs, such as CReLU and VLReLU: • QReLU led to ACC, precision, sensitivity/recall, and F1-score all higher by 1% those obtained via CReLU when evaluating the CNN's classification performance on the MNIST data (Table 2 ). key: cord-131094-1zz8rd3h authors: Parisi, L.; Neagu, D.; Ma, R.; Campean, F. title: QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics date: 2020-10-15 journal: nan DOI: nan sha: doc_id: 131094 cord_uid: 1zz8rd3h The ReLU activation function (AF) has been extensively applied in deep neural networks, in particular Convolutional Neural Networks (CNN), for image classification despite its unresolved dying ReLU problem, which poses challenges to reliable applications.


id: cord-034614-r429idtl
author: Yasar, Huseyin
date: 2020-11-04
extension: .txt
mime: text/plain
words: 7769
sentence: 300
flesch: 54
cache: cache/cord-034614-r429idtl.txt
txt: txt/cord-034614-r429idtl.txt
title:
  A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

summary:
  The first Vietnamese case of COVID-19 acquired from China The role of augmented intelligence (ai) in detecting and preventing the spread of novel coronavirus An automated residual exemplar local binary pattern and iterative ReliefF based corona detection method using lung X-ray image Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet Automated detection of COVID-19 cases using deep neural networks with X-ray images Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods Coronet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks Covidgan: data augmentation using auxiliary classifier Gan for improved covid-19 detection CovXNet: a multidilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization Deep learning COVID-19 detection bias: accuracy through artificial intelligence Deep transfer learning with apache spark to detect COVID-19 in chest X-ray images New machine learning method for image-based diagnosis of COVID-19 Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19 Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks COVID-XNet: a custom deep learning system to diagnose and locate COVID-19 in chest X-ray images Deepcovid: predicting covid-19 from chest x-ray images using deep transfer learning COVID-19 image data collection COVID-19 X rays Two public chest X-ray datasets for computer-aided screening of pulmonary diseases A comparative study of texture measures with classification based on featured distributions The dual-tree complex wavelet transform The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement Shift invariant properties of the dual-tree complex wavelet transform Dual-tree complex wavelet transform and SVD based medical image resolution enhancement A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform Blood vessel extraction from retinal images using complex wavelet transform and complex-valued artificial neural network Improved adaptive image retrieval with the use of shadowed sets Uncertainty-optimized deep learning model for small-scale person re-identification Vehicle and wheel detection: a novel SSD-based approach and associated large-scale benchmark dataset Kernel pooling for convolutional neural networks Deep metric learning with angular loss A study on the cardinality of ordered average pooling in visual recognition Data augmentation for eeg-based emotion recognition with deep convolutional neural networks A modified convolutional neural network for face sketch synthesis A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification A face recognition method based on LBP feature for CNN. Appl Intell DOI: 10.1007/s10489-020-02019-1 sha: doc_id: 34614 cord_uid: r429idtl In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN).


id: cord-337740-8ujk830g
author: Matencio, Adrián
title: Cyclic Oligosaccharides as Active Drugs, an Updated Review
date: 2020-09-29
extension: .txt
mime: text/plain
words: 8102
sentence: 294
flesch: 33
cache: cache/cord-337740-8ujk830g.txt
txt: txt/cord-337740-8ujk830g.txt
summary:
  In an interesting study, treatment with β-CD reduced the content of ordered domains of cholesterol at the cell surface, which in turn, protected cells against NMDA(N-Methyl-d-aspartate)-mediated excitotoxicity (Figure 2 ). Collagen-glycoseaminoglycan scaffolds that incorporate β-CD showed improved sequestration as well as the extended retention and release of TGF-β1 (transforming growth factor beta 1) and BMP-2 (bone morphogenetic protein 2), which influence the metabolic activity and proliferation of mesenchymal stem cells.


