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Optimal Machine Learning Driven sentiment Analysis on COVID-19 Twitter Data

Computers, Materials & Continua 2023年第4期75卷 81-97页

作者： Bahjat Fakieh abdullah s.al-malaise al-ghamdi Farrukh saleem Mahmoud Ragab Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the *** COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased *** studies used sentiment Analysis(sA)to analyze the emotions expressed through tweets upon ***,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven sA(ABCMLsA)model is developed for conducting sentiment Analysis of COVID-19 Twitter *** prime focus of the presented ABCML-sA model is to recognize the sentiments expressed in tweets made *** involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature *** identification and classification of the sentiments,the support Vector Machine(sVM)model is *** last,the ABC algorithm is applied to fine tune the parameters involved in *** demonstrate the improved performance of the proposed ABCML-sA model,a sequence of simulations was *** comparative assessment results confirmed the effectual performance of the proposed ABCML-sA model over other approaches.

关键词： sentiment analysis twitter data data mining COVID-19 machine learning artificial bee colony

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Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

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Computers, Materials & Continua 2022年第8期72卷 2895-2907页

作者： abdullah s.al-malaise al-ghamdi Mahmoud Ragab Maha Farouk s.sabir Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of *** accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish *** incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game ***,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly ***,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in *** study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI *** goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education *** AICID-HEI technique encompassesmin-max normalization approach to preprocess the ***,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-Fs)technique is applied to choose the feature ***,the bidirectional long short-term memory(BiLsTM)model is utilized for the detection and classification of intrusions in the ***,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational *** order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark *** experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

关键词： Higher education institutions deep learning machine learning cybersecurity intrusion detection

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim aljabhan Mahmoud Ragab sultanah M.alshammari abdullah s.al-malaise al-ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia s 7Information systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITs).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLsA-TFP)*** presentedOADLsA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLsA-TFP model employs attention-based bidirectional long short-term memory(ABLsTM)model for predicting traffic *** order to enhance the performance of the ABLsTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFsA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLsA-TFP model over the recent approaches mean square error(MsE),root mean square error(RMsE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Intelligent Forensic Investigation Using Optimal stacked Autoencoder for Critical Industrial Infrastructures

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Computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： abdullah s.al-malaise al-ghamdi Mahmoud Ragab F.J.alsolami Hani Choudhry Ibrahim Rizqallah alzahrani Information systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control systems(ICs)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OsAE)model,named MFROFs-OsAE *** primary objective of the MFROFs-OsAE technique is to determine the presence of abnormal events in critical industrial ***-OsAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OsAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFs-OsAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFs-OsAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection system

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Computer systems science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab sultanah M.alshammari abdullah s.al-malaise al-ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDs)is broadly utilized for the enhancement of security posture in an IT *** IDs is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(ML)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDs mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDs self-protection unrealistic and *** study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for *** proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented MM-WMVEDL technique implements min-max normalization to s.ale the input *** feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)*** the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLsTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDs-2017 dataset to exhibit the promising performance of the MM-WMVEDL *** comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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Predicting Energy Consumption Using stacked LsTM snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd alghamdi abdullah s.A.L-malaise al-ghamdi Mahmoud Ragab Information systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors.allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long short-Term Memory(LsTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean square Error(RMsE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LsTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

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Provenance and depositional setting of the Late MiocenePleistocene clastic sediments in the eastern Arabian Peninsula and western Iraq using rare earth elements geochemistry

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Acta Geochimica 2024年第2期43卷 399-410页

作者： salih M.Awadh Zinah s.al-Ankaz Mohanad R.A.al-Owaidi John s.Armstrong-altrin Department of Geology College of ScienceUniversity of BaghdadBaghdadIraq Department of Geology College of ScienceUniversity of WasitWasit 52011Iraq Department of Applied Geology College of ScienceUniversity of BabylonBabylon 51001Iraq Unidad de Procesos Oceanicos y Costeros Instituto de Ciencias del Mar y LimnologíaUniversidad Nacional Autonoma de MexicoCiudad Universitaria04510 Mexico CityCDMXMexico Department of Marine sciences Bharathidasan UniversityTiruchirapalliTamil Nadu 620024India

