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| 1 | Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes显示文摘Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success. | Muhammad Hasnain Imran Ghani Seung Ryul Jeong Muhammad Fermi Pasha Sardar Usman Anjum Abbas | 2023 | Computers, Materials & Continua2023，，1： | 1 |
| 2 | An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence显示文摘Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain.Several solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent issues.Hence,we propose a novel framework and implemented an attribute-based access control system using blockchain.Moreover,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security reasons.The current methods only focus on the related clinical records received from a medical diagnosis.Moreover,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare data.We have used a neural network and classifier to categorize the user access to our proposed system.Our proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare system.Experimental results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time. | Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 3 | User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework显示文摘As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in real-time.Failing to do so may cause a serious loss of reputation as well as business.At the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware early.The ability to learn tasks sequentially is crucial to the development of artificial intelligence.Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational resources.As defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)learning.Hence,an intelligent attack/malware detection system with on-the-fly learning capability is required.For this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(s.SCASHM)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly learning.The s.SCASHM consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)platform.The s.SCASHMis implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies.The experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning. | Rahmat Budiarto Ahmad A.Alqarni Mohammed YAlzahrani Muhammad Fermi Pasha Mohamed FazilMohamed Firdhous Deris Stiawan | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 4 | Combined measures to control the COVID-19 pandemic in Wuhan, Hubei, China: A narrative review显示文摘Coronavirus disease 2019(COVID-19)is an emerging disease caused by the coronavirus,SARS-CoV-2,which leads to severe respiratory infections in humans.COVID-19 was first reported in December 2019 in Wuhan city,a populated area of the Hubei province in China.As of now,Wuhan and other cities nearby have become safe places for locals.The rapid control of the spread of COVID-19 infection was made possible due to several interventions and measures that were undertaken in Wuhan.This narrative review study was designed to evaluate the emerging literature on the combined measures taken to control the COVID-19 pandemic in Wuhan city.Science Direct,Springer,Web of Science,and the PubMed data repositories were searched for studies published between December 1,2019,and June 07,2020.The referred“preferred reporting items for systematic reviews and meta-analyses”(PRISMA)protocol was used to conduct this narrative review.A total of 330 research studies were found as a result of the initial search based on exclusion and inclusion criteria,and 30 articles were chosen on final evaluation.It was discovered that the combined measures to control the spread of COVID-19 in Wuhan included cordon sanitaire,social distancing,universal symptom surveys,quarantine strategies,and transport restrictions.Based on the recommendations presented in this review study,existing policies with regard to combined measures and public health policies can be enforced by other countries to implement a rapid control procedure to control the spread of the COVID-19 pandemic. | Muhammad Hasnain Muhammad Fermi Pasha Imran Ghani | 2020 | Journal of Biosafety and Biosecurity2020，2，2： | 0 |
| 5 | An Ontology Based Test Case Prioritization Approach in Regression Testing显示文摘Regression testing is a widely studied research area,with the aim of meeting the quality challenges of software systems.To achieve a software system of good quality,we face high consumption of resources during testing.To overcome this challenge,test case prioritization(TCP)as a sub-type of regression testing is continuously investigated to achieve the testing objectives.This study provides an insight into proposing the ontology-based TCP(OTCP)approach,aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems.The proposed approach uses software metrics to examine the behavior of classes of software systems.It uses Binary Logistic Regression(BLR)and AdaBoostM1 classifiers to verify correct predictions of the faulty and non-faulty classes of software systems.Reference ontology is used to match the code metrics and class attributes.We investigated five Java programs for the evaluation of the proposed approach,which was used to achieve code metrics.This study has resulted in an average percentage of fault detected(APFD)value of 94.80%,which is higher when compared to other TCP approaches.In future works,large sized programs in different languages can be used to evaluate the scalability of the proposed OTCP approach. | Muhammad Hasnain Seung Ryul Jeong Muhammad Fermi Pasha Imran Ghani | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 6 | Performance Anomaly Detection in Web Services: An RNN-Based Approach Using Dynamic Quality of Service Features显示文摘Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data.Anomaly detection has been applied to areas such as fraud detection,intrusion detection systems,and network systems.In this paper,we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup.Three variants of recurrent neural networks-SimpleRNN,long short term memory,and gated recurrent unit are evaluated.The results reveal that the proposed method effectively detects anomalies in web services with high accuracy.The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics. | Muhammad Hasnain Seung Ryul Jeong Muhammad Fermi Pasha Imran Ghani | 2020 | Computers, Materials & Continua2020，，8： | 0 |
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