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Allocation of Repairable and Replaceable Components for a System Availability Using Selective Maintenance with Probabilistic Maintenance Time Constraints

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American Journal of Operations Research 2011年第3期1卷 147-154页

作者： Irfan Ali mohammed faisal khan Yashpal Singh Raghav Abdul Bari 不详

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

关键词： Selective Maintenance Availability Optimization and Allocation Chance’s Constraints

Allocation in Multivariate Stratified Surveys with Non-Linear Random Cost Function

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American Journal of Operations Research 2012年第1期2卷 100-105页

作者： mohammed faisal khan Irfan Ali Yashpal Singh Raghav Abdul Bari CSIRO Department of Mathematics Integral University Lucknow India Department of Statistics & Operations Research Aligarh Muslim University Aligarh India

In this paper, we consider an allocation problem in multivariate surveys with non-linear costs of enumeration as a problem of non-linear stochastic programming with multiple objective functions. The solution is obtained through Chance Constrained programming. A different formulation of the problem is also presented in which the non-linear cost function is minimised under the precision constraints on estimates of various characters. The solution is then obtained by using Modified E-model. A numerical example is solved for both the formulations.

关键词： Stratified Survey Optimum Allocation Compromise Allocation Stochastic Programming Chance Constraints Modified E-Model

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes mohammed Zakariah Syed Umar Amin Zafar Iqbal khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify *** NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

The Impact of Simulation Education on Self-Efficacy in Pre-Registration Nursing Students

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Open Journal of Nursing 2024年第1期14卷 51-76页

作者： Ahmed A. Hakami Aisha Hussin Rabie Sultan Ghormallah M. Alzahrani faisal mohammed Alnakhilan Khalid Awaidhalharbi College of Nursing Umm Al-Qura University Makkah Saudi Arabia Al-Noor Specialist Hospital Makkah Saudi Arabia (MOH) Ministry of Health King Fahad Hospital Al Baha Saudi Arabia (MOH) Ministry of Health Eradah Complex Riyadh Saudi Arabia King Salman Medical City Al Madinah Saudi Arabia

This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-efficacy of undergraduate pre-registration nursing students. The second aim of this study is to explore additional outcome variables that were examined in the previous studies. Five electronic databases were searched systematically. These databases were MEDLINE, CINAHL Plus, Scopus, Embase and PsycINFO. The PICO model was employed to identify the search terms, with a thesaurus being used to provide synonyms. Reference lists of relevant articles were examined and hand searches of journals were also undertaken. The quality of each study was assessed using the Simulation Research Rubric (SRR). A total of 11 studies were included. All studies explored the impact of simulation education on undergraduate pre-registration nursing. Six studies explored nursing students’ competence and performance and two papers examined their critical thinking. Problem solving, learning motivation, communication skills and knowledge acquisition were examined once. The majority of studies indicated that simulation training has a positive impact on pre-registration nursing students’ self-efficacy and other outcome variables. Furthermore, the study results indicate that simulation training is more dependable than traditional training, and students were extremely satisfied with the simulation training. However, most of the studies included in this review had several gaps, including study design, sample size and dissimilarities between the scales used. Further research with large samples, reliable and valid instruments, and outcomes measures (such as critical thinking and transferability of skills) is required to provide better insight into the effectiveness of simulation in undergraduate nursing education. .

关键词： Simulation Education Self-Efficacy Pre-Registration Nursing Students Clinical Skills Undergraduate Nursing Education Teaching Techniques Decision-Making

Oral candidiasis and potential risk factors among disabled and nondisabled in Al-Baha region, Saudi Arabia

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World Journal of Clinical Cases 2024年第27期12卷 6077-6086页

