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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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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

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Astrocytes integrate time and space

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Neural Regeneration Research 2025年第2期20卷 467-468页

作者： Justin Lines department of Biological sciences Columbia UniversityNew YorkNYUSA

Astrocytes read and react to synaptic transmission through tripartite synapses,where the binding of neurotransmitters onto astrocytic receptors triggers an increase in intracellular *** investigations have revealed that astrocytes exhibit two distinct states of intracellular calcium activity:(1)graded subcellular localized clusters with independently active microdomains,likely influenced by nearby synaptic events,and(2)whole-cell astrocyte calcium surges,believed to result from the coordinated activation of multiple ***,astrocyte calcium responses are not solely graded;instead,a spatial threshold of intracellular calcium activity can be overcome to elicit an astrocyte calcium *** these calcium responses,in turn,initiate downstream signaling pathways capable of modifying synaptic communication and overall network *** summary,astrocytes can function as integrators of local synaptic events,actively contributing to information processing within the brain.

关键词： graded integrate instead

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Large-scale branch contingency analysis through master/slave parallel computing

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Journal of Modern Power Systems and Clean Energy 2013年第2期1卷 159-166页

作者： Xi YANG Cong LIU Jianhui WANG decision and information sciences Division of Argonne National Laboratory ArgonneIL 60439USA

Contingency analysis(CA)requires fast execution time for real-time power system *** CA problems can naturally be divided into separate subtasks,parallel computing helps to speed up the computation *** paper proposes a master/slave parallel computing architecture and studies the computation of CA in a large-scale power system through high performance computing,adopting a message passing interface for *** particular,although the execution time of CA varies,there is a tradeoff between having an imbalanced workload and"paying"a synchronization penalty for parallel computing:either factor blocks the progress of *** proposed layered dynamic scheduling method is effective to tackle the challenge of high synchronization cost and workload imbalance and have the potential to further scale for the N-2 contingency analysis.

关键词： Massive parallel computing Power system Contingency analysis

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Applying Ricci flow to high dimensional manifold learning

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Science China(information sciences) 2019年第9期62卷 23-36页

作者： Yangyang LI Ruqian LU Key Lab of Management Decision and Information Systems Academy of Mathematics and Systems ScienceChinese Academy of Sciences University of Chinese Academy of sciences

In machine learning, a high dimensional data set such as the digital image of a human face is often viewed as a point set distributed on a differentiable manifold. In many cases, the intrinsic dimension of this manifold is low but the representation dimension of the data points is high. To ease data processing requirements, manifold learning(ML) techniques can be used to reduce a high dimensional manifold(HDM)to a low dimensional one while keeping the essential geometric properties, such as relative distances between points, unchanged. Traditional ML algorithms often assume that the local neighborhood of any point on an HDM is roughly equal to the tangent space at that point. This assumption leads to the disadvantage that the neighborhoods of points on the manifold, though they have a very different curvature, will be treated equally and will be projected to a lower dimensional space. The curvature is a different way of manifold processing, where traditional dimension reduction is ineffective at preserving the *** overcome this obstacle, we perform an "operation" on the HDM using Ricci flow before a manifold’s dimension reduction. More precisely, with the Ricci flow, we transform each local neighborhood of the HDM to a constant curvature patch. The HDM, as a whole, is then transformed into a subset of a sphere with constant positive curvature. We compare the proposed algorithm with other traditional manifold learning algorithms. Experimental results have shown that the proposed method outperforms other ML algorithms with a better neighborhood preserving rate.

关键词： manifold learning Ricci flow Ricci curvature dimension reduction curvature estimation

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Locality preserving projection on SPD matrix Lie group: algorithm and analysis

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Science China(information sciences) 2018年第9期61卷 50-66页

作者： Yangyang LI Ruqian LU Academy of Mathematics and Systems Science Key Lab of Management Decision and Information SystemsChinese Academy of Sciences University of Chinese Academy of sciences

Symmetric positive definite(SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data is impossible. The SPD matrix must first be transformed into a long vector, and then the dimension of this vector must be reduced. However, this approach breaks the spatial structure of the SPD matrix space. To overcome this limitation, we propose a new dimension reduction algorithm on SPD matrix space to transform high-dimensional SPD matrices into low-dimensional SPD matrices. Our work is based on the fact that the set of all SPD matrices with the same size has a Lie group structure, and we aim to transform the manifold learning to the SPD matrix Lie group. We use the basic idea of the manifold learning algorithm called locality preserving projection(LPP)to construct the corresponding Laplacian matrix on the SPD matrix Lie group. Thus, we call our approach Lie-LPP to emphasize its Lie group character. We present a detailed algorithm analysis and show through experiments that Lie-LPP achieves effective results on human action recognition and human face recognition.

关键词： manifold learning SPD matrix Lie group locally preserving projection Laplace operator LogEuclidean metric

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Determining winner in multi-attribute procurement auction:a method based on technical and business experts' evaluation information

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Journal of Systems Engineering and Electronics 2015年第6期26卷 1244-1253页

作者： Guangxin Gao Zhiping Fan Yang Liu department of information Management and decision sciences School of Business Administration Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

A method for solving the winner determination problem （WDP） in multi-attribute procurement auctions is proposed, based on technical and business experts＇ evaluation information. Firstly, on the background of procurements in China, a multi-attribute pro- curement auction mechanism is presented, where technical and business experts participate in the bid evaluation. Then, the concept of TOPSIS is used to determine the positive and negative ideal points of the WDP according to bid prices, the technical and business experts＇ evaluation information. Further, the closeness coefficient of each bidder （candidate supplier） is obtained by calculating the distances to the positive and negative ideal points. Thus, the winning supplier can be determined according to the closeness coefficients. Finally, a numerical example is used to illustrate the use of the proposed method.

关键词： multi-attribute auction winner determination technical and business experts bid evaluation.

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Shaping the Next Generation Pharmaceutical Supply Chain Control Tower with Autonomous Intelligence

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Journal of Autonomous Intelligence 2019年第1期2卷 56-71页

作者： Matthew Liotine information & decision sciences University of Illinois at Chicago

This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies,such as artificial intelligence and machine learning,impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical *** technologies can shift CTs to a model in which real-time information gathering,analysis,and decision making are *** can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by *** of the key points identified are in the areas of the fundamental capabilities that need to be supported and the improved level of decision visibility that they *** also consider some the challenges in achieving this,which include data quality and integrity,collaboration and data sharing across supply chain tiers,cross-system interoperability,decision-validation and organizational impacts,among others.

关键词： Supply Chain Management Control Tower Autonomous Intelligence

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Feature-Based Aggregation and Deep Reinforcement Learning:A Survey and Some New Implementations

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 1-31页

作者： Dimitri P.Bertsekas the department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision SystemsMassachusetts Institute of Technology

In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.

关键词： Reinforcement learning dynamic programming Markovian decision problems aggregation feature-based architectures policy iteration deep neural networks rollout algorithms

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ON SERVICES RESEARCH AND EDUCATION

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Journal of Systems Science and Systems Engineering 2006年第3期15卷 257-283页

作者： James M.TIEN Daniel BERG department of decision sciences and Engineering Systems Rensselaer Polytechnic Institute

The importance of the services sector can not be overstated; it employs 82.1 percent of the U. S. workforce and 69 percent of graduates from an example technological university. Yet, university research and education have not followed suit. Clearly, services research and education deserve our critical attention and support since services - and services innovation - serve as an indispensable engine for global economic growth. The theme of this paper is that we can and should build services research and education on what has occurred in manufacturing research （especially in regard to customization and intellectual property） and education; indeed, services and manufactured goods become indistinguishable as they are jointly co-produced in real-time. Fortunately, inasmuch as manufacturing concepts, methodologies and technologies have been developed and refined over a long period of time （i.e., since the 1800s）, the complementary set of concepts, methodologies and technologies for services are more obvious. However, while new technologies （e.g., the Internet） and globalization trends have served to enable, if not facilitate, services innovation, the same technologies （e.g., the Internet） and 21st Century realities （e.g., terrorism） are making services innovation a far more complex problem and, in fact, may be undermining previous innovations in both services and manufacturing. Finally, there is a need to define a ＂knowledge-adjusted＂ GDP metric that can more adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.

关键词： Services manufacturing customization innovation education intellectual property

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