Bio-inspired High-Utility Item Framework based Particle Swarm Optimization Tree Algorithms for Mining High Utility Itemset

2020 ◽  
pp. 265-276
Author(s):  
V. Jeevika Tharini ◽  
S. Vijayarani
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Tree Algorithms ◽  
High Utility

scholarly journals Implementation of Bio-Inspired Algorithms in High Utility Itemset Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 7238-7243
Keyword(s):  
Particle Swarm Optimization ◽  
Execution Time ◽  
Online Advertising ◽  
Particle Swarm ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Itemset Mining ◽  
Important Research Topic ◽  
High Utility ◽  
Biological Organisms

Utility based itemset mining hasevolved as an important research topic in data mining, having application in retail-market data analysis, stock market prediction, online advertising and so on. Bio-inspired computation attempts to replicate the way in which biological organisms and suborganisms operate using abstract computing ideas from living phenomena or biological systems.This study focuses on the application of bio-inspired algorithms on high utility itemset mining. A detailed analysis on the performance of thesealgorithmswere conducted on various parameters such as execution time, memory usage and the number of high utility items identified. Experimental result suggest Particle Swarm Optimization excels in its efficiency in execution time and memory usage.When the number of high utility items identified are concerned, it is Genetic Algorithm which outperforms Particle Swarm optimization and Bats algorithm

Mining high-utility itemsets based on particle swarm optimization

2016 ◽  
Vol 55 ◽  
pp. 320-330 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Lu Yang ◽  
Philippe Fournier-Viger ◽  
Jimmy Ming-Thai Wu ◽  
Tzung-Pei Hong ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
High Utility ◽  
High Utility Itemsets

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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