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COMPUTER 2024年第9期57卷 96-100页

作者： Hsu, D. Frank Kristal, Bruce S. Schweikert, Christina Fordham Univ Sci New York NY 10023 USA Fordham Univ Comp & Informat Sci New York NY 10023 USA Tufts Univ Jean Mayer US Dept Agr Human Nutr Res Ctr Aging Boston MA 02111 USA St Johns Univ Comp Sci Queens NY 11439 USA St Johns Univ Data Sci Program Queens NY 11439 USA

Combinatorial fusion analysis is a paradigm that seeks to provide methods and workflows for combining multiple scoring systems in computational learning and modeling, informatics, and intelligent systems.

关键词： Combinatorial Mathematics Computational Modeling Intelligent Systems Data Models Diversity Reception Informatics Encoding MCDM Large Language Models Modeling Neural Network Drug Discovery Deep Learning Computational Model Scoring System Scoring Function Informatics Workspace Recent Example Intelligent Systems Information Retrieval Virtual Screening Euclidean Space combination of algorithms Protein Structure Prediction Information Fusion Global Space Gene Expression Microarray Analysis Ranking Function Motif Detection System A Artificial Neural Network Information Bits Weak Order Ranking System Ranking Score Binary Classification

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Descriptive Image Analysis

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2023年第4期33卷 784-839页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

An overview of the main methods, models, and results of Descriptive Image Analysis is given. Descriptive Image Analysis is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The state of the art and trends in the development of Descriptive Image Analysis are determined by the methods, models, and results of the Descriptive Approach to image analysis and understanding. As the methods and apparatus of the Descriptive Approach to the analysis and understanding of images were developed and refined, its interpretation was proposed, defined as Descriptive Image Analysis. The main goal of Descriptive Image Analysis is to structure and standardize the various methods, processes, and concepts used in image analysis and recognition. Descriptive Image Analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in image analysis, recognition, and understanding problems, in particular, associated with automating the extraction of information from images to make intelligent decisions (diagnosis, prediction, detection, assessment, and identification patterns of objects, events and processes). Descriptive Image Analysis makes it possible to solve both problems related to constructing formal descriptions of images as recognition objects and problems of synthesizing procedures for recognizing and understanding images. It is suggested that the processes of analysis and evaluation of information represented in the form of images (problem solution trajectories) can generally be considered a sequence/combination of transformations and calculations of a set of intermediate and final (determining the solution) estimates. These transformations are defined by equivalence classes of images and their representations. The latter are defined descriptively, i.e., using a basic set of prototypes and corresponding generating transformations that are functionally complete with respect t

关键词： descriptive approach to image analysis and understanding mathematical theory of image analysis Descriptive Image Analysis image formalization space descriptive image algebras descriptive image algebras with one ring descriptive image models efficient algorithms for recognizing spatial information descriptive algorithmic schemes descriptive algorithmic representation schemes images multiple classifiers combination of algorithms combination of data dual image representations reducing images to a recognizable form generating descriptive trees image representations axioms for Descriptive Image Analysis

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The calculation of the reliability index in nonlinear problems: A new method

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AIN SHAMS ENGINEERING JOURNAL 2020年第3期11卷 705-716页

作者： Alamdarloo, Pezhman Derakhshan Moghaddam, Mehdi Azhdary Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Hasofer-Lind, Rackwitz-Fiessler, and Monte Carlo could lead to unstable, fluctuating, and distorted solutions to nonlinear problems, featuring complicated structural performance functions. The present study aimed to propose a new method by combining the particle swarm optimization and differential evolution algorithms in order to calculate the reliability index. The performance of the proposed method was evaluated by 10 examples from different studies, and the convergence results were compared to the results of studies such as Hasofer-Lind, Rackwitz-Fiessler, Monte Carlo and some other methods. To verify the accuracy of the proposed method, to verify the accuracy of the proposed method, a reliability index chart was applied. The comparisons indicated the high accuracy and speed of the proposed method. Accordingly, in higher order nonlinear problems, the proposed method successfully calculated the reliability index while some failed to solve these problems. (C) 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.

关键词： Reliability index combination of algorithms Particle swarm optimization algorithm Differential evolution algorithm

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Improvement by combination: How to increase the Performance of Optimisation algorithms by combining them 10

Improvement by combination: How to increase the Performance ...

