Browsing by Author "Shikah J. Alsunaidi"

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Applications of Big Data Analytics to Control COVID-19 Pandemic
(2021) Shikah J. Alsunaidi; Kawther S. Alqudaihi; Abdullah M. Almuhaideb; Nehad M. Ibrahim; Fatema S. Shaikh; Fahd A. Alhaidari; Irfan Ullah Khan; Nida Aslam; Mohammed S. Alshahrani
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications
Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities
(2021) Alqudaihi K.S. and Aslam N. and Kha I.U. and Almuhaide A.M. and Alsunaidi S.J. and Ibrahim N.M.A.R. and Alhaidari F.A. and Shaikh F.S. and Alsenbel Y.M. and Alalharith D.M. and Alharthi H.M. and Alghamdi W.M. and Alshahrani M.S.; Shikah J. Alsunaidi; Yasmine M. Alsenbel; Dima M. Alalharith; Hajar M. Alharthi; WEJDAN M. ALGHAMDI; Nida Aslam; Irfan Ullah Khan; Abdullah M. Almuhaideb; Nehad M. Ibrahim; Fahd A. Alhaidari; Fatema S. Shaikh; Mohammed S. Alshahrani
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people’s well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
E-Triage Systems for COVID-19 Outbreak: Review and Recommendations
(2021) Shikah J. Alsunaidi; Hajar M. Alharthi; Yasmine M. Alsenbel; Dima M. Alalharith; Fahd A. Alhaidari; Abdullah M. Almuhaideb; Nehad M. Ibrahim; Nida Aslam; Irfan Ullah Khan; Fatema S. Shaikh
With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patientsâ€™ conditions and identifying their severity levels
Homoglyph Attack Detection Model Using Machine Learning and Hash Function
(2022) Almuhaideb, Abdullah M.; Aslam, Nida; Sarah Altamimi; Shooq Alothman; Alothman, Shooq; Waad Aldosari; Aldosari, Waad; Nida Aslam; Alissa, Khalid A.
Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection.
Investigation of the optimal method for generating and verifying the Smartphone’s fingerprint: A review
(2022) Abdullah M. Almuhaideb; Shikah J. Alsunaidi
The technical transformation and transfer of most services to digital platforms require that everyone has an electronic device connected to the Internet to assist them accomplish their tasks. Smartphones are one of the best options for everyone because of their small size and ease of transport, in addition to their high capabilities equivalent to a personal computer. It is necessary to identify these devices, usually by checking their IMEI (International Mobile Equipment Identity), to provide and manage several services like the cellular network service. On the other hand, criminals and counterfeiters can manipulate this identity to hide the device and prevent it from being tracked or to make high profits from selling substandard devices. Therefore, several recent proposals have emerged to create a strong fingerprint for use in device identification purposes. This paper reviews and discusses the existing methods to generate a device identity and defines their gaps. Also, it classifies the methods into four categories based on the used technique, namely PUF, machine learning, comparison approach, and sensor calibration. Additionally, it introduces the factors to consider when choosing the technique of device identification. It provides a list of possible attacks on each technology used in device identification methods.
Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia
(2022) KAWTHER SALEH ALQUDAIHI; Almuhaideb, Abdullah M.; Alsunaidi, Shikah J.; Alqudaihi, Kawther S.; Alamoudi, Fatimah A.; دينا عبدالله العباد; Abdullah M. Almuhaideb; Alshahrani, Mohammed S.
The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients’ need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.
Sensor-based identification to detect counterfeit smartphones using Blockchain
(2022) Abdullah M. Almuhaideb; Shikah J. Alsunaidi
The rapid growth of the technology sector and the increasing need to own smartphones contributed to the increase in the rate of smartphones theft, as well as the sale of counterfeit or substandard devices. The Internet provides many tools and tutorials that show the criminal how to manipulate the identity of a stolen device to prevent tracking or detection, as well as to help him/her unlock/unblock the device. Consequently, the likelihood of recovering or finding stolen smartphones is significantly reduced. There are many challenges during the development of a security solution to this problem, the most important of which is the limited smartphone resources. In this paper, we propose a new device identification approach considering the challenges of the smartphone identification process. It utilizes the sensor calibration-based identity as a device fingerprint and the Blockchain technology to manage the fingerprint. The comparisons with related solutions showed that our proposed method can balance between all solution requirements including compatibility with resource-constrained devices, fulfilling of the device identification, and the security requirements to detect counterfeit smartphones.