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Security Test Case Prioritization through Ant Colony Optimization Algorithm

Computer Systems Science & Engineering 2023年第12期47卷 3165-3195页

作者： Abdulaziz Attaallah Khalil al-Sulbi areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Computer Application Integral UniversityLucknowUttar Pradesh226026India Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar Pradesh226025India

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software *** of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test *** effectively applying test case prioritization,both the time and cost required for developing secure software can be *** paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic *** performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional *** has been applied to a Mobile Payment Wallet application to validate the proposed *** results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD *** ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering *** findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

关键词： Confidentiality integrity authentication non-repudiation resilience authorization Ant Colony Optimization algorithm

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Inference on Constant-Partially Accelerated Life Tests for Mixture of Pareto Distributions under Progressive Type-II Censoring

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Open Journal of Statistics 2017年第2期7卷 323-346页

作者： Tahani A. Abushal areej M. AL-Zaydi Department of Mathematics Umm AL-Qura University Mecca KSA Department of Mathematics Taif University Taif KSA

The main purpose of this paper is to obtain the inference of parameters of heterogeneous population represented by finite mixture of two Pareto (MTP) distributions of the second kind. The constant-partially accelerated life tests are applied based on progressively type-II censored samples. The maximum likelihood estimates (MLEs) for the considered parameters are obtained by solving the likelihood equations of the model parameters numerically. The Bayes estimators are obtained by using Markov chain Monte Carlo algorithm under the balanced squared error loss function. Based on Monte Carlo simulation, Bayes estimators are compared with their corresponding maximum likelihood estimators. The two-sample prediction technique is considered to derive Bayesian prediction bounds for future order statistics based on progressively type-II censored informative samples obtained from constant-partially accelerated life testing models. The informative and future samples are assumed to be obtained from the same population. The coverage probabilities and the average interval lengths of the confidence intervals are computed via a Monte Carlo simulation to investigate the procedure of the prediction intervals. Analysis of a simulated data set has also been presented for illustrative purposes. Finally, comparisons are made between Bayesian and maximum likelihood estimators via a Monte Carlo simulation study.

关键词： Pareto Distribution Finite Mixtures Constant—Partially ALT Progressive Type-II Censoring Bayesian Estimation Maximum Likelihood Estimation Bayesian Prediction the Two-Sample Prediction MCMC

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In Silico Evaluation for the Inhibitory Action of Curcumin Derivatives on the SARS-CoV-2 Proteins

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Journal of Biosciences and Medicines 2022年第4期10卷 63-76页

作者： areej Jaradat Yasmeen Salameh Hilal Zaid Siba Shanak Faculty of Sciences Arab American University Jenin Palestine Faculty of Medicine Arab American University Jenin Palestine Qasemi Research Center Al-Qasemi Academic College Baqa al-Gharbia Israel

Purpose of the Study: COVID-19 is caused by the SARS-CoV-2 virus that had a global pandemic spread in the last two years. Symptoms of the disease include respiratory distress and, in severe cases may consequently lead to death. Blocking the viral proteins can aid in treating this disease and alleviating its symptoms. Two target proteins of the coronavirus that are hot spots in drug discovery are the papain-like protease PL-pro and the main protease M-pro. PL-pro is an enzyme that is required for processing viral polyproteins to generate a functional replicase complex and enable viral spread. M-pro is the major protease of SARS-CoV-2, which is a keystone in viral replication and transcription. Methods: In this study, we shed the light on the route of targeting viral proteins for disease alleviation, by targeting the two aforementioned enzymes, PL-pro and M-pro using in silico studies. Docking experiments, using AutoDock algorithms, were performed to predict the inhibitory effect of recently produced synthetic derivatives of curcumin on the viral proteins. Results: Most of the curcumin derivatives have shown variable levels of inhibition, e.g., S1 - S6, mainly on the papain-like protease, and to a lesser extent on the main protease. Conclusion: The results indicated that curcumin derivatives can be potent anti-viral drug of SARS-CoV-2, namely targeting the papain-like protease.

关键词： Natural Compounds Curcumin Derivatives In Silico Docking SARS-CoV-2 COVID-19 Phytochemicals PL-Pro M-Pro Coronavirus

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The Translation of Perinatal Anxiety Screening Scale (PASS) into Arabic

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Open Journal of Nursing 2020年第4期10卷 367-380页

作者： Eqbal Mohammad Alfarajat areej Othman Inaam Khalaf Maternal and Neonatal Health Nursing School of Nursing University of Jordan Amman Jordan Faculty of Nursing Al-Hussein bin Talal University Maan Jordan Maternal and Child Health Nursing Department School of Nursing University of Jordan Amman Jordan

Background: early detection of perinatal anxiety using appropriate measures helps in reducing maternal and fetal complication. WHO guidelines for instrument translation and adaptation provide rigor and transparent method for Perinatal Anxiety Screening Scale (PASS) translation and expand the knowledge in diversity cultural contexts. Aim: to describe the process of cultural adaptation of the PASS into the Jordanian context based on the WHO framework for instrument translation and adaptation. Methods: PASS was completed by a convenience sample of 31 pregnant women. In which PASS went through WHO framework for instrument translation and adaptation process includes forward translation, expert panel, blind back translation, pre testing and cognitive interview, and the final version is ready for piloting. Some comments were added to three items by five expert panel, then the modified version was ready for piloting. Result: the internal consistency reliability of PASS was 0.869 and five experts who reviewed PASS confirmed the scale appropriateness and clarity after a slight modification to three items. Participants found PASS in general easy to complete but some of them found difficulties in understanding two items located in the Perfectionism, control and trauma subscale where they need an explanation of their meaning to answer them. Conclusion: using WHO guidelines for instruments translation considered a rigorous method and revealed that PASS is reliable and valid tool to be used within the Jordanian context to measure perinatal anxiety. However, the focus on explaining items 11 and 14 to participants is important due to difficulty in understanding their meaning.

