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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen meznah a.alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Data Assimilation Enhances WRF-Chem Performance in Modeling Volcanic Ash Clouds from Hunga Tonga–Hunga Ha'apai Eruption

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Journal of Meteorological Research 2024年第6期 1122-1140页

作者： Hosni SNOUN Mohammad Mosaed ALAHMADI Amirhossein NIKFAL Abderrazak ARIF William HATHEWAY meznah a.alamro Alaeddine MIHOUB Moez KRICHEN Numthaja Co. Ltd. Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Institut National de la Météorologie Free Researcher Department of Information Technology College of Computer & Information Science Princess Nourah Bint Abdul Rahman University Department of Management Information Systems and Production Management College of Business and EconomicsQassim University Faculty of Computer Science and Information Technology (FCSIT) Al-Baha University ReDCAD Laboratory University of Sfax

Volcanic eruptions release large amounts of ash clouds and gas aerosols into the atmosphere,which can be simulated by air quality prediction ***,the performance of these models remains unsatisfactory,even though both relevant physics and chemistry are ***,exploring the approaches for improvement such as inclusion of data assimilation is *** this study,we depict the modeling of the volcanic ash dispersion from the Hunga Tonga–Hunga Ha’apai underwater volcano,which erupted in a series of large explosions in late December 2021 and early January *** 15 January 2022,a particularly significant explosion sent a massive ash cloud high into the *** used the inline Weather Research and Forecasting model coupled with chemistry(WRF-Chem) and incorporated meteorological data assimilation within the Flux Adjusting Surface Data Assimilation System (FASDAS).We compared three forecast scenarios:one with only meteorology and no chemistry(OMET),one with gas and aerosol chemistry and no assimilation (NODA),and one with both chemistry and assimilation (FASDAS).We found that FASDAS resulted in lower planetary boundary layer height (PBLH),downward surface shortwave flux,and 2-m temperature by up to 800 m,200 W m-2,and 6°C on the land portion,respectively,while the opposite was observed near the eruption *** validated the model against the observations and the results showed that FASDAS significantly enhanced the model performance in retrieving meteorological ***,the simulations also revealed significant biases in the concentration of volcanic ash around the ash *** from the Copernicus TROPOspheric Monitoring Instrument Sentinel-5 Precursor (TROPOMI-S5P) showed a westward trend of the total SO2 *** work demonstrates the significant contribution of data assimilation to the results of operational air quality predictions during violent volcanic eruption events.

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Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

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Computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid Muhammad Rizwan Abdulatif Alabdulatif Abdullah Alnajim meznah alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

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Usability and Security of Arabic Text-based CAPTCHA Using Visual Cryptography

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Computer Systems Science & Engineering 2022年第2期40卷 421-440页

作者： Suliman A.Alsuhibany meznah Alquraishi Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Recently,with the spread of online services involving websites,attack-ers have the opportunity to expose these services to malicious *** protect these services,A Completely Automated Public Turing Test to Tell Computers and Humans Apart(CAPTCHA)is a proposed *** many Arabic countries have developed their online services in Arabic,Arabic text-based CAPTCHA has been introduced to improve the usability for their ***-over,there exist a visual cryptography(VC)technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each ***,as yet,the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried ***,this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC *** evaluate this scheme,experi-mental studies are conducted,and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.

关键词： Visual cryptography Arabic text-based CAPTCHA usability security printed and handwritten Arabic script

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Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection

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Intelligent Automation & Soft Computing 2023年第9期37卷 2687-2706页

作者： Hachemi Bennaceur meznah Almutairy Norah Alhussain Computer Science Department Imam Mohammad bin Saud Islamic UniversityRiyadh13318Saudi Arabia

The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational *** literature includes much research on feature selection for supervised ***,feature selection for unsupervised learning has only recently been *** the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are *** work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means *** the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation *** effectiveness of this algorithm is demonstrated on various ***-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error ***,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection *** results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to *** particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all *** compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected feature

关键词： Genetic algorithm unsupervised feature selection k-Means clustering

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The Availability Degree of Excellence Management Standards and Their Impact on the Performance Results in Kuwait University

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Psychology Research 2017年第7期7卷 397-407页

作者： meznah S. K. H. Alazmi Reem R. Almutairi Kuwait University Kuwait Kuwait

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