Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Duhayyim, Mesfer Al; Mohamed, Heba G.; Alotaibi, Saud S.; Mahgoub, Hany; Mohamed, Abdullah; Motwakel, Abdelwahed; Zamani, Abu Sarwar; Eldesouki, Mohamed I.

doi:10.32604/cmc.2022.031096

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Figure 7 inspects the accuracy of the BOA-EVCHAR method in the training and validation on 70:30 of TRP/ TSP. The result specified that the BOA-EVCHAR technique The comparison results of the BOA-EVCHAR technique on HAR are reported in Table 4 and Figure 9 (Duhayyim, 2023). The results illustrate that the BOA-EVCHAR technique exhibits effective performance under all measures.…”

Section: Journal Of Disability Research 2024mentioning

confidence: 79%

IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

Almalki,

Alnfiai,

Al-Wesabi

et al. 2024

Journal of Disability Research

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Internet of Things (IoT)-based human action recognition (HAR) has made a significant contribution to scientific studies. Furthermore, hand gesture recognition is a subsection of HAR, and plays a vital role in interacting with deaf people. It is the automatic detection of the actions of one or many subjects using a series of observations. Convolutional neural network structures are often utilized for finding human activities. With this intention, this study presents a new bat optimization algorithm with an ensemble voting classifier for human activity recognition (BOA-EVCHAR) technique to help disabled persons in the IoT environment. The BOA-EVCHAR technique makes use of the ensemble classification concept to recognize human activities proficiently in the IoT environment. In the presented BOA-EVCHAR approach, data preprocessing is generally achieved at the beginning level. For the identification and classification of human activities, an ensemble of two classifiers namely long short-term memory (LSTM) and deep belief network (DBN) models is utilized. Finally, the BOA is used to optimally select the hyperparameter values of the LSTM and DBN models. To elicit the enhanced performances of the BOA-EVCHAR technique, a series of experimentation analyses were performed. The extensive results of the BOA-EVCHAR technique show a superior value of 99.31% on the HAR process.

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Section: Journal Of Disability Research 2024mentioning

confidence: 79%

IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

Almalki,

Alnfiai,

Al-Wesabi

et al. 2024

Journal of Disability Research

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“…A brief comparative assessment of the ODRNN-HAR technique with current approaches is made in Table 3 and Figure 9 (Duhayyim, 2023). Based on accu y , the ODRNN-HAR technique reaches higher accu y of 99.49%, while the ODRNN-HAR, IPODTL-HAR, RF, NNN, SVM, ANN, and LSTM models yield lower accu y of 99.10, 86.18, 87.50, 88.81, 91.83, and 93.97%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Alotaibi,

Alnfiai,

Al-Wesabi

et al. 2023

Journal of Disability Research

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Aging is related to a decrease in the ability to execute activities of day-to-day routine and decay in physical exercise, which affect mental and physical health. Elderly patients or people can depend on a human activity recognition (HAR) system, which monitors the activity interventions and patterns if any critical event or behavioral changes occur. A HAR system incorporated with the Internet of Things (IoT) environment might allow these people to live independently. While the number of groups of activities and sensor measurements is enormous, the HAR problem could not be resolved deterministically. Hence, machine learning (ML) algorithm was broadly applied for the advancement of the HAR system to find the patterns of human activity from the sensor data. Therefore, this study presents an Optimal Deep Recurrent Neural Networks for Human Activity Recognition (ODRNN-HAR) on Elderly and Disabled Persons technique in the IoT platform. The intension of the ODRNN-HAR approach lies in the recognition and classification of various kinds of human activities in the IoT environment. Primarily, the ODRNN-HAR technique enables IoT devices to collect human activity data and employs Z-score normalization as a preprocessing step. For effectual recognition of human activities, the ODRNN-HAR technique uses the DRNN model. At the final stage, the optimal hyperparameter adjustment of the DRNN model takes place using the mayfly optimization (MFO) algorithm. The result analysis of the ODRNN-HAR algorithm takes place on benchmark HAR dataset, and the outcomes are examined. The comprehensive simulation outcomes highlighted the improved recognition results of the ODRNN-HAR approach in terms of different measures.

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“…SVM is a popular supervised machine learning technique used for both classification and regression tasks; it is based on the kernel method [ 52 ]. Because of this, we set out to optimize the SVM hyperparameters in search of the kernel function and parameters that would provide the most reliable model [ 53 , 54 ]. Using a random starting point in the hyperparameter space, the Bayesian technique iteratively assesses prospective hyperparameter configurations in light of the existing model to see if any of them enhance the model.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Elshewey

Shams

El-Rashidy

et al. 2023

Sensors

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Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Cited by 3 publications

References 22 publications

IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

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