Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model.

Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model.

<p>Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mohanad Faeq Ali (21354273) (author)
مؤلفون آخرون:	Mohammed Shakir Mohmood (21354276) (author), Ban Salman Shukur (21354279) (author), Rex Bacarra (21354282) (author), Jamil Abedalrahim Jamil Alsayaydeh (21354285) (author), Masrullizam Mat Ibrahim (21354288) (author), Safarudin Gazali Herawan (19749996) (author)
منشور في:	2025
الموضوعات:	Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination outperformed existing models model &# 8217 maximum recognition rate including false positive implemented using python identifying abnormal patterns false positive rate false negative rates false negative rate causes prediction inaccuracies computational complexity issues communication networks within indicate potential cyberattacks iomt threat detection dynamic iomt ecosystem recurrent neural networks interconnected healthcare devices enhancing cyberattack prediction dataset availability issues computational efficiency enhances sensitive healthcare data recurrent networks healthcare devices computational efficiency detecting cyberattacks efficiency depends iomt infrastructure iomt environments xlink "> various metrics training data rfe ), results demonstrated research introduces research focuses rapid development raising concerns principal component optimization technique medical things learning techniques irrelevant features innovative machine gathered information extracted features evolving threats cybersecurity attacks created system attacks threaten
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مواد مشابهة

Graphical abstract of HCAP.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Performance comparison of cyber security techniques in IoMT healthcare devices.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Impact of cyberattack types on IoMT devices.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

LSTM algorithm for cyber attack prediction in IoMT.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

IoMT devices and attack overview in the CICIoMT2024 dataset.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Computational efficiency.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

FPR and FNR analysis based on varying cyber attack types.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

FPR and FNR analysis based on different time steps.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Recall analysis.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

<b> </b> Precision analysis.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Prediction accuracy on varying attack types.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Prediction accuracy analysis over time steps.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Probabilities of normal and various cyber attack types over time sequence.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Convergence rate analysis.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

Analysis of IoMT data sources.
حسب: Mohanad Faeq Ali (21354273)
منشور في: (2025)

IN-DEPTH INSIGHTS INTO IOMT INFRASTRUCTURE CLOUD COMPUTING’S ADVANCES AND IMPACT
حسب: Olorunfemi Bolaji Asore (18505719)
منشور في: (2025)

Comparison of the proposed framework with the existing literature.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Statistical comparison of model performance.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Performance metrics with mean, standard deviation, and 95% confidence interval.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Performance comparison of classifiers for Suspicious and Pathological categories.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Evaluating the proposed framework’s accuracy in binary and multiclassification scenarios against the baseline DL models.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Performance of classifiers for normal and abnormal task detection using Recall (R), Precision (P), and F1-score (F1). G.LSTM, G.DNN, and G.CNN denote proposed models with Noise-Aware Encoder (NAE).
حسب: Amin Ullah (12015113)
منشور في: (2025)

CTG imbalance dataset distribution.
حسب: Amin Ullah (12015113)
منشور في: (2025)

A description of CTG features reassuring and non-reassuring values.
حسب: Amin Ullah (12015113)
منشور في: (2025)

A description of CTG and clinical features, including the types of sensors used to capture these features and the normal range for each feature.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Speed efficiency of the proposed technique in comparison with the baseline deep learning models.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Using the Recall, Precision, and F1-score metrics, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for multiclassification of the Pathological minor class in an imbalanced CTG test dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Three types of models are compared using the Accuracy metric for multiclassification in an imbalanced CTG test dataset: basic deep learning models, advanced deep learning models, and the proposed framework.
حسب: Amin Ullah (12015113)
منشور في: (2025)

ROC analysis of the proposed framework on the imabalnace CTG dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

The proposed framework’s confusion matrix on the unbalanced CTG dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary classification of abnormal class in an imbalanced CTG test dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary classification of normal class in an imbalanced CTG test dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

Using the Accuracy metric, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for the binary classification of normal and abnormal classes in an unbalanced CTG test dataset.
حسب: Amin Ullah (12015113)
منشور في: (2025)

The CNN classifier architecture used in our suggested framework to categorize CTG imbalance data into three groups: abnormal, non-abnormal, and reassuring.
حسب: Amin Ullah (12015113)
منشور في: (2025)

The pre-processing of CTG raw signals [26].
حسب: Amin Ullah (12015113)
منشور في: (2025)

Creation of a model for tracking fetal health monitoring utilizing an imbalanced CTG dataset, Generative Adversarial Network, and Autoencoder for anomaly detection and classification.
حسب: Amin Ullah (12015113)
منشور في: (2025)

The architecture of the proposed framework.
حسب: Amin Ullah (12015113)
منشور في: (2025)

An actual CTG recording was acquired from a Pakistani hospital located in Islamabad.
حسب: Amin Ullah (12015113)
منشور في: (2025)

To monitor fetal distress monitoring, many CTG devices are placed in rooms within a distributed SDN-IoT architecture.
حسب: Amin Ullah (12015113)
منشور في: (2025)

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