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

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

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information engineering infrastructure for life sciences and its implementation in China

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Science China(Life sciences) 2013年第3期56卷 220-227页

作者： ZHU WeiMin ZHU YunPing YANG XiaoLing institute of Basic Medical sciences Chinese Academy of Medical Sciences & School of Basic MedicinePeking Union Medical College Taicang institute of Life sciences information National Center of Protein sciences(Beijing) Beijing Proteome Research Center

Biological data,represented by the data from omics platforms,are accumulating *** some other data-intensive scientific disciplines such as high-energy physics,climatology,meteorology,geology,geography and environmental sciences,modern life sciences have entered the information-rich era,the era of the 4th *** creation of Chinese information engineering infrastructure for pan-omics studies(CIEIPOS) has been long overdue as part of national scientific infrastructure,in accelerating the further development of Chinese life sciences,and translating rich data into knowledge and medical *** gathering facts of current status of international and Chinese bioinformatics communities in collecting,managing and utilizing biological data,the essay stresses the significance and urgency to create a 'data hub' in CIEIPOS,discusses challenges and possible solutions to integrate,query and visualize these *** important component of CIEIPOS,which is not part of traditional biological data centers such as NCBI and EBI,is omics *** spectroscopy platform was taken as an example to illustrate the complexity of omics *** heavy dependency on computational power is *** demand for such power in omics studies is argued as the fundamental function to meet for *** outlook of CIEIPOS in hardware and network is discussed.

关键词： biological database services omics informatics information engineering infrastructure for pan-omics studies

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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

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A Novel Method for Synchronization Parameters Automatic Extraction in Computer Electromagnetic Eavesdropping

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Chinese Journal of Electronics 2019年第2期28卷 370-376页

作者： XU Yanyun institute of information Engineering Chinese Academy of Sciences

Electromagnetic compromising emanations are potential threat to computer security. Computer emits energy in the form of electromagnetic wave which includes the processed information. The electromagnetic wave can be received and decoded in the distance, so the unintended information leakage occurs. In this paper,the automatic information reconstruction for computer electromagnetic eavesdropping is studied. This paper attempts to combine simple digital signal processing method for the first time to extract electromagnetic leakage information. The performance of the new method is presented numerically and experimentally. Compared with other algorithms, this method has strong practicability and reliability. Under complex electromagnetic environment,the synchronization parameters can be extracted and the information can be reconstructed automatically, quickly and reliably.

关键词： Electromagnetic compromising emanations electromagnetic eavesdropping synchronization parameters extraction information reconstruction

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Incremental Alignment Manifold Learning

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Journal of Computer Science & Technology 2011年第1期26卷 153-165页

作者：韩志孟德宇徐宗本古楠楠 institute for information and System sciences Xi'an Jiaotong University

A new manifold learning method, called incremental alignment method （IAM）, is proposed for nonlinear dimensionality reduction of high dimensional data with intrinsic low dimensionality. The main idea is to incrementally align low-dimensional coordinates of input data patch-by-patch to iteratively generate the representation of the entire ***. The method consists of two major steps, the incremental step and the alignment step. The incremental step incrementally searches neighborhood patch to be aligned in the next step, and the alignment step iteratively aligns the low-dimensional coordinates of the neighborhood patch searched to generate the embeddings of the entire dataset. Compared with the existing manifold learning methods, the proposed method dominates in several aspects： high efficiency, easy out-of-sample extension, well metric-preserving, and averting of the local minima issue. All these properties are supported by a series of experiments performed on the synthetic and real-life datasets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically argued and experimentally demonstrated.

关键词： alignment incremental learning manifold learning nonlinear dimensionality reduction out-of-sample issue

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Universal adversarial examples and perturbations for quantum classifiers

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National Science Review 2022年第6期9卷 48-55页

作者： Weiyuan Gong Dong-Ling Deng Center for Quantum information Institute for Interdisciplinary Information Sciences (ⅢS) Tsinghua University Shanghai Qi Zhi institute

Quantum machine learning explores the interplay between machine learning and quantum physics, which may lead to unprecedented perspectives for both fields. In fact, recent works have shown strong evidence that quantum computers could outperform classical computers in solving certain notable machine learning tasks. Yet, quantum learning systems may also suffer from the vulnerability problem: adding a tiny carefully crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we study the universality of adversarial examples and perturbations for quantum classifiers. Through concrete examples involving classifications of real-life images and quantum phases of matter, we show that there exist universal adversarial examples that can fool a set of different quantum classifiers. We prove that, for a set of k classifiers with each receiving input data of n qubits, an O(ln [k]/2~n) increase of the perturbation strength is enough to ensure a moderate universal adversarial risk. In addition, for a given quantum classifier, we show that there exist universal adversarial perturbations, which can be added to different legitimate samples to make them adversarial examples for the classifier. Our results reveal the universality perspective of adversarial attacks for quantum machine learning systems, which would be crucial for practical applications of both near-term and future quantum technologies in solving machine learning problems.

