A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier

Rao, P. Sankara; Parida, Priyadarsan; Sahu, Gupteswar; Dash, Sonali

doi:10.1016/j.imavis.2023.104721

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…In their social ruling class, these wolves follow strict rules. The internal leadership hierarchy is split into α, β, δ, and ω wolves [94].…”

Section: Deep Learning Optimizermentioning

confidence: 99%

Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning

Shih,

et al. 2023

Mathematics

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Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment. This study uses six deep learning methods through different oversampling techniques and feature selection, and then through dimensionality reduction techniques, to establish a set that can effectively distinguish between Bladder Cancer and Cystitis patient’s deep learning model. The research results show that based on the laboratory clinical dataset, the deep learning model proposed in this study has an accuracy rate of 89.03% in distinguishing between Bladder Cancer and Cystitis, surpassing the results of previous studies. The research model developed in this study can be provided to clinicians as a reference to differentiate between Bladder Cancer and Cystitis.

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“…In their social ruling class, these wolves follow strict rules. The internal leadership hierarchy is split into α, β, δ, and ω wolves [94].…”

Section: Deep Learning Optimizermentioning

confidence: 99%

Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning

Shih,

et al. 2023

Mathematics

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“…Random Forest has proven successful in diverse applications, including surface water detection [9], human gait recognition [10], supplier selection criteria classification [11], glaucoma detection [12], and many others. Past studies have shown that Random Forest exhibits superior generalization performance, faster learning speed, and a more straightforward implementation process without requiring extensive and timeconsuming parameter tuning for diagnosing purposes [13].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Heart Attack Diagnosis Using Random Forest With Bat Algorithm and Greedy Crossover Technique

Ardiyansa,

Maharani,

Anam

et al. 2024

BAREKENG: J. Math. & App.

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Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.

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“…The classification of spam mail is a major challenge faced, as the adversaries are constantly updating the tools and techniques for the creation and dissemination of spam mails. Recent advancement in the area of machine learning [2,3] have proven to be useful in the area of spam classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Application of error level analysis in image spam classification using deep learning model

Singh,

Singh

2023

PLoS ONE

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Image spam is a type of spam that contains text information inserted in an image file. Traditional classification systems based on feature engineering require manual extraction of certain quantitative and qualitative image features for classification. However, these systems are often not robust to adversarial attacks. In contrast, classification pipelines that use convolutional neural network (CNN) models automatically extract features from images. This approach has been shown to achieve high accuracies even on challenge datasets that are designed to defeat the purpose of classification. We propose a method for improving the performance of CNN models for image spam classification. Our method uses the concept of error level analysis (ELA) as a pre-processing step. ELA is a technique for detecting image tampering by analyzing the error levels of the image pixels. We show that ELA can be used to improve the accuracy of CNN models for image spam classification, even on challenge datasets. Our results demonstrate that the application of ELA as a pre-processing technique in our proposed model can significantly improve the results of the classification tasks on image spam datasets.

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A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier

Cited by 8 publications

References 29 publications

Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning

Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning

Optimizing Heart Attack Diagnosis Using Random Forest With Bat Algorithm and Greedy Crossover Technique

Application of error level analysis in image spam classification using deep learning model

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