An automatic arrhythmia classification model based on improved Marine Predators Algorithm and Convolutions Neural Networks

Houssein, Essam H.; Hassaballah, M.; Ibrahim, Ibrahim E.; AbdElminaam, Diaa Salama; Wazery, Yaser M.

doi:10.1016/j.eswa.2021.115936

Cited by 68 publications

(26 citation statements)

References 69 publications

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“…According to the papers described above, deep learning architectures are rapidly being applied to facemask detection to prevent COVID-19 spread using a transfer learning-based deep neural network [27][28][29][30][31][32][33][34]. Other deep learning [35][36][37][38][39][40][41][42] and optimization algorithms are also used to solve various optimization problems [43][44][45][46][47][48][49][50][51]. However, several gaps in using deep learning systems for real-time implementation and prevention strategies must be addressed, as indicated [52][53][54][55][56][57].…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus community transmission. The main contribution of the proposed work is the development of a real-time system for determining whether the person on a webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pretrained models and stringent testing on an objective dataset led to the development of a highly dependable and inexpensive solution. The findings provide validity to the application’s potential for use in real-world settings, contributing to the reduction in pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity, precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and recall. An appropriate balance is kept between the number of necessary parameters and the time needed to conclude the various models.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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“…Over time experts and practitioners have shown keen interest in diagnosing heart disease by employing classical machine learning techniques. Experts usually utilize a classification approach to create a heart disease diagnosis model in their research study [5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Random Forest. A random forest is a meta estimator explained in equations ( 37) and (38), that uses averaging to improve prediction accuracy while minimizing overfitting. e subsample size is determined by the max-samples option, and each tree uses the entire dataset.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

Ullah

Chen

Rajab

et al. 2022

Computational Intelligence and Neuroscience

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Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.

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“…At the same time, the butterfly's traits must be recognized using an encyclopedia, which is enormous and cumbersome to transport on a field trip. Nur Nabila Kamaron Arzar et al [9] have concentrated upon GoogLeNet, a pre-trained CNN architectural model. The Black Veined Tiger, Chocolate Grass Yellow, Grey Pansy, and Plain Lacewing are several varieties of insects that are often encountered throughout Asia.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.

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An automatic arrhythmia classification model based on improved Marine Predators Algorithm and Convolutions Neural Networks

Cited by 68 publications

References 69 publications

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

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