Search Results - (( sequence optimization model algorithm ) OR ( pattern classification modified algorithm ))
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1
A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
Published 2010“…The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. …”
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2
A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. …”
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3
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. …”
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4
Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA
Published 2014“…An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. …”
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5
Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
Published 2019“…The required memory of this algorithm is also time independent. In addition, the optimal state sequence obtained by the EVRA algorithm is the same as that obtained by the classical Viterbi algorithm for HHMM.…”
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6
An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP
Published 2021“…To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. …”
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7
Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines
Published 2009“…Due to NP-hard nature of sequencing problem in mixed model assembly line, a genetic algorithm is applied to cope with problem complexity and obtain a near optimal solution in a reasonable amount of time. …”
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8
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…That is, to use training speech patterns to generate classification rules that can be used later to classify input words patterns. …”
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9
Classification of herbs plant diseases via hierarchical dynamic artificial neural network
Published 2010“…This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. …”
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10
Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review
Published 2019“…This review focused on ASP modeling approach, optimization algorithms and optimization objectives. …”
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11
Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach
Published 2022“…An example of the DNN model is the Attentive Sequence-to-Sequence (Seq2Seq) model that was first created to tackle a problem setting in language processing. …”
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12
Modern fuzzy min max neural networks for pattern classification
Published 2019“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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13
Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization
Published 2019“…The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). …”
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14
Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…This model are built up with 64-40-4 neural network where input data are 8 x 8 dimension image and output are classified to 4 digits which are 0, 1, 2 and 3. Two modified algorithms are proposed in this research, which are mixture of the momentum algorithm with different learning rate algorithms. …”
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15
Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework
Published 2011“…This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. …”
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16
Modelling of Assembly Sequence Planning Problem using Base Part Concept
Published 2018“…Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. …”
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17
MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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18
MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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19
MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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20
MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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