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Deployment of Pre-industrial Autonomous Microbe Sensor in Saudi Arabia’s injection Seawater System

Journal of Sensor Technology 2018年第1期8卷 1-17页

作者： Mohammed A.al-Moniee Xiangyang Zhu Rikke Markfoged Aabdullah H.al-Wadei Poul L.Pedersen Anders K.Tuxen fuad i.al-nuwaiser Lone Tang Tinna Staghoj Roesen Thomas Lundgaard Saudi Arabian Oil Company(Saudi Aramco) DhahranSaudi Arabia Danish Technologi.al institute(DTi) AarhusDenmark

Microbial growth in water injection systems can lead to many problems, including biofouling, water quality deterioration, injectivity loss, microbial corrosion, and reservoir formation damage. Monitoring of microbial activities is required in any mitigation strategy, enabling operators to apply and adjust countermeasures properly and in due time. in this study, the pre-industrial autonomous microbe sensor (AMS) was constructed with techni.al improvements from the prototype for increased sensitivity, durability, robustness, and maintainability. The pre-industrial AMS was lab validated, field proven, and deployed at criti.al locations of seawater injection network for automated detection of microorganisms under the Saudi Arabia’s harsh environment. An excellent correlation between AMS measurement data (fluorescence count) and actual count of microbial cell number under microscope was established (coefficient of determination, R2 > 0.99) for converting AMS fluorescence count to cell numbers (cell mL-1) in the injection seawater. The pre-industrial AMS only required monthly maintenance with solutions refill, and was able to cope with hot summer months even without protection in an air-conditioned shelter. The study team recommended wider deployment of the online AMS for real-time monitoring of bacteria numbers in the various strategic locations in Saudi Aramco’s complex seawater injection network, as an integral component of pipeline corrosion and leak mitigation program.

关键词： Automated Monitoring Autonomous Microbe Sensor Microbial Sensor DNA Staining injection Seawater

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Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

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Computers, Materials & Continua 2024年第5期79卷 2673-2696页

作者： Hina Naz Zuping Zhang Mohammed al-Habib fuad A.Awwad Emad A.A.ismail Zaid ali Khan School of Computer Science and Engineering Central South UniversityChangsha410003China Department of Quantitative analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia Electri.al and Computer Engineering University of VictoriaVictoriaV9A1B8Canada

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.in the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.also,since transformer performance typi.ally relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.in response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and origi.al datasets.The training accuracy of our model reached

关键词： image analysis posture of heart attack(PHA)detection hybrid features VGG-16 ResNet-50 vision transformer advance multi-head attention layer

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identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-Seq approach

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Ani.al Models and Experimental Medicine 2024年第1期7卷 36-47页

作者： Roa'a H.S.Yosief iqbal M.Lone Aharon Nachshon Heinz Himmelbauer irit Gat-Viks fuad A.iraqi Department of Clini.al Microbiology and immunology Sackler Faculty of MedicineTel-Aviv UniversityTel-AvivIsrael School of Molecular Cell Biology and Biotechnology George S.Wise Faculty of Life SciencesTel Aviv UniversityTel AvivIsrael institute of Computational Biology Department of BiotechnologyUniversity of Natural Resources and Life SciencesMuthgasse 181190 ViennaAustria

Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus(Af)using an RNAseq approach in CC lines and hepatic gene expression.Methods:We studied 31 male mice from 25 CC lines at 8 weeks old;the mice were infected with Af.Liver tissues were extracted from these mice 5 days post-infection,and next-generation RNA-sequencing(RNAseq)was performed.The GENE-E analysis platform was used to generate a clustered heat map matrix.Results:Significant variation in body weight changes between CC lines was ob-served.Hepatic gene expression revealed 12 top prioritized candidate genes differ-entially expressed in resistant versus susceptible mice based on body weight changes.interestingly,three candidate genes are located within genomic intervals of the previ-ously mapped quantitative trait loci(QTL),including Gm16270 and Stox1 on chromo-some 10 and Gm11033 on chromosome 8.Conclusions:Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af.As a next step,eQTL analysis will be performed for our RNA-Seq data.Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.

关键词： aspergillus fumigatus infection collaborative cross(CC)mice gene expression profile gene-network host susceptibility quantitative trait loci(QTL)mapping RNA-Seq

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Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An innovative Multilingual Approach for Social Media Reviews

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Computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood alam fuad AAwwad Emad A.A.ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digi.al age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. in the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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Arrhythmia Prediction on Opti.al Features Obtained from the ECG as images

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Computer Systems Science & Engineering 2023年第1期44卷 129-142页

作者： fuad A.M.al-Yarimi Department of Computer Science King Khalid UniversityAbhaKingdom of Saudi Arabia

A criti.al component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electri.al activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.