id: cord-121200-2qys8j4u
author: Zogan, Hamad
title: Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
date: 2020-07-03
extension: .txt
mime: text/plain
words: 10040
sentence: 421
flesch: 46
cache: cache/cord-121200-2qys8j4u.txt
txt: txt/cord-121200-2qys8j4u.txt
summary:
  This motivates us to leverage the superior neural network learning capability with the rich and heterogeneous behavioural patterns of social media users. (3) We built a CNN network to classify user timeline posts concatenated with BiGRU network to identify social media users who suffer from depression.


id: cord-269270-i2odcsx7
author: Sahlol, Ahmed T.
title: COVID-19 image classification using deep features and fractional-order marine predators algorithm
date: 2020-09-21
extension: .txt
mime: text/plain
words: 7060
sentence: 333
flesch: 48
cache: cache/cord-269270-i2odcsx7.txt
txt: txt/cord-269270-i2odcsx7.txt
summary:
  For example, Lambin et al. 7 proposed an efficient approach called Radiomics to extract medical image features. They showed that analyzing image features resulted in more information that improved medical imaging.


id: cord-255884-0qqg10y4
author: Chiroma, H.
title: Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus
date: 2020-11-05
extension: .txt
mime: text/plain
words: 13211
sentence: 602
flesch: 43
cache: cache/cord-255884-0qqg10y4.txt
txt: txt/cord-255884-0qqg10y4.txt
summary:
  Ahuja et al. (2020) reported that COVID-19 data are now growing. In this section, we present the sources of COVID-19 data to the machine learning community.


id: cord-103297-4stnx8dw
author: Widrich, Michael
title: Modern Hopfield Networks and Attention for Immune Repertoire Classification
date: 2020-08-17
extension: .txt
mime: text/plain
words: 14116
sentence: 651
flesch: 51
cache: cache/cord-103297-4stnx8dw.txt
txt: txt/cord-103297-4stnx8dw.txt
summary:
  A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning Explaining and interpreting LSTMs Solving the protein sequence metric problem Rank-loss support instance machines for miml instance annotation Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires Multiple instance learning: a survey of problem characteristics and applications VDJServer: a cloud-based analysis portal and data commons for immune repertoire sequences and rearrangements Tetramer-visualized gluten-specific CD4+ T cells in blood as a potential diagnostic marker for coeliac disease without oral gluten challenge iReceptor: a platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories Support-vector networks Quantifiable predictive features define epitope-specific T cell receptor repertoires On a model of associative memory with huge storage capacity BERT: pre-training of deep bidirectional transformers for language understanding Solving the multiple instance problem with axis-parallel rectangles Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire Predicting antigen-specificity of single T-cells based on TCR CDR3 regions. We apply random and attention-based subsampling of repertoire sequences to reduce over-fitting and decrease computational effort.


id: cord-319868-rtt9i7wu
author: Majeed, Taban
title: Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays
date: 2020-10-06
extension: .txt
mime: text/plain
words: 7684
sentence: 302
flesch: 47
cache: cache/cord-319868-rtt9i7wu.txt
txt: txt/cord-319868-rtt9i7wu.txt
summary:
  As such, it becomes highly desired and required to have computeraided diagnostic systems that can aid radiologists in making a more time-efficient and accurate interpretation of X-ray images that are characteristic of COVID-19 [4] . in [5] proposed a method to detect COVID-19 using X-ray images based on deep feature and support vector machines (SVM).


id: cord-133273-kvyzuayp
author: Christ, Andreas
date: 2020-10-05
extension: .txt
mime: text/plain
words: 42474
sentence: 2129
flesch: 53
cache: cache/cord-133273-kvyzuayp.txt
txt: txt/cord-133273-kvyzuayp.txt
title:
  Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)

summary:
  [16] have shown, that, despite their initial design for image data, a residual neural network (ResNet) can perform quite well on time-series classification. Additionally, it discusses whether more general requirements can be sufficiently addressed in the scenarios, as e.g. proposed in ethical guidelines for AI based systems like [9, 10] .


