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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt college of computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India college of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

WordleNet: A Visualization Approach for Relationship Exploration in Document Collection

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Tsinghua Science and Technology 2020年第3期25卷 384-400页

作者： Xu Wang Zuowei Cui Lei Jiang Wenhuan Lu Jie Li the college of Intelligence and computing

Document collections do not only contain rich semantic content but also a diverse range of *** propose WordleNet,an approach to supporting effective relationship exploration in document *** approaches mainly focus on semantic similarity or a single category of *** constructing a general definition of document relationships,our approach enables the flexible and real-time generation of document relationships that may not otherwise occur to human researchers and may give rise to interesting patterns among *** novel visual components are integrated in our approach,the effectiveness of which has been verified through a case study,a comparative study,and an eye-tracking experiment.

关键词： document relationship interaction techniques text visualization relationship visualization visual analytics

A Rigorous Architectural Approach to Adaptive Software Engineering

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Journal of Computer Science & Technology 2009年第2期24卷 183-188页

作者： Jeff Kramere Jeff Magee Department of computing Imperial College London

The engineering of distributed adaptive software is a complex task which requires a rigorous approach. Software architectural （structural） concepts and principles are highly beneficial in specifying, designing, analysing, constructing and evolving distributed software. A rigorous architectural approach dictates formalisms and techniques that are compositional, components that are context independent and systems that can be constructed and evolved incrementally. This paper overviews some of the underlying reasons for adopting an architectural approach, including a brief ＂rational history＂ of our research work, and indicates how an architectural model can potentially facilitate the provision of self-managed adaptive software system.

关键词： adaptiye systems self-managed systems autonomic systems software architecture

Predicting protein subchloroplast locations:the 10th anniversary

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Frontiers of Computer Science 2021年第2期15卷 1-11页

作者： Jian SUN Pu-Feng DU college of Intelligence and computing Tianjin UniversityTianjin300350China

Chloroplast is a type of subcellular organelle in green plants and *** is the main subcellular organelle for conducting photosynthetic *** proteins,which localize within the chloroplast,are responsible for the photosynthetic process at molecular *** chloroplast can be further divided into several *** in different compartments are related to different steps in the photosynthetic *** the molecular function of a protein is highly correlated to the exact cellular localization,pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic *** process for determining protein subchloroplast location is always costly and time ***,computational approaches were developed to predict the protein subchloroplast locations from the primary *** the last decades,more than a dozen studies have tried to predict protein subchloroplast locations with machine learning *** sequence features and various machine learning algorithms have been introduced in this research *** this review,we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast *** compare these studies in the aspects of benchmarking datasets,sequence features,machine learning algorithms,predictive performances,and the implementation *** summarized the progress and current status in this special research *** also try to figure out the most possible future works in predicting protein subchloroplast *** hope this review not only list all existing works,but also serve the readers as a useful resource for quickly grasping the big picture of this research *** also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.

关键词： subchloroplast locations sequence features performance measures online services machine learning

Single image super-resolution:a comprehensive review and recent insight

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Frontiers of Computer Science 2024年第1期18卷 139-156页

作者： Hanadi AL-MEKHLAFI Shiguang LIU college of Intelligence and computing Tianjin UniversityTianjin 300350China

Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the *** main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).It is an ongoing process in image technology,through up-sampling,de-blurring,and *** neural network(CNN)has been widely used to enhance the resolution of images in recent *** alternative methods use deep learning to improve the progress of image super-resolution based on ***,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video ***,we present the challenges and assess future trends in super-resolution based on deep learning.

关键词： super-resolution deep learning single-image interpolation-based learning-based reconstruction-based

An efficient protocol for secure multicast key distribution in the presence of adaptive adversaries

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Science China(Information Sciences) 2017年第5期60卷 181-192页

作者： Huafei ZHU School of Computer and computing Science Zhejiang University City College

In this paper, an efficient construction of multicast key distribution schemes based on semantically secure symmetric-key encryption schemes and cryptographically strong pseudo-random number generators is presented and analyzed. The proposed scheme is provably secure against adaptive adversaries leveraging the security amplification technique defined over the logical key hierarchy structures. Our protocol tolerates any coalition of revoked users; in particular, we do not assume any limit on the size or structure of the coalition. The proposed scheme is efficient as a performance of Join or Leave procedure requires 2 log(N) multicast activities defined over a sibling ancestor node set, 2 log(N) internal state updates of the underlying pseudo-random number generator and 2 log(N) symmetric-key encryption activities for N users in a session.

关键词： adaptive adversary multicast key distribution pseudo-random number generator semantic secu rity symmetric-key encryption

Line drawing via saliency map and ETF

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Frontiers of Computer Science 2022年第5期16卷 159-171页

作者： Shiguang LIU Ziqi LIU college of Intelligence and computing Tianjin UniversityTianjin 300350China

Line drawing is a means of superior visual communication which can effectively convey useful information to *** usually draw what they see rather than what exists,which means the most attractive object is emphasized while the rest are ***,artists draw the whole object with coherent lines instead of fractured lines,which also contribute to the outstanding visual *** these perspectives,generating line drawings with saliency and coherence remains a great *** line drawing generation methods ignore these important properties and cannot generate coherent lines in some cases since they do not take into account how artists draw a *** this end,a novel saliency-aware line drawing method was proposed to better grasp the viewer’s attention on an ***,a saliency enhanced line extraction method combining saliency map and edge tangent flow was proposed to ensure the saliency and coherence of lines,especially in salient but low contrast ***,the salient information was highlighted while irrelevant details were eliminated by inhibiting lines in less salient areas to enhance the saliency of the line ***,the transparency of lines was adjusted to further emphasize important *** results showed that our method can generate line drawings with better visual saliency and coherence than the state-of-the-art methods.

关键词： line drawing saliency coherence

vSpec: workload-adaptive operating system specialization for virtual machines in cloud computing

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Science China(Information Sciences) 2016年第9期59卷 47-62页

作者： Xinkui ZHAO Jianwei YIN Zuoning CHEN Sheng HE college of Computer Science Zhejiang University National Parallel computing Engineering Research Center Jiangnan Institute of computing Technology

In general, operating systems(OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same *** in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine(VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, v Spec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. v Spec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of v Spec on benchmarks and real-world applications.

关键词： workload classification operating system specialization workload-aware cloud computing v Spec system performance