关键词： quantum machine learning quantum classifiers adversarial examples measure concentration quantum no-free-lunch theorem

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Image composite authentication using a single shadow observation

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Science China(information sciences) 2015年第9期58卷 122-134页

作者： CAO XiaoChun ZHAO HanDong WANG Chuan ZHANG Wei State Key Laboratory of information Security Institute of Information Engineering Chinese Academy of Sciences

The paper presents a novel method for image composite authentication based on a single shadow in an outdoor image. Existing methods based on the shadow geometry consistency involve more than two shadow observations. We relax the requirement of two shadow observations in the same scene. As a trade-off,we use the information in EXIF(Exchangeable Image File) based on which the ground truth sun elevation is calculated. We also estimate the sun elevation based on geometric constraints the scene provides, whose consistency serves as the cue to authenticate image forgery. It is the first attempt, to our best knowledge, to make use of sun elevation with shadow of one object to authenticate outdoor images. Also, the novel use of GPS(Global Positioning System), and Date and Time data of captured image makes image forensics possible even in one image containing a single shadow. Experimental results on both the synthetic data and visually plausible images demonstrate the performance of the proposed method.

关键词： image forensics solar shadow sun elevation exchangeable image file geometry-based technique

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Experimental Hamiltonian Learning of an 11-Qubit Solid-State Quantum Spin Register

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Chinese Physics Letters 2019年第10期36卷 11-16页

作者： P.-Y.Hou L.He F.Wang X.-Z.Huang W.-G.Zhang X.-L.Ouyang X.Wang W.-Q.Lian X.-Y.Chang L.-M.Duan Center for Quantum information Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing 100084

Learning the Hamiltonian of a quantum system is indispensable for prediction of the system dynamics and realization of high fidelity quantum ***,it is a significant challenge to efficiently characterize the Hamiltonian which has a Hilbert space dimension exponentially growing with the system ***,we develop and implement an adaptive method to learn the effective Hamiltonian of an 11-qubit quantum system consisting of one electron spin and ten nuclear spins associated with a single nitrogen-vacancy center in a *** validate the estimated Hamiltonian by designing universal quantum gates based on the learnt Hamiltonian and implementing these gates in the *** experimental result demonstrates a well-characterized 11-qubit quantum spin register with the ability to test quantum algorithms,and shows our Hamiltonian learning method as a useful tool for characterizing the Hamiltonian of the nodes in a quantum network with solid-state spin qubits.

关键词： Hamiltonian quantum system

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PARAMETRIC EQUATION SOLVING AND QUANTIFIER ELIMINATION IN FINITE FIELDS WITH THE CHARACTERISTIC SET METHOD

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Journal of Systems Science & Complexity 2012年第4期25卷 778-791页

作者： Zhenyu HUANG State Key Laboratory of information Security Institute of Information EngineeringChinese Academy of Sciences

For a parametric algebraic system in finite fields, this paper presents a method for computing the cover and the refined cover based on the characteristic set method. From the cover, the author knows for what parametric values the system has solutions and at the same time presents the solutions in the form of proper chains. By the refined cover, the author gives a complete classification of the number of solutions for this system, that is, the author divides the parameter space into several disjoint components, and on every component the system has a fix number of solutions. Moreover, the author develops a method of quantifier elimination for first order formulas in finite fields.

关键词： Characteristic set method finite field parametric equation system quantifier elimination.

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VNF-FG design and VNF placement for 5G mobile networks

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Science China(information sciences) 2017年第4期60卷 17-31页

作者： Jiuyue CAO Yan ZHANG Wei AN Xin CHEN Jiyan SUN Yanni HAN State Key Laboratory of information Security Institute of Information EngineeringChinese Academy of Sciences

Network function virtualization(NFV) is envisioned as one of the critical technologies in 5thGeneration(5G) mobile networks. This paper investigates the virtual network function forwarding graph(VNFFG) design and virtual network function(VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms(multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms(random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04%, 87.76% and88.42% of the average total bandwidth consumption, respectively, and achieves only 13.81%, 25.04% and 25.41%of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and GreedyNSGA-II together can also reduce the total bandwidth consumption remarkably.

关键词： network function virtualization VNF-FG design VNF placement multi-objective optimization genetic algorithm

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