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Predicting Energy Consumption Using stacked LsTM snapshot Ensemble

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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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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static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

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Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah abdullah al-Johany sanaa abdullah sharaf Fathy Elbouraey Eassa Reem Abdulaziz alnanih Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia software Engineering and Distributed system Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Dust particle surface potential in an argonhelium plasma using the(r,q)-distribution function

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Communications in Theoretical Physics 2024年第9期76卷 141-148页

作者： Bahaaudin M Raffah A A Abid abdullah Khan Amin Esmaeili Yas al-Hadeethi M N s Qureshi Department of Physics Faculty of SciencesKing Abdulaziz UniversityJeddah 21589Saudi Arabia Lithography in Devices Fabrication and Development Research Group Deanship of Scientific ResearchKing Abdulaziz UniversityJeddah 21589Saudi Arabia Joint Laboratory of Plasma Application Technology Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei230026China CAs Key Laboratory of Geospace Environment University of Science and Technology of ChinaHefei230026China Department of Physics Zhejiang Normal UniversityJinhua321005China Department of Physics Graduate School of ScienceTokyo Metropolitan UniversityMinami-Osawa 1-1Hachioji City192-0397Japan Department of Physics GC UniversityLahore 54000Pakistan

In this paper,the dust particle surface potential for argon-helium plasma is evaluated analytically and numerically in the context of negatively charged dust particles by employing a power-law(r,q)-distribution *** studies have reported the argon-helium plasma and conducted a brief theoretical and experimental *** deepen our understanding further,this study aims to analyze the argon-helium plasma comprehensively using the same pattern but with the(r,q)-distribution *** this purpose,the current balance equations are derived for electron,helium and argon ions,when these charge species attain the quasineutrality *** numerically examined the currents of plasma species for a broad range of effective distribution function parameters r and *** is revealed that the surface potential of dust particles is significantly affected by the parameters r and q,helium ion-to-electron temperature ratio,argon ion-to-electron temperature ratio,and helium ion to argon ion number density *** incorporating the multi-ion(argon-helium)species,the significance of low-temperature nonMaxwellian dusty(complex)plasma is briefly examined.

关键词： dusty plasma dust charge potential plasma physics helium argon plasma r q distribution function non-Maxwellian distribution

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Leveraging User-Generated Comments and Fused BiLsTM Models to Detect and Predict Issues with Mobile Apps

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Computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.s.Yafooz abdullah alsaeedi Department of Computer science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, saudi Arabia greatly depends on digitalservices to assist people and visitors. such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long short-TermMemory (LsTM), and Convolutional Neural Network Long short-Term Memory (CNN-LsTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LsTM bidirectional LsTM

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Intelligent solution system for Cloud security Based on Equity Distribution:Model and algorithms

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Computers, Materials & Continua 2024年第1期78卷 1461-1479页

作者： sarah Mustafa Eljack Mahdi Jemmali Mohsen Denden Mutasim al sadig abdullah M.algashami sadok Turki Department of Computer science and Information College of Science at ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer science College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Higher Institute of Computer science and Mathematics University of MonastirMonastir5000Tunisia Mars Laboratory University of SousseSousseTunisia Department of Logistic and Maintenance UFR MIM at MetzUniversity of LorraineMetzFrance Department of Computer and Information Technologies College of Telecommunicationand Information Riyadh CTITechnical and Vocational Training Corporation TVTCRiyadh12464Saudi Arabia Department of Computer science Higher Institute of Applied Sciences of SousseSousse UniversitySousse4000Tunisia

In the cloud environment,ensuring a high level of data security is in high *** planning storage optimization is part of the whole security process in the cloud *** enables data security by avoiding the risk of data loss and data *** development of data flow scheduling approaches in the cloud environment taking security parameters into account is *** our work,we propose a data scheduling model for the cloud *** is made up of three parts that together help dispatch user data flow to the appropriate *** first component is the Collector Agent whichmust periodically collect information on the state of the network *** second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the *** third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable *** should be noted that each part of the proposedmodel requires the development of its *** this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link *** algorithms are based on the grouping of transmitted files and the iterative *** proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)*** experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

