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Deep Learning and machine Learning for Early Detection of Stroke and Haemorrhage

Computers, materials & Continua 2022年第7期72卷 775-796页

作者： Zeyad Ghaleb Al-mekhlafi Ebrahim mohammed Senan Taha H.Rassem Badiea Abdulkarem mohammed Nasrin m.makbol Adwan Alownie Alanazi Tariq S.Almurayziq Fuad A.Ghaleb Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Department of Computer Science&Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad431004India Faculty of Science and Technology Bournemouth UniversityPooleBH125BBUK Department of Computer Engineering College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia College of Computer Science and Engineering Hodeidah UniversityHodeidah966Yemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart *** this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on themagnetic Resonance Imaging(mRI)dataset for cerebral *** the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding *** t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data ***,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant *** features are fed into the various classification algorithms,namely,Support Vector machine(SVm),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and multilayer *** algorithms achieved superior *** Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,*** the second dataset,the mRI image dataset was evaluated by using the AlexNet model and AlexNet+SVm hybrid *** hybrid model AlexNet+SVm performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively.

关键词： Stroke cerebral haemorrhage deep learning machine learning t-SNE and RFE algorithms

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Linear Active Disturbance Rejection Control with a Fractional-Order Integral Action

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Computers, materials & Continua 2022年第11期73卷 3057-3079页

作者： maâmar Bettayeb Rachid mansouri Ubaid m.Al-Saggaf Abdulrahman U.Alsaggaf mohammed moinuddin Electrical Engineering Department University of SharjahUnited Arab Emirates L2CSP Laboratory University Mouloud Mammeri of Tizi-OuzouAlgeria Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddahSaudi Arabia Electricaland Computer Engineering Department King Abdulaziz UniversityJeddahSaudi Arabia

Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and *** extended state observer(ESO)based controller is the key to the LADRC *** this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard *** the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC *** simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved *** illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried ***,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop ***,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.

关键词： Fractional calculus active disturbance rejection control pole placement cart-pendulum system robust control

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Disaster Collaborative Exercises for Healthcare Teamwork in a Saudi Context

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International Journal of Disaster Risk Science 2023年第2期14卷 183-193页

作者： mohammed Ali Salem Sultan Amir Khorram-manesh Jarle Lφwe Sφrensen Johan Berlin Eric Carlstrōm model of Care Healthcare TransformationRegional Health DirectorateNajran66255Saudi Arabia Institute of Health and Care Sciences Sahlgrenska AcademyGothenburg University40530GothenburgSweden Institute of Clinical Sciences Sahlgrenska AcademyGothenburg University40530GothenburgSweden Gothenburg Emergency medicine Research Group(GEmREG) Sahlgrenska Academy41345GothenburgSweden USN School of Business University of South-Eastern Norway3199BorreNorway Department of Social and Behavioural Studies University West46186TrolhättanSweden

This study aimed to evaluate the development of healthcare teamwork during and after the collaboration tabletop exercises,through observation and interview *** and maturity theoretical models were employed to explain the collaborative challenges in teams that may suffer from unequally distributed power,hierarchies,and *** three-level collaboration tabletop exercises and the Command and control,Safety,Communication,Assessment,Treatment,Triage,Transport(CSCATTT)instrument,100 healthcare workers were observed during each step in the implementation of the CSCATTT instrument using two simulated *** results show a lack of integration and team maturity among participants in the first scenario,leading to the delayed start of the activity,task distribution,and decision *** shortcomings were improved in the second ***-depth interviews with 20 participants in the second phase of the study revealed improved knowledge and practical skills,self-confidence,and ability in team building within trans-professional groups in the second scenario,which in concordance with the integration theory,was due to the attempts made in the first ***,there was an improvement in the team’s maturity,which in concordance with the maturity theory,was due to the knowledge and practical skills during scenario *** results indicate the importance of continuous tabletop training,and the use of CSCATTT as a collaborative instrument,to promote the development of collaboration and to test the concept of preparedness.

