Covid-19 X-Ray Image Detection using ResNet50 and VGG16 in Convolution Neural Network

COVID-19 is an outbreak of disease which is created by China. COVID-19 is originated by coronavirus (CoV), generally created mutation pattern with 'SARS-CoV2' or '2019 novel coronavirus'. It is declared by the World Health Organization of 2019 in December. COVID-19 is a contagious virus and contiguous disease that will create the morality of life. Even though it is detected in an early stage it can be incurable if the severity is more. The throat and nose samples are collected to identify COVID-19 disease. We collected the X-Ray images to identify the virus. We propose a system to diagnose the images using Convolutional Neural Network (CNN) models. Dataset used consists of both Covid and Normal X-ray images. Among Convolutional Neural Network (CNN) models, the proposed models are ResNet50 and VGG16. RESNET50 consists of 48 convolutional, 1 MaxPool, and Average Pool layers, and VGG16 is another convolutional neural network that consists of 16 deep layers. By using these two models, the detection of COVID-19 is done. This research is designed to help physicians for successful detection of COVID-19 disease at an early stage in the medical field. © 2022 IEEE.

RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images

In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.

Classification of COVID-19 Patient Based on Multilayer Perceptron Neural Networks Optimized with Garra Rufa Fish Optimization Using CT Scan Images

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, F-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, 33.98%, 21.95%, 34.78% lower error rate compared with the existing methods, like multi-task deep learning using CT image analysis for COVID-19 pneumonia classification and segmentation (COV-19-MDP-CTI), COVID-19 classification utilizing CT scan depending on meta-classifier approach (COV-19-SEMC-CTI) and deep learning-based COVID-19 prediction utilizing CT scan images (COV-19-CNN-CTI), respectively. [ FROM AUTHOR] Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

PSSO: Political Squirrel Search Optimizer-Driven Deep Learning for Severity Level Detection and Classification of Lung Cancer

Lung cancer accounts for about 7.6 million deaths annually worldwide. Early identification of lung cancer is essential for reducing preventable deaths. In this paper, we developed a Political Squirrel Search Optimization (PSSO)-based deep learning scheme for efficacious lung cancer recognition and classification. We used Spine General Adversarial Network (Spine GAN) to segment lung lobe regions where a Deep Neuro Fuzzy Network (DNFN) classifier forecasts cancerous areas. A Deep Residual Network (DRN) is also used to determine the various cancer severity levels. The Political Optimizer (PO) and Squirrel Search Algorithm (SSA) were combined to create the newly announced PSSO method. Experimental outcomes are assessed using the dataset of images from the Lung Image Database Consortium.

Feedback Artificial Tree-Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)-based deep residual network (DRN), is developed. FAVO-based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region-Of-Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE-1 and ABIDE-2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE-2 dataset. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Lung CT image segmentation algorithm based on improved U-net structure

Accurate segmentation of medical images can help doctors diagnose and treat diseases. In the face of the complex COVID-19 image, this paper proposes an improved U-net network segmentation model, which uses the residual network structure to deepen the network level, and adds the attention module to integrate different receptive field, global, local and spatial features to enhance the detail segmentation effect of the network. For the COVID-19 CT data set, the F1-Score, Accuracy, SE, SP and Precision of the U-Net network are 0.9176, 0.9578, 0.9669, 0.9487 and 0.8574 respectively. Compared with U-Net, our model proposed in this paper increased by 6.43%, 3.36%, 0.85%, 4.78% and 13.11% on F1-Score, Accuracy, SE, SP and Precision, respectively. The automatic and effective segmentation of COVID-19 lung CT image is realized. © 2022 IEEE.

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification.

OBJECTIVES: The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications. METHODS: The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells. RESULTS: After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%. CONCLUSIONS: Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.

