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Machine Learning and Data Fusion Approach for Elastic rock Properties Estimation and Fracturability Evaluation

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Energy and AI 2024年第2期16卷 1-13页

作者： Yiwen Gong Ilham El-Monier mohamed Mehana The Ohio State University SimTech LLC Los Alamos National Laboratory

Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock *** study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated *** used the microstructural insights extracted from rock images for geomechanical facies ***,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s *** developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and *** findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical *** Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability ***,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth *** conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.

关键词： Young’s modulus prediction fracability evaluation landing point selection rock mechanics machine learning

Contemporary construction in historical sites:The missing factors

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Frontiers of Architectural research 2024年第3期13卷 487-504页

作者： Sina Kamali Tabrizi mohamed Gamal Abdelmonem School of Architecture Design and the Built EnvironmentNottingham Trent UniversityNottinghamUK Department of Architecture and the Built Environment University of YorkYorkUK

Historical sites(HSs)are akin to living entities,and their existence is perpetuated through the erection of new buildings or *** HSs need sustainable development and new construction,as existing monuments fail to meet contemporary user *** the urgent demand,many new buildings within HSs have been constructed without considering the influencing factors on contextual value,built,and natural *** oversight has led to irreversible damage to their historical *** studies have independently explored interventions,construction,and development within ***,there is a conspicuous absence of comprehensive research that concurrently addresses these issues while preserving the intrinsic value of the *** study aims to identify the variables and factors contributing to the successful design and construction of new buildings within HSs,satisfying user needs,conserving contextual values,and achieving sustainability with the natural and built *** research employs a mixed-method approach,gathering raw data through literature reviews,case studies,and expert *** study population comprised 97 experts in architectural heritage conservation and sustainable *** factor analysis was utilized for data analysis,leading to the identification of influential *** study highlights the significance of the location,function,scale,form,material,skyline,viewpoint,accessibility,reversibility,topography,sustainability,technology,structural system,lifespan,and interior design in constructing new buildings within a ***,the design of new additions should adhere to five guiding principles:integrated design,volume design,green design,compatible design,and modern *** this context,the new structures will fulfill user needs,preserve or enhance contextual values,and harmonize with the built and natural environment.

关键词： New building Contemporary construction Historical sites Sustainable development Architectural heritage

Machine Learning Enabled Novel real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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Computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(rF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(rFE)and randomforest feature importance(rFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

Enhancing renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(rESs).Efficient integration of stochastic rESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of rESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

Model Predictive Current Control for Low-Cost Shunt Active Power Filter

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1589-1598页

作者： Ali A.Abdel-Aziz mohamed A.Elgenedy Barry W.Williams the Electronic and Electrical Engineering Department Strathclyde UniversityGlasgowG11XQUK School of Computing Engineering and the Built Environment(SCEBE)Glasgow Caledonian UniversityGlasgowG40BAUK

Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference ***,designing an accurate current controller is crucial to guarantee satisfactory SAPF *** paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost *** addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current *** proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.

关键词： Four-switch converter harmonic distortion model predictive current control power quality shunt active power filter

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 977-997页

作者： Mustufa Haider Abidi Hisham Alkhalefah mohamed K.Aboudaif Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box-800Riyadh11421Saudi Arabia

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for ***,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient *** methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful ***,this paper proposes a crossover-based Multilayer Perceptron(CMLP)*** collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of *** an attack is detected,healthcare professionals are promptly alerted to prevent data *** paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are *** results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as recall,Precision,Accuracy,and F1-score to predict patient *** the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and ***,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

关键词： Smart healthcare systems multilayer perceptron cybersecurity adversarial attack detection Healthcare 4.0

Application of supervised machine learning to predict the enhanced gas recovery by CO_(2) injection in shale gas reservoirs

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Petroleum 2024年第1期10卷 124-134页

作者： Moataz Mansi mohamed Almobarak Jamiu Ekundayo Christopher Lagat Quan Xie Discipline of Petroleum Engineering WA School of Mines:MineralsEnergy and Chemical EngineeringCurtin UniversityWestern AustraliaAustralia

The technique of Enhanced Gas recovery by CO_(2) injection(CO_(2)-EGr)into shale reservoirs has brought increasing attention in the recent ***_(2)-EGr is a complex geophysical process that is controlled by several parameters of shale properties and engineering ***,more challenges arise when simulating and predicting CO_(2)/CH4 displacement within the complex pore systems of ***,the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO_(2) injection to shale *** recent years,machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical ***,this work proposes a solution by developing a supervised machine learning(ML)based model to preliminary evaluate CO_(2)-EGr *** used for this work was drawn across a wide range of simulation sensitivity studies and experimental *** this work,linear regression and artificial neural networks(ANNs)implementations were considered for predicting the incremental enhanced *** on the model performance in training and validation sets,our accuracy comparison showed that(ANNs)algorithms gave 15%higher accuracy in predicting the enhanced CH4 compared to the linear regression *** ensure the model is more generalizable,the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs *** ANNs models presented,ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination(r2)of 0.78 compared to the linear regression model with r2 of *** developed MLbased model presents a powerful,reliable and cost-effective tool which can accurately predict the incremental enhanced CH4 by CO_(2) injection in shale gas reservoirs.

关键词： Artificial intelligence Supervised Machine Learning Shale Gas Enhanced Shale Gas recovery CO_(2)Injection CO_(2)sequestration

robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

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Computers, Materials & Continua 2024年第4期79卷 1419-1439页

作者： Khaled Soliman mohamed Sobh Ayman M.Bahaa-Eldin Department of Computer and Systems Engineering Ain Shams UniversityCairo11517Egypt Department of Computer Engineering Technology ElSewedy University of TechnologyCairo44629Egypt

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a robust Malicious ExecutableDetection (rMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of rMED *** most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that rMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

关键词： Portable executable malware intrusion detection cybersecurity zero-day threats Host IntrusionDetection System(HIDS) machine learning Anomaly-based Intrusion Detection System(AIDS) deep learning

Thermal noise-driven resonant sensors

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Microsystems & Nanoengineering 2024年第3期10卷 259-271页

作者： Yan Qiao Alaaeldin Elhady mohamed Arabi Eihab Abdel-rahman Wenming Zhang School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghaiChina Department of Systems Design Engineering University of WaterlooWaterlooONCanada

MEMS/NEMS resonant sensors hold promise for minute mass and force ***,one major challenge is that conventional externally driven sensors inevitably encounter undesired intrinsic noise,which imposes a fundamental limitation upon their signal-to-noise ratio(SNr)and,consequently,the ***,this restriction becomes increasingly pronounced as sensors shrink to the *** this work,we propose a counterintuitive paradigm shift that turns intrinsic thermal noise from an impediment to a constituent of the sensor by harvesting it as the driving force,obviating the need for external actuation and realizing‘noise-driven’*** sensors employ the dynamically amplified response to thermal noise at resonances for stimulus *** demonstrate that lightly damped and highly compliant nano-structures with high aspect ratios are promising candidates for this class of *** overcome the phase incoherence of the drive force,three noise-enabled quantitative sensing mechanisms are *** validated our sensor paradigm by experimental demonstrating noise-driven pressure and temperature ***-driven sensors offer a new opportunity for delivering practical NEMS sensors that can function at room temperature and under ambient pressure,and a development that suggests a path to cheaper,simpler,and low-power-consumption sensors.

关键词： noise resonant overcome