scholarly journals BEMD-3DCNN-Based Method for COVID-19 Detection

2021 ◽  
Author(s):  
Ali Riahi ◽  
Omar Elharrouss ◽  
Noor Almaadeed ◽  
Somaya Al-Maadeed
Keyword(s):  
Empirical Mode Decomposition ◽  
Detection System ◽  
Virus Detection ◽  
Research Projects ◽  
X Ray ◽  
Mode Decomposition ◽  
The World ◽  
A Priority ◽  
The Right

Abstract Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus detection in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from expanding. The purpose of the study is to combine the bi-dimensional empirical mode decomposition (BEMD) technique with 3DCNN to detect COVID-19. BEMD is used to decompose the original images into IMFs and from there built a video then apply the 3DCNN to classify and detect COVID-19 virus. In our experiment we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. It reached the accuracy of 100%.

scholarly journals TRIZ in Retrospect and Prospect

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Chien Chiang Lin ◽  
Hsing-Hung Lin ◽  
Kun-Chih Huang
Keyword(s):  
Research Agenda ◽  
Quality Function Deployment ◽  
Service Industries ◽  
Operational Performance ◽  
Future Research ◽  
Research Projects ◽  
Text Data ◽  
Theoretical Perspectives ◽  
The World ◽  
The Right

In last 70 years TRIZ(Theory of Inventive Problem Solving) has been developed prosperously,  including the establishment of associations, training centers, consulting companies, and software suppliers; research projects as well as related outcomes from various domains did enrich the accumulation of the literature. Actually, a plethora of studies could be discovered from different databases extensively tackling related issues of TRIZ from theoretical perspectives, methodological concerns and the combination of TRIZ and other tools. Practically speaking, manufacturing as well as service industries were the major playground for utilizing TRIZ to improve operational performance for achieving excellence. It is, therefore, about the right time to understand the progress of applying TRIZ methodology from various fields in the world and to set a research agenda for future research and application. The authors conducted a systematic review of previous studies selected from several databases. Based on statistical analysis and the results of text/data mining, the current study concluded that the most adopted tools in TRIZ are contradiction and patent analysis; furthermore, quality function deployment (QFD) and green design are the most popular methods used in combination with TRIZ.

scholarly journals BEMD-3DCNN-Based Method for COVID-19 Detection

2021 ◽  
Author(s):  
Ali Riahi ◽  
Omar Elharrouss ◽  
Noor Almaadeed ◽  
Somaya Al-Maadeed
Keyword(s):  
Detection System ◽  
Imaging Techniques ◽  
Contextual Information ◽  
Context Aware ◽  
Feature Maps ◽  
Virus Identification ◽  
Mode Decomposition ◽  
The Right ◽  
Fully Connected ◽  
Deep Learning Model

Abstract Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus identification in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from spreading. Medical imaging techniques has been used for this purpose. The existing works used transfer learning by exploiting different backbones like VGG, ResNet, DenseNet or combine them to detect COVID-19. By using these backbones many aspect can not be analysed like the spatial and contextual information in the images, while these information's can be useful for a better detection performance. For that in this paper, we used 3D representation of the data (video) as input of the 3DCNN-based deep learning model. The Bi-dimensional empirical mode decomposition (BEMD) technique to decompose the original image into IMFs, then built a video of these IMFs images. The formed video is used as input of 3DCNN model to classify and detect COVID-19 virus. 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) modules then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows a learning from different feature maps. In the experiments we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. Also, the use of 3DCNN model with contextual information processing exploiting CAA networks helps to achieve better performance.

scholarly journals A Review on Deep Learning Approaches for COVID-19 Detection in Chest X-Ray Images

2021 ◽  
Vol 9 (10) ◽  
pp. 1792-1801
Author(s):  
Tanishka Dodiya
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
High Rate ◽  
Learning Approaches ◽  
X Ray ◽  
First Case ◽  
X Ray Imaging ◽  
The World ◽  
Chest X Ray ◽  
Diagnostic Capabilities

