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AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

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Computers, Materials & Continua 2024年第9期80卷 4577-4601页

作者： Ahmad Nahar Quttoum Muteb Alshammari Department of Computer Engineering Faculty of EngineeringThe Hashemite UniversityZarqa13133Jordan Department of Information Technology Faculty of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility ***,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs ***,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling *** utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their *** providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients *** this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client *** this,the providers seek to retrieve those leased unused resources from their *** is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s ***,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned ***,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each *** to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

关键词： Data center networks energy-aware resource management resource utilization game-theory mechanisms

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A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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UNVEILING THE WAVE CONCEPT IN ELECTROMAGNETIC THEORY: APPLICATION TO THE TRANSVERSE WAVE APPROACH FOR MICROWAVE SYSTEMS ANALYSIS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2024年第3期86卷 251-268页

作者： Klai, Zeineb Hammami, Mohamed Ali Faculty of Sciences of Sfax University of Sfax Tunisia Faculty of Computing and Information Technology Northern Border University Saudi Arabia

Numerical methods have established their efficacy in diverse domains, including electric machines, telecommunications, radar systems, and digital computing. Within this paradigm, the Wave Concept emerges as a pivotal tool for accelerating these methods. By transforming integral formulations of the electromagnetic (EM) field into algebraic problems within the framework of Hilbert space methods, the Wave Concept facilitates enhanced computational efficiency. This paper delves into the development of the Wave Concept and its application within the Transverse Wave Approach, shedding light on its utility in addressing EM field challenges. The Transverse Wave Approach is explored as a method to convert integral formulations into algebraic problems, offering a novel perspective on problem-solving within the electromagnetic domain. To gauge its computational effectiveness, a comprehensive evaluation is conducted in the specific context of microwave systems. The outcomes of this investigation contribute to a deeper understanding of the Transverse Wave Approach's potential impact on advancing numerical methods in electromagnetics. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Integral equations

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Feature-Limited Prediction on the UCI Heart Disease Dataset

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Computers, Materials & Continua 2023年第3期74卷 5871-5883页

作者： Khadijah Mohammad Alfadli Alaa Omran Almagrabi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart diseases are the undisputed leading causes of death ***,the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart *** potentially indicative factors exist,such as abnormal pulse rate,high blood pressure,diabetes,high cholesterol,*** analyzing these health signals’interactions is challenging and requires years of medical training and ***,this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence *** specifically,this paper builds a hybrid model as a tool for data mining algorithms like feature *** goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy *** contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum *** developed model achieves 84.24%accuracy,89.22%Recall,and 83.49%Precision using only a subset of the features.

关键词： Machine learning feature selection heart disease

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A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

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Computers, Materials & Continua 2023年第8期76卷 2079-2093页

作者： Alaa Omran Almagrabi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental *** existing work solely focuses on classifying the audio system of CPS without utilizing feature *** study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within *** primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of *** has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient ***,the data is augmented and ***,the mel spectrogram features are extracted through two-way feature ***,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature ***,the classification is performed using the CNN-LSTM *** methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification *** study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural *** dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

关键词： Cyber-physical system internet of things feature extraction,classification CNN principal component analysis mel spectrograms monitoring deep learning

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COMPARATIVE PERFORMANCE ANALYSIS OF FCFS, JOHNSON SEQUENCING AND DYNAMIC MAX-MIN JOHNSON SEQUENCING (DMMJS) ALGORITHM USING THREE SERVERS IN CLOUD computing ENVIRONMENT

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2023年第3期85卷 181-194页

作者： Banerjee, Pallab Roy, Sharmistha Faculty of Computing and Information Technology Usha Martin University Ranchi India

This paper presents Dynamic Max-Min Johnson scheduling algorithm that offers the optimal sequence. Johnson's rule is perhaps the most classical algorithm in the scheduling field. Dynamic Max-Min Johnson scheduling algorithm gives the most excellent way to the system flow shop to reduce the makespan in polynomial time. The sequencing problem offers with determining a most useful series of acting a number of tasks via a finite number of machines in line with some pre-assigned order with a view to optimize the output. The goal is to decide on the most desirable order of performing the jobs in such a manner that the full elapsed time may be minimal. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Scheduling algorithms

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A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

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Intelligent Automation & Soft computing 2023年第8期37卷 2355-2370页

作者： Ali Ahmed Alaa Omran Almagrabi Omar MBarukab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University–RabighRabigh21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz University-RabighRabigh21589Saudi Arabia

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image *** are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient *** methods are applied to enhance the retrieval performance of CBMIR *** new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these *** study proposes the relative difference-based similarity measure(RDBSM)for *** new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture ***,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)*** proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval *** effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

关键词： Medical image retrieval feature extraction similarity measure fusion method

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Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 3539-3560页

作者： Gibrael Abosamra Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image *** binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution *** to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate *** pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate *** any of the previous versions will give moderate results but with very low ***,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate *** memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used ***,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar *** image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new *** results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by

关键词： Genetic algorithms memetic algorithm convolutional neural network object detection adaptive binarization filters license plate detection

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (Science and information Organization). All rights reserved.

关键词： Data integrity

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