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10th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2014

作者： Ostler, Johannes Wilke, Peter University of Erlangen-Nuernberg Computer Science Department Multi Criteria Optimisation Group Martensstrasse 3 Erlangen91058 Germany

ISBN: (纸本)9780992998400

Working with different optimisation algorithms leads to the observation that different types of solutions are generated, disclosing their different nature, their pros and cons. We investigated the question whether or not the combination of optimisation algorithms yield even better results than each of its constituents or will their drawbacks make things even worse. © PATAT 2014 - Proceedings of the 10th International Conference on the Practice and Theory of Automated Timetabling. All rights reserved.

关键词： combination of algorithms Optimisation algorithms Performance Improvement

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An Adaptive Algorithm for Public Transport Arrival Time Prediction Based on Hierarhical Regression 18

An Adaptive Algorithm for Public Transport Arrival Time Pred...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Agafonov, Anton Myasnikov, Vladislav SSAU RAS IPSI Samara Russia

ISBN: (纸本)9781467365963

In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.

关键词： public transport arrival time prediction combination of algorithms hierarchical regression

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An Adaptive Algorithm for Public Transport Arrival Time Prediction Based on Hierarhical Regression

An Adaptive Algorithm for Public Transport Arrival Time Pred...

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International IEEE Conference on Intelligent Transportation Systems

作者： Anton Agafonov Vladislav Myasnikov Samara State Aerospace University (SSAU) Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS) Samara Russia

ISBN: (纸本)9781467365970

关键词： public transport arrival time prediction combination of algorithms hierarchical regression Public Transit control parameter Prediction algorithms composite algorithm arrival time Adaptive algorithms metropolis Traffic density Regression (Psychology)

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Applied Research of Improved Hybrid Discrete PSO for Dynamic Job-shop Scheduling Problem

Applied Research of Improved Hybrid Discrete PSO for Dynamic...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wang, Shufeng Xiao, Xiaocheng Li, Fei Wang, Ce Zhengzhou Univ Sch Elect Engn Zhengzhou Henan Province Peoples R China

ISBN: (纸本)9781424467129

By providing a detailed analysis of the particle swarm optimization (PSO) principle and job-shop scheduling problems, this paper presents a new hybrid discrete GAPSO combining the genetic strategy. Adjusting factors are introduced to regulate the generation of convergence;the proposed algorithm is tested by a set of benchmark problems. The results obtained show good convergence of the algorithm. On this basis, a new event-driven strategy for dynamic JSP is proposed, with regard to some uncertain dynamic events like inserting new jobs and machine failures, the proposed algorithm can reschedule once there occur uncertain dynamic events. The results of simulation have confirmed the effectiveness and feasibility of the improved hybrid discrete GAPSO algorithm.

关键词： Dynamic job-shop scheduling problem Discrete particle swarm optimization Genetic algorithm combination of algorithms Event-driven

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Validating the Reliability of Five Ventricular Fibrillation Detecting algorithms

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4th European Conference of the International Federation for Medical and Biological Engineering (ECIFMBE)

作者： Ismail, A. H. Fries, M. Rossaint, R. Leonhardt, S. Rhein Westfal TH Aachen Philips Chair Med Informat Technol Pauwelsstr 20 D-52074 Aachen Germany Rhein Westfal TH Aachen Philips Chair Med Informat Technol Helmholtz Inst Biomed Engn D-52062 Aachen Germany Rhein Westfal TH Aachen Aachen Univ Hosp Dept Anesthesiol D-52062 Aachen Germany

ISBN: (纸本)9783540892076

Early accurate detection of ventricular fibrillation (VF) is crucial for inducing a proper electrical therapy (e.g. defibrillation). For automatic defibrillation, several detection algorithms have been developed to discriminate between VF and non-VF rhythms. However, many of them are not very accurate or at least very complex for real-time implementation. The present study investigates the performance of five VF detection techniques by using a ventricular tachyarrhythmia ECG database annotated on a beat-to-beat-level. The algorithms were selected mainly for their accuracy and low computational cost. In addition, the authors propose a method based on a combination of two algorithms into one detection system. For each algorithm, the sensitivity and specificity were computed by comparing the decisions given by the algorithm with those found in the annotation files associated with the ECG database. The obtained results confirm that 1. each of the presented methods is capable of identifying VF (however, certain improvements are still required) and 2. that the use of two algorithms in series combines their advantages and reduces the error committed by each of them.

关键词： Ventricular fibrillation VF Automated external defibrillator AED Arrhythmia classification system combination of algorithms

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