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New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique
Published 2016“…There are 6 main designs that the algorithms proposed namely the Orthogonal-Diamond Search Algorithm with Small Diamond Search Pattern (ODS-SDSP), the Orthogonal-Diamond Search Algorithm with Large Diamond Search Pattern (ODS-LDSP), the Diamond-Orthogonal Search Algorithm with Small Diamond Pattern (DOS-SDSP), the Diamond-Orthogonal Search Algorithm with Large Diamond Pattern (DOS-LDSP), the Modified Diamond-Orthogonal Search Algorithm with Small Diamond Pattern (MDOS-SDSP), and the Modified Diamond-Orthogonal Search Algorithm with Large Diamond Pattern (MDOS-LDSP). …”
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Linear-PSO with binary search algorithm for DNA Motif Discovery / Hazaruddin Harun
Published 2015“…Evaluation of these algorithms showed that the results are unsatisfactory due to the lower validity and accuracy of these algorithms. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). …”
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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm
Published 2023Article -
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Songket pattern classification using backpropagation neural network / Nik Aidil Syawalni Nik Mazlan
Published 2024“…The BPNN algorithm was implemented to proficiently identify and classify Songket pattern, and the evaluation involved analyzing predicted results with different data splitting ratios, such as 70:30 and 80:20. …”
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IncSPADE: An Incremental Sequential Pattern Mining Algorithm Based on SPADE Property
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Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…To evaluate the performance of the developed algorithm, the average PSNR value, average search point and average elapsed processing time is calculated. …”
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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 datasets and the results were compared with other algorithms. …”
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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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Statistical computing for forensic firearm pattern identification: Evaluating a fisher linear discriminant analysis-based algorithm against fixed-impulse noise
Published 2025“…This study evaluates the robustness of the FLDA-based ballistic identification algorithm against fixed-value impulse noise, including pepper, salt, and salt-and-pepper noise. …”
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Linear-pso with binary search algorithm for DNA motif discovery / Hazaruddin Harun
Published 2015“…Evaluation of these algorithms showed that the results are unsatisfactory due to the lower validity and accuracy of these algorithms. …”
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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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An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
Published 2016“…Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. …”
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15
Design of intelligent Qira’at identification algorithm
Published 2017“…The process of the SPAP Algorithm is to extend parameters of the Affine Projection Block with two different selections of windowing length that affect the final accuracy on pattern classification. …”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification
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Stereo matching algorithm based on hybrid convolutional neural network and directional intensity difference
Published 2021“…A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. …”
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A framework of enhanced authentication for PDF textual documents using Zigzag-LSB embedding algorithm
Published 2024“…The effectiveness of the algorithm has been established since it uses an image-based approach after conversion between document and images with a numbering pattern that is fragile to deletion, replacement, insertion, combine, and copy attack. …”
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Neural network paradigm for classification of defects on PCB
Published 2003“…A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. …”
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