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Orbit Weighting Scheme in the Context of Vector Space information Retrieval

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Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates information Security department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems Engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India department of Computing and information technology Usha Martin UniversityRanchi835103India department of information technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia department of CSE Delhi UniversityNew DelhiIndia College of Computing and information technology University of Doha for Science and TechnologyDoha24449Qatar department of information technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of information technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

Distributed Region Tracking and Perimeter Surveillance for Second-Order Multi-Agent Systems in Star-Shaped Sets

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Journal of Systems Science & Complexity 2024年第5期37卷 1995-2020页

作者： SHANG Juan MO Lipo MI Rongxin CAO Xianbing School of Mathematics and Statistics Beijing Technology and Business University The department of Foundation Shijiazhuang Institute of Railway Technology School of Computer and Artificial Intelligence Beijing Technology and Business University Research Centre for Agri-Product Quality Traceability Beijing Technology and Business University Coordination Center of China National Computer Network Emergency Response Technical Team

This paper investigates region tracking and perimeter surveillance of second-order multi-agent systems, where all agents move within a star-shaped set. First, by coordination transformations,the region tracking problem is converted from the star-shaped sets to a circular region. The authors employ communication and collaboration to complete region tracking and perimeter surveillance tasks,and then revert back to the star-shaped set by using inverse transformations. Second, the authors propose a distributed control strategy based on attractive and interaction potential functions, under which all agents can quickly track a given circular region and move around the perimeter. Finally,the authors validate the effectiveness and performance advantages of the proposed method through simulation experiments.

关键词： Distributed control multi-agent systems star-shaped sets target tracking

Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan J.Alyamani department of information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

Cloud‐based video streaming services:Trends,challenges,and opportunities

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CAAI Transactions on Intelligence technology 2024年第2期9卷 265-285页

作者： Tajinder Kumar Purushottam Sharma Jaswinder Tanwar Hisham Alsghier Shashi Bhushan Hesham Alhumyani Vivek Sharma Ahmed I.Alutaibi CSE department JMIETIYamunanagarHaryanaIndia department of information technology ASETAmity University Uttar PradeshNoidaIndia Tata Communications Limited New DelhiIndia department of information technology College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia Amity School of Engineering and technology Amity UniversityPatnaBiharIndia department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia

Cloud computing has drastically changed the delivery and consumption of live streaming *** designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time *** different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is *** such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are *** addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are *** future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and *** technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant *** on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

关键词： cloud computing video analysis video coding

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt department of information technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

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Computational Visual Media 2024年第3期10卷 593-608页

作者： Fan Zhang Gongguan Chen Hua Wang Caiming Zhang Shandong technology and business University Shandong 264005China School of information and Electrical Engineering Ludong UniversityYantai 264025China Shangdong University Shandong 250100China

Recently, facial-expression recognition (FER)has primarily focused on images in the wild, includingfactors such as face occlusion and image blurring, ratherthan laboratory images. Complex field environmentshave introduced new challenges to FER. To addressthese challenges, this study proposes a cross-fusion dualattention network. The network comprises three parts:(1) a cross-fusion grouped dual-attention mechanism torefine local features and obtain global information;(2) aproposed C2 activation function construction method,which is a piecewise cubic polynomial with threedegrees of freedom, requiring less computation withimproved ﬂexibility and recognition abilities, whichcan better address slow running speeds and neuroninactivation problems;and (3) a closed-loop operationbetween the self-attention distillation process andresidual connections to suppress redundant informationand improve the generalization ability of the *** recognition accuracies on the RAF-DB, FERPlus,and AffectNet datasets were 92.78%, 92.02%, and63.58%, respectively. Experiments show that this modelcan provide more effective solutions for FER tasks.

关键词： facial-expression recognition(FER) cubic polynomial activation function dualattention mechanism interactive learning self-attention distillation

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

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Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem department of information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia department of information Systems and technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic *** detection of these diseases is essential for effective *** propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf *** proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf *** model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning *** the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and *** wavelet transformation,the augmented images are decomposed into three frequency *** pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning *** models were trained using the approximate images of the third-level sub-band of the wavelet *** the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout *** proposed model was evaluated using a dataset of images of healthy and infected olive *** achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the *** finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

Near-field wideband beam training for ELAA with uniform circular array

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Science China(information Sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI department of Electronic Engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training