scholarly journals Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
M. Rubaiyat Hossain Mondal ◽  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Meta Analysis ◽  
Future Research ◽  
Research Papers ◽  
Convolutional Network ◽  
X Ray ◽  
Comprehensive Survey ◽  
Novel Coronavirus

Background: This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Method: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. Conclusion: Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.

scholarly journals A Comprehensive Survey of Privacy-preserving Federated Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-36
Author(s):  
Xuefei Yin ◽  
Yanming Zhu ◽  
Jiankun Hu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Research Directions ◽  
Privacy Concerns ◽  
Privacy Leakage ◽  
Comprehensive Survey ◽  
Future Research Directions

The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

Reconfigurable manufacturing system: a systematic review, meta-analysis and future research directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Pansare ◽  
Gunjan Yadav ◽  
Madhukar R. Nagare
Keyword(s):  
Manufacturing System ◽  
Meta Analysis ◽  
Future Research ◽  
Future Trends ◽  
Reconfigurable Manufacturing ◽  
Research Papers ◽  
Reconfigurable Manufacturing System ◽  
Content Type ◽  
Related Research ◽  
And Performance

Purpose Uncertainties in manufacturing and changing customer demands force manufacturing industries to adopt new strategies, such as the reconfigurable manufacturing system (RMS). To improve the implementation and performance of RMS, it is necessary to review the available literature and identify future trends in this field. This paper aims to analyze existing literature and to see trends in RMS-related research. Design/methodology/approach The systematic literature review and analysis of RMS-related research papers from 1999 to 2020 is carried out in this literature. The selected studies are analyzed based on the year of publication, journals, publishers, active authors, research design, countries, enablers, barriers, performance evaluation parameters and universities. Findings After the analysis of selected RMS-related research papers, the top countries, universities, journals, publishers and authors are identified in this domain. Research themes and trends in research are identified in this study. Besides, it has been noted that there is a need for further research in this domain and for the creation of a generalized framework that can guide researchers and practitioners to increase RMS adoption. Practical implications Research insights, guidance and observations from this paper are provided to RMS-related researchers and practitioners. Important research gaps are identified in this study, which can provide direction for future research and trends in RMS research. Originality/value The study presented focuses mainly on the method of collecting, organizing, capturing, interpreting and analyzing data to provide more insight into RMS to identify future trends in research.

scholarly journals Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid -19 Diagnosis

2021 ◽  
Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mathematical Models ◽  
Ct Scan ◽  
Virus Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Laboratory Test Results ◽  
Learning Architectures

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Sign in / Sign up

Export Citation Format

Share Document