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1
Improved random forest for feature selection in writer identification
Published 2015“…Writer Identification (WI) is a process to determine the writer of a given handwriting sample. …”
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Thesis -
2
A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis
Published 2014“…However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. …”
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Book Chapter -
3
A new swarm-based framework for handwritten authorship identification in forensic document analysis
Published 2014“…The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. …”
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Book Chapter -
4
Discretization of integrated moment invariants for writer identification
Published 2008“…Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. …”
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Conference or Workshop Item -
5
Invariants discretization for individuality representation in handwritten authorship
Published 2008“…Writer identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in. …”
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Conference or Workshop Item -
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…Adopting the medoid instead of the mean can enhance the efficiency. However, the complexity of the kmedoid based algorithms in general is more than the complexity of the k-means based algorithms. …”
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Thesis -
7
Objective and Subjective Evaluations of Adaptive Noise Cancellation Systems with Selectable Algorithms for Speech Intelligibility
Published 2018“…For subjective evaluation, listening tests were evaluated using the Mean Opinion Score (MOS) technique. …”
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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis
Published 2022“…The objective of this paper is to investigate how the parameters behave with a measurement criterion for feature selection, that is, the total error reduction ratio (TERR). The k-means and the Gaussian mixture distribution were adopted as the clustering algorithms and each algorithm was tested on four datasets with four distinct clustering evaluation criteria: Calinski-Harabasz, Davies-Bouldin, Gap and Silhouette. …”
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Book Chapter -
9
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
Published 2023“…The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. …”
Article -
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Performance evaluation of different data aggregation algorithms for different types of sensors in WSN based cluster
Published 2018“…The algorithms applied separately with (1) Mean (2) Median (3) Mode (4) Geometric mean (5) Harmonic mean. …”
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Thesis -
11
Image segmentation based on normalised cuts with clustering algorithm
Published 2013“…Evaluation of c -means and fuzzy c-means clustering algorithm with normalised cuts image segmentation on various kinds of images has been carried out. …”
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Thesis -
12
Comparison of performance and computational complexity of nonlinear active noise control algorithms
Published 2011“…The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. …”
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Article -
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Document clustering based on firefly algorithm
Published 2015“…Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. …”
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Article -
14
Segmentation of MRI brain images using statistical approaches
Published 2011“…Noise is one of the obstacles for brain MRI segmentation. The non-Local means (NL-means) algorithm is a state-of-the art neighbourhood-based noisereduction method which is time-consuming and its accuracy can be improved. …”
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Thesis -
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Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi
Published 2010“…The k-Means and the k-Modes algorithms are the fundamental algorithms for the centroid-based partitioning technique, whereas the k-Medoids is a representative object-based partitioning technique. …”
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Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. …”
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Final Year Project -
18
Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. …”
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Final Year Project -
19
Eye-state analysis using an interdependence and adaptive scale mean shift (IASMS) algorithm
Published 2014“…This paper presents a novel eye state analysis design aimed for human fatigue evaluation systems. The design is based on an interdependence and adaptive scale mean shift (IASMS) algorithm. …”
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