Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media

Fouad, Yasser; Osman, Ahmed M.; Hassan, Samah A. Z.; El-Bakry, Hazem M.; Elshewey, Ahmed M.

doi:10.14569/ijacsa.2023.0140728

Cited by 3 publications

(2 citation statements)

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“…The experiments were executed on a computer that operates on Microsoft Windows 10, with an Intel Core i5 CPU and 16 GB of RAM. To assess the effectiveness of our models, we employed several metrics, including accuracy, recall, precision, and F1-score [61,62]. These metrics were used to evaluate and measure the model's performance in different aspects, such as correctly identifying true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).…”

Section: Resultsmentioning

confidence: 99%

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Elshewey,

Shams,

Tawfeek

et al. 2023

Diagnostics

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The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model’s accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system’s efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

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Section: Resultsmentioning

confidence: 99%

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Elshewey,

Shams,

Tawfeek

et al. 2023

Diagnostics

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show abstract

“…Five indicators are used to evaluate the predictive capabilities of the presented prediction models and assess their performances. Each model's performance is evaluated using metrics such as accuracy, training score, testing score, F-measure, recall, precision, and the receiver operating characteristic (ROC) curve [49]. Accuracy is computed using Equation ( 15):…”

Section: Evaluation Metricsmentioning

confidence: 99%

Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model

Alkhammash,

Assiri,

Nemenqani

et al. 2023

Biomimetics

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During the pandemic of the coronavirus disease (COVID-19), statistics showed that the number of affected cases differed from one country to another and also from one city to another. Therefore, in this paper, we provide an enhanced model for predicting COVID-19 samples in different regions of Saudi Arabia (high-altitude and sea-level areas). The model is developed using several stages and was successfully trained and tested using two datasets that were collected from Taif city (high-altitude area) and Jeddah city (sea-level area) in Saudi Arabia. Binary particle swarm optimization (BPSO) is used in this study for making feature selections using three different machine learning models, i.e., the random forest model, gradient boosting model, and naive Bayes model. A number of predicting evaluation metrics including accuracy, training score, testing score, F-measure, recall, precision, and receiver operating characteristic (ROC) curve were calculated to verify the performance of the three machine learning models on these datasets. The experimental results demonstrated that the gradient boosting model gives better results than the random forest and naive Bayes models with an accuracy of 94.6% using the Taif city dataset. For the dataset of Jeddah city, the results demonstrated that the random forest model outperforms the gradient boosting and naive Bayes models with an accuracy of 95.5%. The dataset of Jeddah city achieved better results than the dataset of Taif city in Saudi Arabia using the enhanced model for the term of accuracy.

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An AI healthcare ecosystem framework for Covid-19 detection and forecasting using CronaSona

Hassan

2024

Med Biol Eng Comput

Self Cite

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The primary purpose of this paper is to establish a healthcare ecosystem framework for COVID-19, CronaSona. Unlike some studies that focus solely on detection or forecasting, CronaSona aims to provide a holistic solution, for managing data and/or knowledge, incorporating detection, forecasting, expert advice, treatment recommendations, real-time tracking, and finally visualizing results. The innovation lies in creating a comprehensive healthcare ecosystem framework and an application that not only aids in COVID-19 diagnosis but also addresses broader health challenges. The main objective is to introduce a novel framework designed to simplify the development and construction of applications by standardizing essential components required for applications focused on addressing diseases. CronaSona includes two parts, which are stakeholders and shared components, and four subsystems: (1) the management information subsystem, (2) the expert subsystem, (3) the COVID-19 detection and forecasting subsystem, and (4) the mobile tracker subsystem. In the proposed framework, a CronaSona app. was built to try to put the virus under control. It is a reactive mobile application for all users, especially COVID-19 patients and doctors. It aims to provide a reliable diagnostic tool for COVID-19 using deep learning techniques, accelerating diagnosis and referral processes, and focuses on forecasting the transmission of COVID-19. It also includes a mobile tracker subsystem for monitoring potential carriers and minimizing the virus spread. It was built to compete with other applications and to help people face the COVID-19 virus. Upon receiving the proposed framework, an application was developed to validate and test the framework’s functionalities. The main aim of the developed application, CronaSona app., is to develop and test a reliable diagnostic tool using deep learning techniques to avoid increasing the spread of the disease as much as possible and to accelerate the diagnosis and referral of patients by detecting COVID-19 features from their chest X-ray images. By using CronaSona, human health is saved and stress is reduced by knowing everything about the virus. It performs with the highest accuracy, F1-score, and precision, with consecutive values of 97%, 97.6%, and 96.6%. Graphical Abstract

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Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media

Cited by 3 publications

References 0 publications

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model

An AI healthcare ecosystem framework for Covid-19 detection and forecasting using CronaSona

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