Architecture-based Evaluation of VGG16 and ResNet Models for an Online Deep Learning Environment for Medical Applications

Image classification using deep learning models has evolved impressively well in the past decade. Datasets containing millions of images grouped into thousands of classes have been used to train and test these models. Medical image classification however still faces the challenge of scarcity in datasets. Gathering data from various locations and placing it in a commonly accessed dataset is highly time-consuming. Diseases need real-Time response just like any other mission-critical operation and online deep learning could be handy. There are many pre-Trained models which acquired good accuracy on large datasets. But as the depth of the model increases the time taken to train the model and the number of computations also increase. In this paper, we evaluated two models with different architectures. VGG16 is a 16-layer normal stack of convolutional layers and ResNet50V2 is a stack of residual blocks with skip connections and 50 layers. We used a Computer Tomography (CT) Lung image dataset to classify images into COVID, healthy and pneumonia images. We found that VGG16 is taking lesser time and computations with reduced loss when compared to the ResNet50V2 model. We finally conclude that ResNet50V2 is taking more time to train images as the model is 50 layers deep, whereas the VGG16 model is only 16 layers deep. Also, images that show mild infection were predicted as healthy images by ResNet50V2 but predicted correctly by the VGG16 model. © 2022 IEEE.

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

IPFS based storage Authentication and access control model with optimization enabled deep learning for intrusion detection

Network security has benefited from intrusion detection, which may spot unexpected threats from network traffic. Modern methods for detecting network anomalies typically rely on conventional machine learning models. The human construction of traffic features that these systems mainly rely on, which is no longer relevant in the age of big data, results in relatively low accuracy and certain exceptional features. A storage authentication and access control model based on Interplanetary File System (IPFS) and a network intrusion detection system based on Chronological Anticorona Virus Optimization are hence the main goals of this research (CACVO-based DRN).The setup, user registration, initialization, data encryption and storage, authentication, testing, access control, and decryption stages are used here to perform the blockchain authentication and access control. After then, DRN is used to perform network intrusion detection. To do this, the recorded data log file is initially sent to the feature fusion module, which uses Deep Belief Network and hybrid correlation factors (DBN). After the feature fusion is complete, the proposed optimization technique, CACVO, which was recently developed by fusing the Chronological Concept with Anti Corona virus Optimization (ACVO) algorithm, is used to perform intrusion detection utilizing DRN. The experimental outcome shows that, based on the f-measure value of 0.939 and 0.938, respectively, the developed model achieved greater performance.

Image Enhancement on Deep Learning Algorithm for COVID-19 Lung X-Ray Classification

The Novel Coronavirus Disease 2019 (COVID-19) is continuously a phenomenon that continues to study for its development throughout the world because of its international emergency status. The act of testing by clinical laboratory experts is a preventive effort to reduce the increase in cases. However, the number of experts is minimal compared to the cases. So, with deep learning, we need the best model for classifying lung disease variants that the world of health can utilize. This study applies several image enhancement techniques to the convolutional neural network algorithm ResNet50 architecture, which produces gamma correction as the best image improvement technique in this study with an accuracy of 0.986. These techniques also have a reasonably efficient time and a good loss value. © 2022 IEEE.

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning.

Blood Oxygen ( SpO 2 ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower SpO 2 before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring SpO 2 by face videos, this paper proposes a novel multi-model fusion method based on deep learning for SpO 2 estimation. The method includes the feature extraction network named Residuals and Coordinate Attention (RCA) and the multi-model fusion SpO 2 estimation module. The RCA network uses the residual block cascade and coordinate attention mechanism to focus on the correlation between feature channels and the location information of feature space. The multi-model fusion module includes the Color Channel Model (CCM) and the Network-Based Model(NBM). To fully use the color feature information in face videos, an image generator is constructed in the CCM to calculate SpO 2 by reconstructing the red and blue channel signals. Besides, to reduce the disturbance of other physiological signals, a novel two-part loss function is designed in the NBM. Given the complementarity of the features and models that CCM and NBM focus on, a Multi-Model Fusion Model(MMFM) is constructed. The experimental results on the PURE and VIPL-HR datasets show that three models meet the clinical requirement(the mean absolute error ⩽ 2%) and demonstrate that the multi-model fusion can fully exploit the SpO 2 features of face videos and improve the SpO 2 estimation performance. Our research achievements will facilitate applications in remote medicine and home health.

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.