关键词： ECG records electrocardiogram morphologi.al features(MF) empiri.al mode decomposition algorithm HOS

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Comparative Evaluation of Data Mining algorithms in Breast Cancer

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Computers, Materials & Continua 2023年第10期77卷 633-645页

作者： fuad A.M.al-Yarimi Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer.The only method to avoid breast cancer-related deaths is through early detection and treatment.The proper classification of malignancies is one of the most significant challenges in the medi.al industry.Due to their high precision and accuracy,machine learning techniques are extensively employed for identifying and classifying various forms of cancer.Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on.This article introduces several techniques,including support vector machine(SVM),K star(K∗)classifier,Additive Regression(AR),Back Propagation Neural Network(BP),and Bagging.These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients.Comparing the results,the author found that Support Vector Machine and Bagging had the highest precision and accuracy,respectively.also,assess the number of studies that provide machine learning techniques for breast cancer detection.

关键词： Many-core multi-core N-conjugate shuffle multi-port content addressable memory interconnection network

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Opti.al Load Balancing in Cloud Environment of Virtual Machines

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Computer Systems Science & Engineering 2022年第6期41卷 919-932页

作者： fuad A.M.al-Yarimi Sami.althahabi Majdy Mohammed Eltayeb Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of information System King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia

Cloud resource scheduling is gaining prominence with the increasingtrends of reliance on cloud infrastructure solutions. Numerous sets of cloudresource scheduling models were evident in the literature. Cloud resource scheduling refers to the distinct set of algorithms or programs the service providersengage to maintain the service level allocation for various resources over a virtualenvironment. The model proposed in this manuscript schedules resources of virtual machines under potential volatility aspects, which can be applied for anypriority metric chosen by the server administrators. also, the model can be flexible for any time frame-based analysis of the load factor. The model discussed inthis manuscript relies on the Bollinger Bands tool for understanding the potentialvolatility aspects of a Virtual Machine. The experimental study of the model compared to the contemporary load balancing model called STLB (Starvation Threshold-based Load Balancing) refers to a simple and potential model that can bemore pragmatic for sustainable ways of load balancing.

关键词： Cloud computing service level agreements virtual machines load balancing scheduling

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A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

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Computers, Materials & Continua 2022年第3期70卷 4829-4845页

作者： Mohammad Hadwan Basheer M.al-Maqaleh fuad N.al-Badani Rehan Ullah Khan Mohammed A.al-Hagery Department of information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen intelligent Analytics Group(iAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Faculty of Computer Sciences and information Systems Thamar UniversityThamarYemen Faculty of Administration Science Ibb UniversityIbbYemen Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medi.al domains,exchange rates and consumer price index.The main problem is insufficient forecasting accuracy.The present study proposes a hybrid forecastingmethods to address this need.The proposed method includes three models.The first model is based on the autoregressive integrated moving average(ARiMA)statisti.al model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARiMA and BPNN(ARiMA/BPNN)and artificial neural networks and ARiMA(ARiMA/ANN)to gain the benefits of linear and nonlinearmodeling.The forecasting models proposed in this study are used to predict the indices of the consumer price index(CPi),and predict the expected number of cancer patients in the ibb Province in Yemen.Statisti.al standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage error.Based on the computational results,the improvement rate of forecasting the CPi dataset was 5%,71%,and 4%for ARiMA/BPNN model,ARiMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARiMA/BPNNmodel,ARiMA/ANN model,and BPNNmodel respectively.Therefore,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data non-linearity.The ARiMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

关键词： Hybrid model forecasting non-linear data time series models cancer patients neural networks box-jenkins consumer price index

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Evolutionary algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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Computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm Shaha al-Otaibi Amel Aalhussan Mesfer al Duhayyim fuad ali Mohammed al-Yarimi Mohammed Rizwanullah ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. in addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the opti.al parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

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Mode-locked ytterbium-doped fiber laser with zinc phthalocyanine thin film saturable absorber

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Frontiers of Optoelectronics 2022年第2期15卷 181-190页

作者： Rawan S.M.Soboh Ahmed H.H.al-Masoodi fuad N.A.Erman Abtisam H.H.al-Masoodi Bi.al Nizamani Hamzah Arof Retna Apsari Sulaiman Wadi Harun Department of Electri.al Engineering Faculty of EngineeringUniversity of Malaya50630Kuala LumpurMalaysia Electronic and Telecommunication Engineering Department College of EngineeringThe American University of KurdistanDuhok42001Iraq Department of Physics Faculty of ScienceUniversity of Malaya50603Kuala LumpurMalaysia Department of Physics Faculty of Science and TechnologyAirlangga University60115SurabayaIndonesia

A stable mode-locked laser was demonstrated using a newly developed zinc phthalocyanine(ZnPc)thin film as passive saturable absorber(SA)in ytterbium-doped fiber laser(YDFL).The ZnPc thin film was obtained using a casting method and then inserted between the two fiber ferrules of a YDFL ring cavity to generate mode-locked pulses.The resulting pulsed laser operated at a wavelength of 1034.5 nm having a repetition rate of 3.3 MHz.At pump power of 277 mW,the maximum output power and pulse energy are achieved at 4.92 mW and 1.36 nJ,respectively.ZnPc has a high chemi.al and photochemi.al stability,and its significance for use as a potential SA in a mode-locked laser is reported in this work.

关键词： Mode-locking Ytterbium-doped fiber laser(YDFL) Saturable absorber(SA) Zinc phthalocyanine(ZnPc)thin film

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