Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for Heart Disease Diagnosis

Al-Safi, Haedar; Munilla, Jorge; Rahebi, Javad

doi:10.1109/icmnwc52512.2021.9688348

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…In fact, the function of the general goal in the part of detecting perturbations or not detecting it is described in equation (11). In general, the above function should be minimized as much as possible to detect coronary heart disease.…”

Section: Methodsmentioning

confidence: 99%

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An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Al-Safi

Munilla

Rahebi

2022

Computational Intelligence and Neuroscience

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Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.

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Section: Methodsmentioning

confidence: 99%

“…e early stages of the disease have no symptoms so that it can be easily diagnosed. Due to the annoyance of this disease and the similarity of cardiac arrhythmia symptoms to other heart diseases, designing an intelligent system to diagnose this disease seems necessary [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Al-Safi

Munilla

Rahebi

2022

Computational Intelligence and Neuroscience

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“…In this study, images were evaluated using accuracy, sensitivity, precision, and F1 scores, as determined by Equations ( 6)- (10), respectively [50][51][52][53] True positive (TP), false positive (FP), false negative (FN), and true negative (TN) were used to determine each of these indicators [53]. Accuracy (ACC) is the proportion of correct predictions out of all predictions.…”

Section: Resultsmentioning

confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

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“…In this study, the classification of images is done using accuracy, sensitivity, precision, and F1 indices. Equations ( 9)-( 12), respectively, define the criterion for each of the indicators [19][20][21][22][23]:…”

Section: Discussionmentioning

confidence: 99%

Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm

et al. 2023

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This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.

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Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for Heart Disease Diagnosis

Cited by 8 publications

References 14 publications

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm

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