A5058150486- Artificial Intelligence in Healthcare
- Machine Learning in Healthcare
- Chronic Disease Management Strategies
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Text and Document Classification Technologies
- Network Security and Intrusion Detection
- Dental Research and COVID-19
- Time Series Analysis and Forecasting
- Soil and Land Suitability Analysis
- Anomaly Detection Techniques and Applications
- Data-Driven Disease Surveillance
- Misinformation and Its Impacts
- Antibiotic Use and Resistance
- Imbalanced Data Classification Techniques
- Dental Anxiety and Anesthesia Techniques
University of Jeddah
2020-2024
Durham University
2018-2021
With the increase of cyber-attacks and security threats in recent decade, it is necessary to safeguard sensitive data provide robust protection information systems computer networks. In this paper, an anomaly-based network outlier detection system (NODS) proposed optimized check classify incoming traffic stream's behaviours that affect The NODS has high classification efficiency. Network connection events classified as outliers are reported admin drop block its packets. NSL-KDD CICIDS2017...
In this study, we present a framework for flu-prediction/detection based on the available data of Social Networking Sites (SNS). The uses state-of-the-art text classifier, which is FastText, to classify Twitter posts into flu-related or flu-unrelated posts. FastText trained and tested using pre-labeled dataset utilizing features sentiment analysis predefined keyword occurrences in addition textual features. Results show that improves accuracy, efficiency flu disease surveillance systems use...
Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed recorded in hospital systems. Making use of data to help physicians evaluate the mortality risk in-hospital patients provides an invaluable source information that can ultimately with improving healthcare services. In particular, quick accurate predictions be valuable for who making decisions about interventions. this work we introduce a predictive Deep Learning model patients. Stacked...
Background Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic records data identifying ultimately provide better outcomes. Objective Our study investigated performance to forecast HbA1c levels by employing several machine learning models. We also examined use patient record longitudinal in...
Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate effect glycated hemoglobin (HbA
This study aimed to explore an affordable and easily accessible alternative the Munsell colour system, while also assessing reliability of data output from chart when used by two different individuals. The research involved reevaluation 28 stored soil samples using both system a app installed on android phone. resulting was then analyzed compared with existing data. Three models emerged this study: generated (referred as MCS1), produced MCS2), capture SCC) app. All three sets were...
<sec> <title>BACKGROUND</title> Antibiotics are thus considered essential tools in the therapeutic armamentarium of dental practice, and their proper use is fundamental ensuring an effective clinical setting. However, if not used appropriately, antibiotics can cause harm by contributing to development antimicrobial resistance, issue growing concern that threatens our ability treat common infectious diseases. </sec> <title>OBJECTIVE</title> The aim study was evaluate Dentists' knowledge,...
<sec> <title>BACKGROUND</title> Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate effect glycated hemoglobin (HbA<sub>1c</sub>) elevation on prediction diabetes onset. However, there is still a need for validation these models using EHR data collected from different populations. </sec> <title>OBJECTIVE</title> The aim this study perform...
<sec> <title>BACKGROUND</title> Predicting the risk of glycated hemoglobin (HbA<sub>1c</sub>) elevation can help identify patients with potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic records data identifying ultimately provide better outcomes. </sec> <title>OBJECTIVE</title> Our study investigated performance to forecast HbA<sub>1c</sub> levels by...