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

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

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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

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Rare earth based MgPm_(2)X_(4)(X=S,Se) spinel chalcogenides for spintronic and thermoelectric applications

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Journal of Rare earths 2024年第8期42卷 1577-1585,I0006页

作者： Tariq M.Al-Daraghmeh Omar Zayed Ghulam M.Mustafa Taharh Zelai Bisma Younas Hind Albalawi S.Bouzgarrou Othman Hakami Q.Mahmood Khaild I.Hussein Basic Sciences Department King Abdullah Ⅱ School of EngineeringPrincess Sumaya University For TechnologyP.O.Box 1438Al-jubaiha11941AmmanJordan Department of Physics Division of Science&TechnologyUniversity of EducationLahorePakistan Department of Physics Faculty of ScienceJazan University JazanSaudi Arabia Department of Physics The University of LahoreLahorePakistan Department of Physics College of SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia Department of Physics College of ScienceQassim UniversityPo.64BuraidahSaudi Arabia Laboratoire de Microelectronique et Instrumentation(UR 03/13-04) Faculte des Sciences de MonastirAvenue de I'Environnement 5000MonastirTunisia Department of Chemistry Faculty of ScienceJazan University JazanSaudi Arabia Basic&Applied Scientific Research Center Department of PhysicsCollege of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of Radiological Sciences College of Applied Medical SciencesKing Khalid UniversityAbha61421Saudi Arabia

In current report,the structural,magnetic,and thermoelectric properties of Re doped MgPm_(2)X_(4)(X=S,Se) spinels were *** energy difference in ferromagnetic and antiferromagnetic states reveals the stability of MgPm_(2)(S/Se)_(4) in the ferromagnetic *** co mputation of enthalpy of formation also ascertains thermodynamic stability of crystal ***-dependent band structure and density of states analysis reveal ferromagnetic semiconducting character showing different electronic behavior in both spin *** room temperature ferromagnetism,spin polarization and Curie temperature are estimated from exchange energies *** addition,exchange constants(N_(0)α and N_(0)β),exchange energy Δ_(x)(pd),crystal ifeld energy,and double exchange mechanism were studied to explore the magnetic ***,the electrical conductivity,thermal conductivity,Seebeck co-efficient,and power factor show effect on electrons spin and their potential for thermoelectric devices.

关键词： Rare earth elements Spintronic applications Thermoelectric effects exchange energies

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Bio-analysis of Saliva Using Paper Devices and Colorimetric Assays

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Journal of Analysis and Testing 2024年第1期8卷 114-132页

作者： Adhan Narasimhan Harshil Jain Kowshika Muniandy Raja Chinnappan Naresh Kumar Mani Microfluidics Sensors and Diagnostics(μSenD)LaboratoryCentre for MicrofluidicsBiomarkersPhotoceutics&Sensors(μBioPS)Department of BiotechnologyManipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India College of Medicine Alfaisal UniversityRiyadh 11533SaudiArabia Tissue/Organ Bioengineering&BioMeMS Lab Organ Transplant Centre of ExcellenceTransplant Research&Innovation DepartmentKing Faisal Specialist Hospital and Research CentreRiyadhSaudi Arabia

In light of the evolving nature of various diseases,time becomes a crucial factor in diagnosis and identifying the underlying causes.A point-of-care device provides a rapid diagnosis of a disease without using complex and advanced instruments,which are costly and difficult to transport.A paper-based device is a relatively frugal solution wherein the paper is used as a substratum in which the reactions are carried *** methods are non-invasive,and the sample collection is relatively *** is one such body fluid in which various biomarkers are present for numerous *** of saliva has attracted more attention recently due to its non-invasiveness and *** the discovery of clinical biomarkers from salivary analysis has the potential to revolutionize the healthcare sector by providing crucial information to monitor the health status of individuals and disease progression which enables personalized *** review provides the limitation of the traditional methods in clinical applications and highlights the significance of saliva as a non-invasive biological fluid that is a source of multiple biomarkers associated with various *** also provides insights into the different paper-based colorimetric microfluidic devices developed against salivary biomarkers in the past *** major challenges in the point-of-care application and the future prospects have been discussed as ***,we also emphasize the importance of this approach in dental disease diagnosis which is least explored and holds potential applications.

关键词： Paper-based assay Salivary analysis Non-invasive Point-of-care Body fluids Biomarkers Qualitative detection

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Genetic Variability and Phenotypic Correlations Study among Grain Quality Traits and Mineral elements Concentrations in Colored and Non-Colored Rice (Oryza sativa L.)