Background: Recently, the coronavirus disease 2019 (COVID-19) has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing. Objectives: The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS). Methodology: AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy. Results/Conclusion: Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)‐based deep residual network (DRN), is developed. FAVO‐based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region‐Of‐Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE‐1 and ABIDE‐2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE‐2 dataset. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Efficient Diagnosis of Covid19 by Employing Deep Transfer Learning on Pretrained VGG and ResidualNet Architectures

Covid19 has had a widespread influence on health services and the way of life. A prompt diagnosis is crucial for curbing the development of the disease and lowering the number of fatalities. It is customary and standard routine to employ blood tests to detect presence of pathogen, but because of the time and expense involved, it is often necessary to turn to other rapid and affordable options. We implemented two distinct transference based deep layered architectures in this study i.e., ResidualNet50 along with VGG16, to classify X-rays as COVID19, pneumonia, or normal. ResidualNet50 trained with transference approach outperformed the other deep-learning model i.e., VGG16, in the planned execution. Our proposed transfer deep-learning based model obtained an overall high classification accuracy of 98.5 percent. Result analysis and interpretation via performance curves have been comprehensively discussed in this paper. © 2022 IEEE.

An Analysis on the Performance of Different Categories of CNN Models over COVID-19 Dataset

In recent times, there is an enormous application of machine learning (ML) and deep learning (DL) techniques in various domains. Particularly in the medical domain, DL models must have the potential to aid the medical practitioners for effective decision making. COVID-19 had caused the world to come to a grinding halt nearly 2 years ago when the first case was detected in Wuhan, China. Its ripple effects are still felt to this very day and the problem only seems to be getting worse. Studies show that COVID-19, being a virus, will continue to mutate itself into other forms so long as it isn't completely eradicated. With RT-PCR reports taking up six hours to three days to show the results, it is the need of the hour to come up with a more efficient method to detect this virus. This paper has two-fold objectives, one is to analyse the effect of Convolutional Neural Networks (CNN) models for detecting COVID-19 and another is to explore and analyse the performance of different classes of CNN over COVID-19 dataset. For this research work, a dataset of a total of 6464 images is curated for the purpose of training the various CNN models which includes 2500 images of Normal, 1464 images of COVID-19 and 2500 images of Pneumonia chest x-rays. Various pretrained models are used and compared based on their accuracies. © 2022 IEEE.

CT Image Classification and Detection of COVID-19 Based on Convolutional Neural Network

As Corona Virus Disease (COVID-19) emerged at the end of 2019, traditional detection is mainly carried out using four methods: coronavirus screening detection strips, COVID-19 antibody detection kits, COVID-19 nucleic acid detection and CT detection, and the problem of low efficiency exists. In order to solve the problem of using neural network to detection a large number of data, slow speed, low efficiency, high cost, complex algorithm structure and low accuracy of detection of large data sets at present. In this paper, by collecting known public COVID-19 CT image data sets, a convolutional neural network algorithm based on residual network is proposed to reduce parameter complexity, modify weights and biases associated with neurons, and simplify the overall network structure. This algorithm is used to improve the accuracy of COVID-19 case classification detection and the convergence speed of the model. Through model verification, the accuracy of the proposed algorithm model is 0.985, the precision is 0.805, the area under the curve (AUC) of the ROC curve is found to be 0.852, and the recall rate is 0.897. The results show that the classification detection algorithm model proposed in this paper has higher accuracy than the general image classification model, is more concise in the network model, reduces the complexity, and can be more effectively applied to the detection of COVID-19. The combination of traditional medical imaging diagnosis and deep learning technology helps medical personnel to make more rapid, accurate and effective diagnosis.

FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial-of-service attack detection in cloud using spark architecture

Cloud computing is an emerging standard in modern days for the purpose of sharing huge data, as it affords numerous user friendly behaviors. Cloud computing services offer an extensive range of resource pool in order to maintain huge scale data. Although, cloud computing model is disposed to several cyber-attacks and security problems regarding cloud structure, because of the dynamic and distribute character and exposures in virtualization implementation. Distributed denial-of-service (DDoS) attack is a type of cyber-attack, which disturbs the usual traffic of targeted cloud server. Moreover, DDoS produces malicious traffic in cloud structure, and thus consumes cloud resources. In this paper, an effective DDoS attack detection model, named fractional anti corona virus student psychology optimization-based deep residual network (FACVSPO-based DRN) is implemented using spark architecture. The devised FACVSPO approach is newly designed by incorporating anti coronavirus optimization (ACVO) algorithm, fractional calculus (FC) and student psychology based optimization (SPBO) model. Moreover, the hybrid correlative scheme is designed for extracting significant features for attack detection. The DRN structure is utilized for performing attack recognition, which categorizes the data as normal or attack. In addition, the DRN classifier is trained by the developed FACVSPO approach. The developed attack detection model outperformed other existing techniques in terms of testing accuracy, true negative rate (TNR), true positive rate (TPR) of 0.9236, 0.9141, and 0.9412, respectively. The testing accuracy of the implemented model is 12.02%, 8.92%, 7.27%, 6.30%, 5.68%, and 1.20% better than the existing methods, such as Taylor-elephant herd optimisation based deep belief network (TEHO-DBN), deep learning, deep neural network (DNN), multiple kernel learning, Fuzzy Taylor elephant herd optimisation (EHO)-based DBN, fractional anti corona virus optimization-deep neuro fuzzy network (FACVO-based DNFN), respectively. Similarly, the TNR is 10.14%, 6.88%, 5.94%, 5.46%, 4.25%, and 3.28% and TPR is 12.33%, 9.46%, 8.05%, 7.41%, 6.02%, and 3.04% better than the existing methods.

Classification network of Chest X-ray images based on residual network in the context of COVID-19

As COVID-19 spreads across the globe, more cases are being confirmed around the world, making it imperative that we take a better approach to fighting the outbreak. To stop the spread of the disease and better screen for cases, we need a more sensitive and efficient test that can classify images of lung abnormalities in patients. In this paper, residual network is used to classify the collected chest radiographs. Feature extraction and classification were carried out on the original chest X-ray images, which were divided into the following three categories: normal lung, bacterial pneumonia and virus pneumonia. This can quickly rule out normal and routine infections, screen out large numbers of cases, and reduce the burden on health care workers who need to further examine cases. At the same time, our results are also very good, with an accuracy of 94%, which has practical classification significance. © 2022 IEEE.

COVIZONE: A Deep Transfer Learning-Based System for Automatic Detection of Covid-19 using Chest X-Ray

With increasing rise of COVID-19 infected patients in India and worldwide, examining and detecting COVID-19 among such large number of populations is becoming a humongous task for the medical practitioners and civic authorities. RT-PCR, real time reverse transcription-polymerase chain reaction technique is widely accepted and one of the reliable methods for detection of novel COVID-19.However, being a time consuming, laborious and expensive method for declaring results for the patients in over 6-8 hours to even 3 days in remote places, this technique is not being widely used. The high and very fast spread rate of COVID-19 and low availability of RT-PCR kit, is making the use of computer assisted technologies an inevitable and a potentially faster response mechanism catering to a large population with least human error and a cost-effective solution. Therefore, an intelligent system COVIZONE has been presented, in the proposed work, designed using state of the art pre-trained CNN model to analyze and detect COVID-19 presence in the lungs using Chest X-Ray and CT-Scan Images. In the proposed work, a multi-class classification (Normal, Pneumonic and COVID-19) of patients using ResNet and ResNext CNN model has been done. Both the models show similar performance with high accuracy of 96% and 97% respectively on public dataset of COVID-19, Pneumonia and Normal CXR and CT-Scans. To avoid skewness due to lesser number of COVID-19 CXR images, dataset used has limited Pneumonia and Normal CXR images to train the system and achieved noticeable high accuracy. The proposed COVID-19 detection model i.e. COVIZONE, even if not used as a primary Covid testing and detection tool, can still be a very helpful tool for screening potentially infected persons and help the physicians who are yet not trained for this pandemic diagnosis. © 2021 IEEE