Abstract: COVID-19 also famously known as Coronavirus is one of the deadliest viruses found in the world, which has a high rate in both demise and spread. This has caused a severe pandemic in the world. The virus was first reported in Wuhan, China, registering causes like pneumonia. The first case was encountered on December 31, 2019. As of 20th October 2021, more than 242 million cases have been reported in more than 188 countries, and it has around 5 million deaths. COVID- 19 infected persons have pneumonia-like symptoms, and the infection damages the body's respiratory organs, making breathing difficult. The elemental clinical equipment as of now being employed for the analysis of COVID-19 is RT-PCR, which is costly, touchy, and requires specific clinical workforce. According to recent studies, chest X-ray scans include important information about the start of the infection, and this information may be examined so that diagnosis and treatment can begin sooner. This is where artificial intelligence meets the diagnostic capabilities of intimate clinicians. X-ray imaging is an effectively available apparatus that can be an astounding option in the COVID-19 diagnosis. The architecture usually used are VGG16, ResNet50, DenseNet121, Xception, ResNet18, etc. This deep learning based COVID detection system can be installed in hospitals for early diagnosis, or it can be used as a second opinion. Keywords: COVID-19, Deep Learning, CNN, CT-Image, Transfer Learning, VGG, ResNet, DenseNet

Anomaly Detection in Cloud Environments

2019 ◽  
pp. 140-164
Author(s):  
Angelos K. Marnerides
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Detection System ◽  
Security Threats ◽  
Accurate Identification ◽  
Mode Decomposition ◽  
Operational Characteristics ◽  
Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.

scholarly journals Shells as ‘extended architecture’: to escape isolation, social hermit crabs choose shells with the right external architecture

2020 ◽  
Vol 23 (6) ◽  
pp. 1177-1187
Author(s):  
Jakob Krieger ◽  
Marie K. Hörnig ◽  
Mark E. Laidre
Keyword(s):  
Experimental Design ◽  
3D Printing ◽  
Cognitive Abilities ◽  
Physical Space ◽  
Solitary Confinement ◽  
Hermit Crabs ◽  
Trial And Error ◽  
X Ray ◽  
The World ◽  
The Right

AbstractAnimals’ cognitive abilities can be tested by allowing them to choose between alternatives, with only one alternative offering the correct solution to a novel problem. Hermit crabs are evolutionarily specialized to navigate while carrying a shell, with alternative shells representing different forms of ‘extended architecture’, which effectively change the extent of physical space an individual occupies in the world. It is unknown whether individuals can choose such architecture to solve novel navigational problems. Here, we designed an experiment in which social hermit crabs (Coenobita compressus) had to choose between two alternative shells to solve a novel problem: escaping solitary confinement. Using X-ray microtomography and 3D-printing, we copied preferred shell types and then made artificial alterations to their inner or outer shell architecture, designing only some shells to have the correct architectural fit for escaping the opening of an isolated crab’s enclosure. In our ‘escape artist’ experimental design, crabs had to choose an otherwise less preferred shell, since only this shell had the right external architecture to allow the crab to free itself from isolation. Across multiple experiments, crabs were willing to forgo preferred shells and choose less preferred shells that enabled them to escape, suggesting these animals can solve novel navigational problems with extended architecture. Yet, it remains unclear if individuals solved this problem through trial-and-error or were aware of the deeper connection between escape and exterior shell architecture. Our experiments offer a foundation for further explorations of physical, social, and spatial cognition within the context of extended architecture.

Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Salim Lahmiri ◽  
Mounir Boukadoum
Keyword(s):  
Empirical Mode Decomposition ◽  
Digital Images ◽  
Color Image ◽  
Detection System ◽  
Histogram Equalization ◽  
Automated System ◽  
Support Vector ◽  
Clinical Environment ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

AbstractThis work presents a new automated system to detect circinate exudates in retina digital images. It operates as follows: the true color image is converted to gray levels, and contrast-limited adaptive histogram equalization (CLAHE) is applied to it before undergoing empirical mode decomposition (EMD) as intrinsic mode functions (IMFs). The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. The experimental results using a set of 45 images (23 normal images and 22 images with circinate exudates taken from the STARE database) and tenfold cross-validation indicate that the proposed approach outperforms previous works found in the literature, with perfect classification. In addition, the image processing time was <4 min, making the presented circinate exudate detection system fit for use in a clinical environment.

Anomaly Detection in Cloud Environments

2015 ◽  
pp. 43-67
Author(s):  
Angelos K. Marnerides
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Detection System ◽  
Security Threats ◽  
Accurate Identification ◽  
Mode Decomposition ◽  
Operational Characteristics ◽  
Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.

Sign in / Sign up

Export Citation Format

Share Document