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Phyton-International Journal of experimental Botany 2024年第7期93卷 1733-1748页

作者： Adel A.Rezk Mohamed M.el-Malky Heba I.Mohamed Hossam S.el-Beltagi Agricultural Biotechnology Department College of Agricultural and Food ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Plant Pathology Research Institute Agriculture Research CenterGiza12619Egypt Rice Research and Training Center Field Crop Research InstituteAgriculture Research Center33717Egypt Biological and Geological Sciences Department Faculty of EducationAin Shams UniversityCairo11511Egypt Biochemistry Department Faculty of AgricultureCairo UniversityGiza12613Egypt

Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and *** revealed signiﬁcant differences for the quality traits *** every trait under study,the phenotypic coefﬁcient of variation was higher than the correspon-dence genotypic coefﬁcient of *** in a broad sense varied from 29.75%for grain length to 98.31%for the elongation *** percentage recovery had a highly signiﬁcant positive correlation with milling and head rice ***,milling percentage had a highly positive correlation with head rice *** amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose *** nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional *** the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new *** was a positive and signiﬁcant correlation between magnesium Mg,K,Zn and ***,K had a positive correlation with zinc Zn,Fe,and protein *** analysis was divided into two groups:theﬁrst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.

关键词： Amylose cluster analysis genotypes grain quality mineral nutrition rice

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Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

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Computers, Materials & Continua 2021年第7期68卷 805-819页

作者： Fawaz Waselallah Alsaade Theyazn H.H.Aldhyani Mosleh Hmoud Al-Adhaileh College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi deanship of e-learning and distance education king faisal university saudi arabia Al-AhsaSaudi Arabia

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of *** study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray *** test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS *** scenarios were considered to develop a system for predicting *** the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray *** segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting *** features were processed using SVM and *** the second scenario,a CNN transfer model(ResNet 50)was used to detect *** system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,***,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.

关键词： Machine-learning algorithm recognition system COVID-19 convolutional neural network

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DeBeRTa-GRU: Sentiment Analysis for Large Language Model

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Computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of electrical and Information engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional encoder Representations from Transformers(BeRT)attention(DeBeRTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BeRT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBeRTa GRU Naive Bayes LSTM sentiment analysis large language model

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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

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Impact of multifocal gas-permeable lens designs on short-term choroidal response, axial length, and retinal defocus profile

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International Journal of Ophthalmology(english edition) 2024年第2期17卷 247-256页

作者： Muteb Alanazi Patrick Caroline Amane Alshamrani Maria Liu Optometry Department College of Applied Medical SciencesKing Saud UniversityRiyadh 14813Saudi Arabia College of Optometry Pacific UniversityForest GroveOregon 97116-1756United States Optometry School University of California at BerkeleyCalifornia 94720United States

AIM:To investigate the impact of multifocal gas permeable contact lens(MFGPCL)in various add power and distance/near area allocation on short-term changes of choroidal thickness(ChT),axial length(AL),and retinal defocus profile in young ***:Seventeen young adults(2 males and 15 females;age 23.17±4.48y)were randomly assigned to wear two designs binocularly with a one-week washout period in *** of four MFGPCL designs were *** designs were distance-center that varied in two add power(+1.50 and 3.00 D)and/or two distance zone(DZ)diameters(1.50 and 3.00 mm;design A:DZ 1.5/add 3.0,B:DZ 1.5/add 1.5,C:DZ 3.0/add 3.0,D:DZ 3.0/add 1.5).ChT,AL,and peripheral refraction data were collected on each subject at baseline,on days 1 and 7 of MFGPCL daily *** was assessed in four quadrants using a spectraldomain optical coherence ***:AL was shortened by-26±44μm with lens C,-18±27μm with lens D,-13±29μm with lens A,and-8±30μm with lens B(all Perall increase in ChT was observed with all 4 designs(lens A:+6±6μm,B:+3±7μm,C:+8±7μm,and D:+8±7μm).Temporal and superior choroid exhibited more choroidal thickening associated with *** designs induced significant relative peripheral myopia(RPM)beyond the central 20o across the horizontal meridian in both nasal and temporal fields(Pence on ChT and AL,which are associated with significant increase in RPM after short-term *** reliability and feasibility of quantifying short-term changes in ChT support its use as a promising marker for the long-term efficacy of myopia-controlling treatments.

关键词： choroid gas-permeable contact lens retinal defocus axial length myopia

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Deep learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

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Computers, Materials & Continua 2021年第5期67卷 2141-2160页

作者： Theyazn H.H.Aldhyani Melfi Alrasheed Mosleh Hmoud Al-Adaileh Ahmed Abdullah Alqarni Mohammed Y.Alzahrani Ahmed H.Alahmadi Community College of Abqaiq King Faisal UniversityAl HofufSaudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl HofufSaudi Arabia deanship of e-learning and distance education King Faisal UniversityAl HofufSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahahSaudi Arabia Department of Computer Science and Information Taibah UniversityMadinahKingdom of Saudi Arabia

The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its ***,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of *** this study,a deep learning algorithm and a Holt-trend model are proposed to predict the *** Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death *** real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely saudi arabia,Spain and *** results suggest that the LSTM models show better performance in predicting the cases of coronavirus *** measure performance Mean squared error(MSe),Root Mean Squared error(RMSe),Mean error and correlation are employed to estimate the results of the proposed *** empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three *** far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to saudi arabia,Italy and Spain ***,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the saudi arabia,Italy and Spain *** empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these *** findings provide better insights regarding the future of Covid-19 this pandemic in *** results were obtained by applying time series models,which

关键词： Deep learning algorithm holt-trend prediction Covid-19 machine learning

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