Search Results - (( java segmentation using algorithm ) OR ( pattern computational efficiency algorithm ))
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1
DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database
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Image clustering comparison of two color segmentation techniques
Published 2010“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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3
ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository
Published 2017“…Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. …”
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4
An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP
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A new split based searching for exact pattern matching for natural texts
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6
Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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Algorithms for frequent itemset mining: a literature review
Published 2018“…This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed. …”
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9
Fast directional search algorithm for fractional pixel motion estimation for H.264 AVC
Published 2013“…The proposed search pattern and early termination reduce computational time by almost 8% to 18% as compared to the hierarchical fractional pixel algorithm employed in the reference software with a negligible degradation in video quality and negligible increase in bit rate.…”
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10
Fast discovery of long patterns for association rules
Published 2003“…An algorithm called Flex for identifying frequent patterns especially efficient when the patterns are long is proposed by successive construction of the nodes lexicographic tree. …”
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11
Development of seven segment display recognition using TensorFlow on Raspberry Pi
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EFP-M2: Efficient model for mining frequent patterns in transactional database
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Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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15
Block matching algorithms for motion estimation using modified Cross-Diamond-Hexagonal search / Abd Razak Mahmud
Published 2008“…This algorithm basically employs two crossshaped search patterns consecutively in the very beginning steps and switch using diamond-shaped patterns. …”
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16
Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification
Published 2017“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. …”
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17
Hilbert-peano and zigzag: Two approaches mapping pattern of digital watermarking for text images authentication
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Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification
Published 2017“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.…”
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19
A filtering algorithm for efficient retrieving of DNA sequence
Published 2009“…The algorithm filtered the expected irrelevant DNA sequences in database from being computed for dynamic programming based optimal alignment process. …”
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An efficient model for indoor radio signal prediction and coverage estimation / Md. Sumon Sarker
Published 2011“…The proposed coverage algorithm also proves that the computation time is much less than that of the existing algorithm and the difference of computation time between the existing and the proposed algorithm is proportional to the number of total receiving points used in the indoor environment. …”
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