Estimating the Impact of COVID-19 Pandemic on the Research Community in the Kingdom of Saudi Arabia

Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

Hybrid Computational Modeling for Web Application Security Assessment

Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era.This has led to digital revolution,and in this context,the hardwired technologies in the software industry play a significant role However,from the beginning,software security remains a serious issue for all levels of stakeholders.Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data,compromising the organizations’reputation that translates into,financial losses as well.Most of the data breaches are financially motivated,especially in the healthcare sector.The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web.Therefore,security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyber-attacks.The aim of this work is to provide efficient and effective healthcare web application security assessment.The study has worked with the hybrid computational model of Multi-Criteria Decision Making(MCDM)based on Analytical Hierarchy Process(AHP)and Technique for Order of Preference by Similarity to Ideal-Solutions(TOPSIS)under the Hesitant Fuzzy(HF)environment.Hesitant fuzzy sets provide effective solutions to address decision making problems where experts counter hesitation to make a decision.The proposed research endeavor will support designers and developers in identifying,selecting and prioritizing the best security attributes for web applications’development.The empirical analysis concludes that Robustness got highest priority amongst the assessed security attributes set followed by Encryption,Authentication,Limit Access,Revoke Access,Data Validation,and Maintain Audit Trail.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the web application security.The study also establishes guidelines which the developers can refer for the identification and prioritization of security attributes to build more secure and trustworthy web-based applications.

Healthcare Device Security Assessment through Computational Methodology

The current study discusses the different methods used to secure healthcare devices and proposes a quantitative framework to list them in order of significances.The study uses the Hesitant Fuzzy(HF),Analytic Hierarchy Process(AHP)integrated with Fuzzy Technical for Order Preference by Similarities to Ideal Solution(TOPSIS)to classify the best alternatives to security techniques for healthcare devices to securing the devices.The technique is enlisted to rate the alternatives based on the degree of satisfaction of their weights.The ranks of the alternatives consequently decide the order of priority for the techniques.A1 was the most probable alternative of all the alternatives,according to the ranks of the alternatives acquired.This means that the security of A2 healthcare devices is the greatest of all the alternatives picked.A corroborative guide for the developers and the makers in quantitatively determining the security of healthcare devices to engineer efficacious devices will be the findings drawn up with the assistance of the proposed framework.The assessments performed using the proposed framework are systematic,precise,and definitive.Therefore,the results of the present empirical analysis are a stronger and accurate choice than the manual assessment of the device’s security.