Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Bhatele, Kirti Raj; Bhadauria, Sarita Singh

doi:10.18280/ts.370611

Cited by 18 publications

(11 citation statements)

References 31 publications

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“…This technique is a scikit-learn [28] wrapper introduced in the open-source, and still experimental, XGBoost package [46], which implies that the interface can be altered. XGBRF has been used by many studies such as [20,47].…”

Section: Random Forests In Xgboost (Xgbrf)mentioning

confidence: 99%

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

Moursi

El-Fishawy

Djahel

et al. 2022

Sensors

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In a world where humanity’s interests come first, the environment is flooded with pollutants produced by humans’ urgent need for expansion. Air pollution and climate change are side effects of humans’ inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN–LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms.

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Section: Random Forests In Xgboost (Xgbrf)mentioning

confidence: 99%

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

Moursi

El-Fishawy

Djahel

et al. 2022

Sensors

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“…Ensemble learning can be used to overcome this problem. Machine learning ensembles are made up of several decision trees known as random forests [32].…”

Section: Experiments 3: Ensemble Network Architecturementioning

confidence: 99%

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

Rajendran¹,

Radhakrishnan²,

Viswanathan³

2022

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The human brain is the body's most complicated organ. Constant blood flow is essential for the sustained functioning of the brain. A blocked blood vessel's interruption of blood supply prevents oxygen and nutrients to the brain tissues. This results in a life-threatening brain disease called Ischemic Stroke. Computed Tomography (CT) images are widely used in the diagnosis of Ischemic Stroke because of their faster acquisition and compatibility with most life support devices. CT acquired from the patients who arrived with stroke symptoms is Primary CT (PCT). After some hours CT taken for the same patient is Secondary CT (SCT). Stroke lesions may not be visible in PCT, whereas visible in SCT. Learning the features automatically using a Convolutional Neural Network (CNN) is essential to classify normal and abnormal CT slices. These networks are capable of learning the global features effectively for image classification. Though this CNN approach works, achieving desired accuracy was challenging. Different architectures considered for this CNN experimentation are VGG1, VGG2, VGG3, VGG16, InceptionV3, and ResNet50. This novel work provides a detailed explanation of the three experiments conducted using PCT and SCT slices. Three experiments are conducted using SCT and PCT slices. The pretrained VGG16, ResNet50, and InceptionV3 networks with the ImageNet database are applied as a first approach. Both SCT and PCT slices are used for testing alone. It resulted in 49.22%, 47.076% and 49.36% classification accuracy. In the second approach, different models were trained for classification from PCT and SCT slices. This includes the networks like VGG1, VGG2, VGG3, VGG16, VGG16 with dropout, ResNet, ResNet50 with lambda regularization, InceptionV3, and InceptionV3 with lambda regularization. The accuracies achieved are 68%, 69.4%, 72%, 78.2%, 79.1%, 77%, 77.8%, 79.6% and 80.1%. The accuracy was improved with dropout and lambda regularization. The networks with high accuracy are selected and an ensemble model is developed as a third approach. ResNet50, VGG16, and InceptionV3 are combined to form an ensemble network. This ensemble network yielded an accuracy of 81.98% when SCT and PCT slices are used for both training and testing. And produced 74% accuracy when PCT slices alone were used. Also produced 93.76% accuracy when SCT slices alone were used.

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“…In this experiment, compared with Principal Component Analysis (PCA) [17] and SelectKBest (SKB) [18], we calculate the running time and accuracy under different number of features by using four machine learning algorithms such as DT [19], Extra Tree (ET) [20], XGBoost [21] and eXtreme Gradient Boosting with Random Forest (XGBRF) [22]. For mainly comparing the pros and cons of extracted features on the same algorithm, we do not adjust the parameters of the above four algorithms too much, and most of them are classified using default parameters.…”

Section: Local Controller Defencementioning

confidence: 99%

MHSDN: A Hierarchical Software Defined Network Reliability Framework design

Zhu¹,

Liu

et al. 2022

IET Information Security

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At present, attacks based on the vulnerability of the controller and flooding attacks still constitute a principal threat for hierarchical Software Defined Network (SDN), such as flow table tampering, malicious Application attacks, Distributed Denial of Service (DDoS) etc., due to the limitation against attacks based on known or unknown vulnerabilities for traditional cyber defence technology. Therefore, this study proposes an active defence architecture based on Mimic Defence (MD)–Mimic Hierarchical SDN Framework (MHSDN). Then endogenous security of MHSDN is theoretically analysed. Simultaneously, the attack surface measurement of MD is innovatively proposed, further improving the security and usability measurement standards of the MD system. Finally, to speed up detection and reduce defence cost of DDoS, this research proposes the Random Forest Feature Extract (RFFE) and tolerable switch migration. Simulation shows that RFFE has achieved a faster detection speed at the cost of less detection accuracy, and MHSDN can better improve the reliability of hierarchical SDN.

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Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Cited by 18 publications

References 31 publications

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

MHSDN: A Hierarchical Software Defined Network Reliability Framework design

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