关键词： Perinatal Anxiety Screening Scale (PASS) WHO Translation Guidelines Arabic Translation Jordan Reliability Validity

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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Computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC Algorithm

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Open Journal of Statistics 2012年第3期2卷 356-367页

作者： Tahani A. Abushal areej M. Al-Zaydi Department of Mathematics Taif University Taif KSA Department of Mathematics Umm Al-Qura University Makkah Al-Mukarramah KSA

This article considers the problem in obtaining the maximum likelihood prediction (point and interval) and Bayesian prediction (point and interval) for a future observation from mixture of two Rayleigh (MTR) distributions based on generalized order statistics (GOS). We consider one-sample and two-sample prediction schemes using the Markov chain Monte Carlo (MCMC) algorithm. The conjugate prior is used to carry out the Bayesian analysis. The results are specialized to upper record values. Numerical example is presented in the methods proposed in this paper.

关键词： Mixture Distributions Rayleigh Distribution Generalized Order Statistics Record Values MCMC

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Portable Hemodialysis Machine for Chronic Hemodialysis in Hospital—Advantages and Future Uses

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Open Journal of Nephrology 2020年第2期10卷 158-169页

作者： areej Ibrahim Alhazmi Bilal Mohsin Said S. A. Khamis Division of Nephrology King Fahd General Hospital Jeddah Kingdom of Saudi Arabia Faculty of Medicine Menoufia University Menoufia Egypt

Introduction: Nx stage one machine results can help to enhance the clinical efficiency and shortage concerning the use of home hemodialysis. Therefore, the study investigates effectiveness of Nx stage machine for intermittent conventional hemodialysis in hospital for inpatients and outpatients to replace conventional hemodialysis in stable chronic hemodialysis patients. Methods: Prospective observatory study was conducted in Nephrology Division, King Fahd General Hospital from April 1 to June 1 2018. Patients with ESRD, who were dependent on dialysis and maintained on regular hemodialysis were recruited. Fifty-two sessions of hemodialysis were performed. The patients were given intermittent dialysis using Nx stage one machine. Pre and Post dialysis investigations and body weight were taken, and patients were observed for any complications during or post-dialysis. Findings: The primary endpoint ultrafiltration with a goal to remove 1 - 4 kg was achieved without any technical difficulty. Secondary endpoint for potassium reduction levels was achieved with mean SD post-dialysis 3.53 ± 0.3, and the tertiary endpoint was achieved in a patient less than 50 kg weight with spKt/V above 1.2. It was difficult to achieve spKt/V in patients with weight more than 60 kg due to limitations in the duration of sessions or dialysate volume. Target spkT/V was achieved for patients with body weight less than 50 kg;however, it was not achieved among the patients with body weight more than 60 kg. Discussion: Nx stage one machine can replace conventional intermittent dialysis in terms of ultrafiltration and Potassium correction;however, further investigation is needed for urea clearance.

关键词： Body Weight Hemodialysis Nx Stage One S Nx Stage One SPKT/V Urea Reduction Ratio (URR)

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Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions

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Open Journal of Statistics 2015年第6期5卷 585-599页

作者： Abd EL-Baset A. Ahmad areej M. Al-Zaydi Department of Mathematics Assiut University Assiut Egypt Department of Mathematics Taif University Taif Saudi Arabia

This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly?type II censored sample. We consider the one sample prediction and two sample prediction techniques. Bayesian prediction intervals for the median of future sample of generalized order statistics having odd and even sizes are obtained. Our results are specialized to ordinary order statistics and ordinary upper record values. A mixture of two Gompertz components model is given as an application. Numerical computations are given to illustrate the procedures.

关键词： Generalized Order Statistics Bayesian Prediction Heterogeneous Population Doubly Type II Censored Samples One- and Two-Sample Schemes

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Parallel Technologies with Image Processing Using Inverse Filter

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Journal of Computer and Communications 2024年第1期12卷 110-119页

作者： Rahaf Alsharhan areej Muheef Yasmin Al Ibrahim Afnan Rayyani Yasir Alguwaifli College of Computer Science and Information Technology (CCSIT) Imam Abdulrahman Bin Faisal University (IAU) Dammam Saudi Arabia

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

关键词： Parallel Parallelization Image Processing Inverse Filtering OpenMP Race Conditions

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Inferences under a Class of Finite Mixture Distributions Based on Generalized Order Statistics

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Open Journal of Statistics 2013年第4期3卷 231-244页

作者： Abd EL-Baset A. Ahmad areej M. AL-Zaydi Department of Mathematics Assiut University Assiut Egypt Department of Mathematics Taif University Taif KSA

The main purpose of this paper is to obtain estimates of parameters, reliability and hazard rate functions of a heterogeneous population represented by finite mixture of two general components. The doubly Type II censoring of generalized order statistics scheme is used. Maximum likelihood and Bayes methods of estimation are used for this purpose. The two methods of estimation are compared via a Monte Carlo Simulation study.

关键词： Generalized Order Statistics Bayes Estimation Heterogeneous Population Monte Carlo Integration Monte Carlo Simulation

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