scholarly journals Sequential minimal optimization for quantum-classical hybrid algorithms

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Ken M. Nakanishi ◽  
Keisuke Fujii ◽  
Synge Todo
Keyword(s):  
Hybrid Algorithms ◽  
Sequential Minimal Optimization

Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

scholarly journals Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

2021 ◽  
Vol 13 (6) ◽  
pp. 3198
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm

2012 ◽  
Vol 20 (4) ◽  
pp. 673-683 ◽  
Author(s):  
Ta-Wen Kuan ◽  
Jhing-Fa Wang ◽  
Jia-Ching Wang ◽  
Po-Chuan Lin ◽  
Gaung-Hui Gu
Keyword(s):  
Optimization Algorithm ◽  
Vlsi Design ◽  
Sequential Minimal Optimization

Research on Fault Diagnosis Based on BP-SOM Hybrid Algorithms for NC Machine

2012 ◽  
Vol 544 ◽  
pp. 32-37 ◽  
Author(s):  
Chao Deng ◽  
Wen Long Dong ◽  
Yuan Hang Wang ◽  
Yao Xiong
Keyword(s):  
Fault Diagnosis ◽  
Manufacturing Industry ◽  
Hybrid Algorithms ◽  
Basic Data ◽  
Precision Data ◽  
The Real ◽  
Simulation Output ◽  
Nc Machine

NC machines are more and more widely used in manufacturing industry. As the key equipments in the factory, NC machines should be closely monitored to avoid faults. Therefore, the research on multi-function diagnosis based on BP-SOM is discussed for NC machine in the paper. Taking the precision data as the basic data for diagnosis, the research used BP and SOM algorithms to build up diagnosis models. As a result, the research has accomplished the diagnosing process and acquires good simulation output, which resembles with the real fact.

scholarly journals Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

2021 ◽  
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

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