关键词： Cyber-security cloud computing cloud security algorithms heuristics

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semantic segmentation and YOLO Detector over Aerial Vehicle Images

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Computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab alazeb Naif al Mudawi Mohammad alonazi Nouf abdullah almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to speeded Up Robust Feature(sURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter sURF

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Influence of cerium ions on structure-dependent magnetic properties of Ba-sr M-type hexaferrite nanocrystals

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Journal of Rare Earths 2024年第2期42卷 364-372,I0005页

作者： Ram A.Pawar sunil M.Patange P.M.Dighe sher singh Meena abdullah Gal-sehemi Mehboobali Pannipara santosh s.Jadhav Department of Physics ArtsCommerce and Science CollegeAhmednagar413711MaharashtraIndia Department of Physics Shri Krishna MahavidyalayaGunjotiDistOsmanabad413606MaharashtraIndia Padmashri Vikhe Patil College of Arts Science and CommercePravaranagarA/P.Loni(Kd)Tal.RahataDistAhmednagar413713MaharashtraIndia solid state Physics Division Bhabha Atomic Research CentreMumbai400085MaharashtraIndia Department of Chemistry King Khalid UniversityAbha61413Saudi Arabia Department of Physics D.S.M's ArtsCommerce and Science CollegeJintur431509MaharashtraIndia

The present study is a systematic effort to investigate the structure-sensitive magnetic parameters of Ce^(3+) substituted Ba-sr hexaferrite nanocrystals chemically formulated as Ba_(0.5)sr_(0.5)Ce_(x)Fe_(12-x)O_(19) where x=0.0-0.2 with Δx=*** hexaferrite powders were prepared using the sol-gel self-ignition route and structurally characterized by means of powder X-ray diffraction and Fourier transform infrared *** creation of the M-type hexaferrite phase within the synthesized samples was revealed from the Rietveld refinement of the X-ray *** occurrence of a secondary phase of CeO_(2) was revealed within the hexaferrites for the substitution,x> *** refined X-ray diffraction data were utilized to compute the lattice parameters,X-ray density,and lattice parameter *** crystal structure plotted from the refined XRD data reveals the occupancy of the ions at different lattice *** XPs data of the hexaferrite were analyzed to confirm the oxidation states of the constituent *** nanocrystalline nature of the hexaferrites was revealed from the crystallite sizes calculated using scherer's *** analysis of FTIR spectra confirms that only a fraction of Ce^(3+)accommodated in the lattice and the remaining Ce content reside in the form of the CeO_(2) *** morphology of the hexaferrites was analyzed from the FEsEM profiles of the ferrite *** magnetic behavior study was performed by analyzing the Curie temperature,hysteresis loops,and hyperfine interactions by means of susceptibility,V.s.M,and Mossbauer spectroscopy,*** hexaferrites with increasing coercivity,decreasing saturation magnetization,and decreasing Curie temperature are reported in the *** substituted Ce^(3+)ions inhibit the grain growth and create lattice imperfections giving rise to hexaferrites with tuned magnetic parameters suitable for different applications.

关键词： Ba-sr hexaferrite Ce^(3+)substitution XPs saturation magnetization Coercivity M?ssbauer spectroscopy

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Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

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Journal of Computer and Communications 2024年第5期12卷 124-139页

作者： Neda seifi abdullah al-Mamun Department of Computer & Cyber sciences— SCCS Augusta University Augusta Georgia USA

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

关键词： Data Layout Optimization CUDA Performance Optimization GPU Memory Optimization Dynamic Programming Matrix Multiplication Memory Access Pattern Optimization in CUDA

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Efficient Object segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf abdullah almujally saud s.alotaibi Abdulwahab alazeb Jeongmin Park Department of Computer science Air UniversityIslamabad44000Pakistan Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information system Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MsRC,Ms COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MsRC,Ms COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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