作者： Abdullah Ali H Alzahrani Nagesh Bhat Pankaj Kukreja Eltayeb mohammed Alhassan Abdallah Ibrahim A Mudawi faisal A Alzahrani Mohammad A Albanghali Department of Dental Health Faculty of Applied Medical SciencesAl-Baha UniversityAl Bahah 65731Saudi Arabia Department of Preventive Dental Sciences School of DentistryAlBaha UniversityAl Bahah 65731Saudi Arabia Department of Biomedical Dental Sciences School of DentistryAlBaha UniversityAl Bahah 65731Saudi Arabia Department of Medical Services General Administration Public SecurityThe Saudi Ministry of InteriorRiyadh 11235Saudi Arabia Department of Public Health Faculty of Applied Medical SciencesAl-Baha UniversityAl Bahah 65731Saudi Arabia

BACKGROUND Oral candidiasis(OC)is an oral health disease that could influence patients’oral health quality of *** To estimate prevalence of OC among disabled and non-disabled individuals and its potential risk factors in the Al-Baha region,Saudi *** An observational cross-sectional study was carried out among 148 disabled and non-disabled *** technique of concentrated oral rinse employing the Sabouraud Dextrose Agar medium accompanied with 0.05%chloramphenicol was conducted to assess and isolate *** examination using the World Health Organization guidelines was conducted to examine participants’oral hea-lth status.A pre-designed questionnaire was also used to evaluate sociodemo-graphic,medical history,and oral hygiene habits of the studied *** Out of 148 participants(n=57,38%)had colonized *** of the studied population had visible Candida ***,Candida was found in the oral rinses without the subject presenting any lesions or issues caused by Candida(asymptomatic colonization).The most common prevalent OC among participants were Candida albicans,Candida glabrata,Candida dubliniensis,Candida krusei,Candida tropicalis,and Candida parapsilosis(n=35,61%;n=8,14%;n=6,10%;n=5,9%;n=2,4%;and n=1,2%)***,smoking,poor plaque,and gingival status were key potential risk factors that significantly associated with candida’s density and presence(P=0.001,P=0.001,P=0.01,and P=0.01)*** status had no statistically significant effect on presence and density of *** The prevalence of OC is almost third of the studied population;thus,may provoke a need to develop preventive strategies to reduce the OC rate and establish solid treatment plans.

关键词： Oral health Candida Oral candidiasis Dental public health Disabled Disability Risk factors Epidemiology

Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman mohammed Awad mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

Quality Assessment of Online Patient Education Materials for Diabetic Foot

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Journal of Biosciences and Medicines 2024年第9期12卷 28-41页

作者： Dana Hendie Eyman Haj-Ali Muhammad Candragupta Jihwaprani Hagar Khaled Marah Alrahal faisal Zain mohammed Al-Zabidi Nazmus Saquib Juliann Saquib College of Medicine Sulaiman Al Rajhi University Bukaryiah Saudi Arabia

Background: The quality of online Arabic educational materials for diabetic foot syndrome (DFS) is unknown. This study evaluated Arabic websites as patients’ sources of information for DFS. Methods: The study assessed patient-related websites about DFS using a modified Ensuring Quality of Information for Patients (EQIP) tool (score 0 - 35). Specific terms were searched in Google to identify DFS websites;eligibility criteria were applied to 20 pages of search results to select the included websites. Data on country of origin, source types and subtypes, and website traffic were extracted. Additional therapeutic information regarding prevention and conservative, pharmacological, and surgical treatments was also recorded and analyzed. Results: Among 559 websites, 157 were eligible for inclusion. The median EQIP score was 16 out of 35, indicating poor quality in one of three domains (content, identification, or structure). Most sources originated from Arab countries (75.8%) were non-governmental (94.9%), and were medical information websites (46.5%). High-scoring websites were significantly more likely than low-scoring websites to describe information on prevention (30.9% vs. 2.9%, p = 0.001), conservative treatment (34.1% vs. 13%, p = 0.002), or pharmacological treatment (32.5% vs. 16.8%, p = 0.024). There were increased odds of scoring high if a website provided information on prevention (OR = 12.9, 95% CI [1.68 - 98.57], p = 0.014). Conclusion: Most Arabic online patient information on DFS is of poor quality. Quality control measures are needed to ensure accurate health information for the public.

关键词： Diabetic Foot Disease Diabetic Foot Ulcer Online Resources Patient Education Patient Education Materials

AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP