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Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

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Journal of Systems Engineering and Electronics 2012年第2期23卷 208-215页

作者： Pei Wang Gerhard Reinelt Yuejin Tan College of Information Systems and Management National University of Defense Technology Changsha 410073 P. R. China Discrete Optimization Research Group Ruprecht-Karls Universitaeit Heidelberg Heidelberg 69120 Germany

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning.mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

关键词： non-identical parallel machine scheduling problem with multiple time windows (NPMSPMTW) oversubscribed self- adaptive large neighborhood search (SALNS) machine learning.

Discrimination and quantification of scar tissue by Mueller matrix imaging with machine learning

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Journal of Innovative Optical Health Sciences 2023年第5期16卷 47-58页

作者： Xi Liu Yanan Sun Weixi Gu Jianguo Sun Yi Wang Li Li Department of Ophthalmology Beijing Children's Hospital Capital Medical UniversityNational Center for Children's Health Beijing 100045P.R.China Experimental Research Center China Academy of Chinese Medical Sciences Beijing 100700 P.R.China China Academy of Industrial Internet Beijing 100102 P.R.China Department of Ophthalmology&Visual Science Eye&ENT Hospital Shanghai Medical College Fudan UniversityShanghai 200031P.R.China

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration *** evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis ***,the distinguishment of conjunctiva,sclera and the scar tissue in the surgical area still relies on pathologists'*** polarized light imaging is sensitive to anisotropic properties of the media,it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion,organization and the orientation of the *** this paper,we defined the conjunctiva,sclera,and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix *** score based on parameters derived from Mueller matrix and machine learning.was calculated and tested as a diagnostic *** a result,the discrimination score of three target tissues showed significant difference between each other(p<0.001).The visualization of the discrimination results showed significant contrast between target *** study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.

关键词： Tissue discrimination glaucoma filtration surgery polarized light Mueller matrix machine learning.

Research and Modelling on the E-commerce Consumer Behavior based on Intelligent Recommendation System and machine learning

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International Journal of Technology Management 2016年第7期 61-63页

作者： Zhang Haixia Harbin Finance University HarbinHeilongjiang150030China

In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.

关键词： E-commerce Consumer Behavior Intelligent Recommendation System machine learning.

machine learning.for spatial environmental data :

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Knowledge Representation for the Geometrical Shapes

1000年

作者： Mikhail Kanevski Alexei Pozdnoukhov and Vadim Timonin.

Includes bibliographical references (p. [347]-371) and index.

ISBN: (纸本)9780849382376;9782940222247

Includes bibliographical references (p. [347]-371) and index.

关键词： Environmental mapping machine learning. Mappings (Mathematics) Cartography Geographic information systems.

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Journal of Mathematics and System Science 2018年第3期8卷 77-83页

作者： Abolfazl Fatholahzadeh Dariush Latifi University of CentraleSupelec Metz 57070 France Department of Mathematics and Applications University of Mohaghegh Ardabili Ardabi156165 Iran

This paper outlines the necessity of the knowledge representation for the geometrical shapes （KRGS）. We advocate that KRGS for being powerful must contain at least three major components, namely （1） fuzzy logic scheme; （2） the machine learning.technique; and （3） an integrated algebraic and logical reasoning. After arguing the need for using fuzzy expressions in spatial reasoning, then inducing the spatial graph generalized and maximal common part of the expressions is discussed. Finally, the integration of approximate references into spatial reasoning using absolute measurements is outlined. The integration here means that the satisfiability of a fuzzy spatial expression is conducted by both logical and algebraic reasoning.

关键词： Knowledge representation integrated algebraic and logical fuzzy logic reasoning machine learning.

Bidirectional Automated Branch and Bound Algorithm for Feature Selection

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Journal of Shanghai University(English Edition) 2005年第3期9卷 244-248页

作者：杨胜施鹏飞 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P. R. China

Feature selection is a process where a minimal feature subset is selected from an original feature set according to a certain measure. In this paper, feature relevancy is defined by an inconsistency rate. A bidirectional automated branch and bound algorithm is presented. It is a new complete search algorithm for feature selection, which performs feature deletion and feature addition in parallel. Its bound is determined by inconsistency rate of the original feature set, hence termed as ‘automated’. Experimental study shows that it is fit for feature selection.

关键词： feature selection pattern classification data mining machine learning.

Theoretical Research on Novel Data Mining Algorithm based on Fuzzy Clustering Theory and Deep Neural Network

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International Journal of Technology Management 2015年第7期 109-111页

作者： Ye Li Weifang University of Science and Technology ShouguangWeifangShandong 262700 China

With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.

关键词： Fuzzy Clustering Data Mining Deep Neural Network machine learning.