关键词： Collaboration exercises Disaster education Emergency management Healthcare personnel training Saudi Arabia Teamwork

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Efficient Data Compression of ECG Signal Based on modified Discrete Cosine Transform

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Computers, materials & Continua 2022年第6期71卷 4391-4408页

作者： Ashraf mohamed Ali Hassan mohammed S.Alzaidi Sherif S.m.Ghoneim Waleed El Nahal Electronics and Communications Engineering Department Faculty of EngineeringSinai UniversityArishCO 45511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Faculty of Technology and Education Suez UniversitySuez43527Egypt Electronics and Communications Engineering Department Faculty of EngineeringMSA UniversityCO 12585Egypt

This paper introduced an efficient compression technique that uses the compressive sensing(CS)method to obtain and recover sparse electrocardiography(ECG)*** recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency.A novel analysis was proposed in this *** apply CS on ECG signal,the first step is to generate a sparse signal,which can be obtained using modified Discrete Cosine Transform(mDCT)on the given *** transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper.A small number of wavelet components can describe the ECG signal as related work to obtain a sparse *** technique for ECGsignal compression,which is a novel area of research,is *** signals are introduced randomly between any successive beats of the ***-BIH database can be represented as the experimental database in this domain of ***-BIH database consists of various ECG signals involving a patient and standard ECG *** can be considered as the simulation tool used in this *** proposed method’s uniqueness was inspired by the compression ratio(CR)and achieved by *** performance measurement of the recovered signal was done by calculating the percentage root mean difference(PRD),mean square error(mSE),and peak signal to noise ratio(PSNR)besides the calculation of ***,the simulation results indicated that this work is one of the most important works in ECG signal compression.

关键词： Compressive sensing sparse beats of hearts compression ratio percentage root mean difference

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Foliar Application of Cytokinin modulates Gas Exchange Features,Water Relation and Biochemical Responses to Improve Growth Performance of maize under Drought Stress

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Phyton-International Journal of Experimental Botany 2022年第3期91卷 633-649页

作者： m.Rafiqul Islam m.Shahinur Islam Nurunnaher Akter mohammed mohi-Ud-Din mohammad Golam mostofa Department of Agronomy Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Department of Crop Botany Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Department of Biochemistry and molecular Biology Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Institute of Genomics for Crop Abiotic Stress Tolerance Department of Plant and Soil ScienceTexas Tech UniversityLubbockTX 79409USA

Improvement of plant performance under drought stress is crucial to sustaining agricultural *** current study investigated the ameliorative effects of foliar-applied kinetin,an adenine-type cytokinin(CK),on growth and gas exchange parameters,water relations and biochemical attributes of maize plants under drought ***-day-old maize plants were subjected to drought by maintaining soil moisture content at 25%field capacity for 8 days followed by foliar application of kinetin at 0,75,150 and 225 mg L^(−1)(CK0,CK75,CK150 and CK225,respectively)to the plants for two-times at the 9-day *** revealed that drought stress markedly reduced stem diameter,dry weight,chlorophyll content,gas exchange parameters and water balance but increased proline,malondialdehyde and soluble sugar contents,electrolyte leakage and senescence in maize *** of exogenous CK remarkably improved maize performance by modulating growth,gas exchange-and water relation-related parameters in a dose-dependent manner under drought ***225 increased chlorophyll content(by 61.54%),relative water content(by 49.14%),net photosynthesis rate(by 39.94%)and transpiration rate(by 121.36%)and also delayed leaf senescence but decreased internal CO_(2)concentration(by 7.38%),water saturation deficit(by 40.40%)and water uptake capacity(by 42.49%)in both well-watered and droughtstressed ***,CK application considerably decreased electrolyte leakage,proline,malondialdehyde and soluble sugar levels in drought-stressed maize plants,as also supported by heatmap and cluster *** together,exogenous CK at proper concentration(225 mg L^(−1))successfully improved maize performance under drought conditions,thereby suggesting CK application as a useful approach to alleviate drought-induced adverse effects in maize plants,and perhaps in other important crop plants.