The sandstones of the Late miocene–Pliocene Dibdibba Formation in the Najaf–Karbala Plateau and Basra were examined to determine their source rocks and origin. The rare earth elements(REE) and trace elements(sc, Co, V, and Th) concentrations in these sandstones revealed that they likely derived from a single source. The steep light rare earth elements(LREE) and flat, heavy rare earth element(HREE) patterns, negative Eu anomaly, and high ΣREE contents in sandstones suggest its derivation from a suggests that a passive continental margin environment and originated from felsic source rocks. The average concentration of ΣREE is 93.5 ppm, which is lower than that of the average crus.al compositions like Upper Continental Crust and Post Archean Australian s.ale. The higher proportion of LREE compared to HREE implies that these sandstones were recycled and derived from a dis.al source. The Th/Co, Th/sc, La/sc, La/Co, Eu/Eu*and(La/Lu)cn elemental ratios indicated that these Late Miocene–Pliocene sandstones were derived from felsic rocks located in the marginal region of the Arabian shield.

关键词： REE Dibdibba formation Clastic sediments Passive continental margin

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Developing a Model for Parkinson’s Disease Detection Using Machine Learning algorithms

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Computers, Materials & Continua 2024年第6期79卷 4945-4962页

作者： Naif al Mudawi Department of Computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Parkinson’s disease(PD)is a chronic neurological condition that progresses over *** start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or *** patient’s symptoms become more severe due to the worsening of their signs over *** this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk *** research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of *** aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD *** research methodology involves developing a model using a machine learning *** the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),support Vector Machine(sVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridsearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning *** 98%accuracy,LightGBM had the highest accuracy in this ***,KNN,and sVM came in second with 96%***,the performance scores of NB and LR were recorded to be 76%and 83%,*** is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory ***,we extracted meaningful insights and figured out potential gaps on top of *** extracting meaningful in

关键词： Light GBM cross-validation ROC-AUC Parkinson’s disease(PD) sVM and XGBoost

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Interplay of serum biomarkers bilirubin andγ-glutamyltranspeptidase in predicting cardiovascular complications in type-2 diabetes mellitus

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World Journal of Diabetes 2024年第6期15卷 1074-1078页

作者： Ebtesam abdullah al-suhaimi abdullah Ahmed al-Rubaish scientific Research and Innovation Imam Abdulrahman Bin Faisal UniversityDammam 31441Saudi Arabia King Abdulaziz and his Companions Foundation for Giftedness and Creativity“Mawhiba” Riyadh 11372Saudi Arabia College of Medicine Imam Abdulrahman Bin Faisal UniversityDammam 31441Saudi Arabia

This editorial synthesizes insights from a series of studies examining the interplay between metabolic and oxidative stress biomarkers in cardiovascular disease(CVD),focusing particularly on type-2 diabetes mellitus(T2DM)and acute coronary syndrome(ACs).The central piece of this synthesis is a study that investigates the balance between oxidative stress and antioxidant systems in the body through the analysis of serum bilirubin andγ-glutamyltranspeptidase(γ-GGT)levels in T2DM patients with *** study highlights serum bilirubin as a protective antioxidant factor,while elevatedγ-GGT levels indicate increased oxidative stress and correlate with major adverse cardiovascular *** to this,other research contributions revealγ-GGT’s role as a risk factor in ACs,its association with cardiovascular mortality in broader populations,and its link to metabolic syndrome,further elucidating the metabolic dysregulation in *** collective findings from these studies underscore the critical roles ofγ-GGT and serum bilirubin in cardiovascular health,especially in the context of T2DM and *** providing a balanced view of the body’s oxidative and antioxidative mechanisms,these insights suggest potential pathways for targeted interventions and improved prognostic assessments in patients with T2DM and *** synthesis not only corroborates the pivotal role ofγ-GGT in cardiovascular pathology but also introduces the protective potential of antioxidants like bilirubin,illuminating the complex interplay between T2DM and heart *** studies collectively underscore the critical roles of serum bilirubin andγ-GGT as biomarkers in cardiovascular health,particularly in T2DM and ACs contexts,offering insights into the body’s oxidative and antioxidative *** synthesis of research supports the potential of these biomarkers in guiding therapeutic strategies and improving prognostic assessments for patients with T2DM and some CVD.

关键词： Type-2 diabetes mellitus Acute coronary syndrome serum biomarkers γ-glutamyltranspeptidase Bilirubin Cardiovascular disease

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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Computer Modeling in Engineering & sciences 2024年第3期138卷 2103-2127页

作者： Othman s.al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail sarmad Nozad Mahmood Mohammed Mudhafar shakir Hussein alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITs) traffic control vehicular communication kalman filtering smart city Internet of Things

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