Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

Khafaga, Doaa Sami; Alhussan, Amel Ali; El-kenawy, El-Sayed M.; Ibrahim, Abdelhameed; Elkhalik, Said H. Abd; El-Mashad, Shady Y.; Abdelhamid, Abdelaziz A.

doi:10.32604/cmc.2022.028550

Cited by 36 publications

(24 citation statements)

References 18 publications

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“…OpenPose provides different pose formats: the BODY_25 and the COCO [42]. Among these, the COCO format is used most extensively.…”

Section: Openpose Vs Blazeposementioning

confidence: 99%

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Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Alsawadi¹,

El-kenawy²,

Rio³

2023

Computers, Materials &Amp; Continua

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The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks. Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition. Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor. Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further. We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset. For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively.

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“…OpenPose provides different pose formats: the BODY_25 and the COCO [42]. Among these, the COCO format is used most extensively.…”

Section: Openpose Vs Blazeposementioning

confidence: 99%

“…The output skeleton provided by the BlazePose system is a superset of COCO [42], the BlazeFace [43] and a BlazePalm consisting of 33 keypoint set. As it is shown in Fig.…”

Section: Figure 2: Skeleton Topology Comparisonmentioning

confidence: 99%

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Alsawadi¹,

El-kenawy²,

Rio³

2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, the selection operator is also employed. The quality of a solution is determined by its value to the target function, according to [40][41][42]. (fitness value).…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Khafaga¹,

El-kenawy²,

Karim³

et al. 2023

Computers, Materials &Amp; Continua

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Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).

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“…LSTM uses time-series data for classification by keeping track of cell states to preserve certain memory trends across time [32]. LSTM block consists of three gates: forget, update, and output, which works with the input for a time series, as shown in Figure 9 [33,34]. The model decides whether to forget, update, or output new data at each state stage.…”

Section: Implementation Of Lstmmentioning

confidence: 99%

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

et al. 2022

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Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

Cited by 36 publications

References 18 publications

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

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