关键词： Cytokinin drought maize osmoprotection photosynthesis water-use-efficiency

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay m.Salih md Arafatur Rahman A.Taufiq Asyhari muhammad Kamran Naeem mohammad Patwary Ryan Alturki mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in mobile Cognitive Radio Networks(mCRNs).moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design mobile cognitive radio networks Routing protocol IIoT applications

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Classification of Electroencephalogram Signals Using LSTm and SVm Based on Fast Walsh-Hadamard Transform

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Computers, materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed mohsen Sherif S.m.Ghoneim mohammed S.Alzaidi Abdullah Alzahrani Ashraf mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTm)and support vector machine(SVm)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTm classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTm are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTm are 99.00%.The results of the SVm classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTm and SVm is 2000 and 2500 s,*** results show that the LSTm classifier provides better performance than SVm in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTm SVm fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Forecasting mental Stress Using machine Learning Algorithms

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Computers, materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi mohammed Alshehri m.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and model Deployment Architecture(mDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent model Retraining(FmR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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Finite Element Analysis of Attraction-Repulsion Chemotaxis *** II:Time Convergence,Error Analysis and Numerical Results

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Communications on Applied mathematics and Computation 2022年第3期4卷 1057-1104页

作者： mohammed Homod Hashim Akil J.Harfash Department of mathematics College of SciencesUniversity of BasrahBasrahIraq

In Hashim and Harfash(***.2021),using a finite element method,the attraction-repulsion chemotaxis model(P)in space is discretised;finite differences were used to do the same in ***,the existence of a global weak solution to the system(PΔt m)was demonstrated by means of analysis of the convergence of the fully discrete approximate problem(P h,Δt m,).moreover,the functions{U,Z,V}were proved to represent a global weak solution to the system(PΔt m)by means of a passage to the limit,h→0 of the approximate *** paper’s purpose is to demonstrate that the solutions can be bounded,independent of *** analysis contained in this paper illustrates the idea of the existence of weak solutions to the model(P),that requires passing to the limits,Δt→0+and m→∞.The time stepΔt is subsequently linked to the cutoff parameter m>1 by positing a demand thatΔt=o(m−1),as m→∞,with the result that the cutoff parameter becomes the only parameter in the problem(PΔt m).The solutions can be bounded,independ-ent of m,with the use of special energy estimates,as demonstrated ***,these m-independent bounds on the relative entropy are employed with the purpose of deriving m-independent bounds on the ***,compactness arguments were utilised to explore the convergence of the finite element approximate *** conclu-sion was that a weak solution for(P)***,we introduced the error estimate and the implicit scheme was used to perform simulations in one and two space dimensions.

关键词： Finite element Chemotaxis Convergence Weak solution

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HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

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Computers, materials & Continua 2022年第11期73卷 2293-2312页

作者： magdy m.Fadel Sally m.El-Ghamrawy Amr m.T.Ali-Eldin mohammed K.Hassan Ali I.El-Desoky Computer Engineering and Systems Department Faculty of EngineeringMansoura UniversityMansoura35516DKEgypt Head of Communications and Computer Engineering Department MISR Higher Institute for Engineering and TechnologyMansoura35111DKEgypt mechatronics Department Faculty of EngineeringHours University in Egypt(HUE)New Damietta34517DTEgypt

Distributed denial-of-service(DDoS)attacks are designed to interrupt network services such as email servers and webpages in traditional computer ***,the enormous number of connected devices makes it difficult to operate such a network *** defined networks(SDN)are networks that are managed through a centralized control system,according to *** controller is the brain of any SDN,composing the forwarding table of all data plane network *** the advantages of SDN controllers,DDoS attacks are easier to perpetrate than on traditional *** the controller is a single point of failure,if it fails,the entire network will *** paper offers a Hybrid Deep Learning Intrusion Detection and Prevention(HDLIDP)framework,which blends signature-based and deep learning neural networks to detect and prevent *** framework improves detection accuracy while addressing all of the aforementioned *** validate the framework,experiments are done on both traditional and SDN datasets;the findings demonstrate a significant improvement in classification accuracy.

关键词： Software defined networks(SDN) distributed denial of service attack(DDoS) signature-based detection whale optimization algorism(WOA) deep learning neural network classifier

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