Unsupervised mining of under-resourced speech corpora for tone features classification

Ekpenyong, Moses E.; Inyang, Udoinyang G.

doi:10.1109/ijcnn.2016.7727494

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…The reference dataset is generated using Monte Carlo simulations of the sampling process. The silhouette, elbow and gap-statistics methods rely on k-mean algorithm 52 . In this paper, the k-means algorithm is implemented in R script consisting of R functions for the silhouette, elbow, and gapstatistics implementation.…”

Section: Optimal Natural Clusters Selectionmentioning

confidence: 99%

A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction

Ekpenyong

Edoho

Inyang

et al. 2021

Preprint

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Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speaks for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining from enriched genome datasets and output classification targets, helped intelligent prediction of emerging or new viral sub-strains. Classification results outsmarted state-of-the-art methods and sustained an increase in sub-strains within the various continents with nucleotide mutations dynamically varying between individuals in close association with the virus adaptability to its host/environment. They also offer explanations for the growing concerns and next wave(s) of the virus. Defuzzifying confusable pattern clusters for comparative performance with the proposed cognitive solution is a possible future research direction of this paper.

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Section: Optimal Natural Clusters Selectionmentioning

confidence: 99%

A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction

Ekpenyong

Edoho

Inyang

et al. 2021

Preprint

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“…The objective of CA is to identify and describe classes within a dataset based on a well specified distance measure, so that the instances in the same class are similar to each other, while different to members of other classes (Maimon & Rokach, 2010). In some datasets, the actual number of clusters are known through the existence of natural divisions or the actual number of cluster are known a priori (for example, the students AP dataset used for this work); while others do not contain information on the natural divisions, or the natural divisions are unknown (Ekpenyong and Inyang, 2016). Cluster validation is an analytic process of assessing the quality of clustering solutions by finding the number of clusters that best satisfy the structure of the dataset without any priori class knowledge.…”

Section: Cluster Discovery and Validity Analysismentioning

confidence: 99%

“…Liu et al (2010) and Sivogolovko and Novikov (2012) identified that the silhouette performs well on a variety of data types irrespective of structural variations, noise and skewed distributions, and performs very well in partitioning and density-based approaches. This paper adopts silhouette criterion in the assessment of clusters by comparing pairs of objects between and within cluster distances (Liu et al, 2010, Liu and Sethuraman, 2013, Ekpenyong and Inyang, 2016. The optimum cluster number and cluster validation was based on experiments driven by k-means algorithm with SCV as the distance measure.…”

Section: Cluster Discovery and Validity Analysismentioning

confidence: 99%

Unsupervised Characterization and Visualization of Students’ Academic Performance Features

Inyang¹,

Umoh²,

Nnaemeka³

et al. 2019

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The large nature of students’ dataset has made it difficult to find patterns associated with students’ academic performance (AP) using conventional methods. This has increased the rate of drop-outs, graduands with weak class of degree (CoD) and students that spend more than the minimum stipulated duration of studies. It is necessary to determine students’ AP using educational data mining (EDM) tools in order to know students who are likely to perform poorly at an early stage of their studies. This paper explores k-means and self-organizing map (SOM) in mining pieces of knowledge relating to the natural number of clusters in students’ dataset and the association of the input features using selected demographic, pre-admission and first year performance. Matlab 2015a was the programming environment and the dataset consists of nine sets of computer science graduands. Cluster validity assessment with k-means discovered four (4) clusters with correlation metric yielding the highest mean silhouette value of 0.5912.  SOM provided an hexagonal grid visual of feature component planes and scatter plots of each significant input attribute. The result shows that the significant attributes were highly correlated with each other except entry mode (EM), indicating that the impact of EM on CoD varies with students irrespective of mode of admission. Also, four distinct clusters were also discovered in the dataset by SOM —7.7% belonging to cluster 1 (first class), and 25% for cluster 2 (2nd class Upper) while Clusters 3 and 4 had 35% proportion each. This validates the results of k-means and further confirms the importance of early detection of students’ AP and confirms the effectiveness of SOM as a cluster validity tool. As further work, the labels from SOM will be associated with records in the dataset for association rule mining, supervised learning and prediction of students’ AP.

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“…Machine learning (ML) methodologies have been demonstrated as promising tools for effective and efficient software defect detection and prediction (Iqbal, et al, 2019). These approaches span, supervised (Iqbal, et al, 2019), unsupervised (Ekpenyong and Inyang 2016;Inyang, et al, 2019, Inyang andInyang, 2011), and hybridized systems (Inyang, and Akinyokun, 2014). Both supervised and unsupervised learning approaches acquire or enhance pieces of knowledge through the training process.…”

Section: Introductionmentioning

confidence: 99%

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

Inyang¹,

Adeoye²,

Udo³

et al. 2022

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In today's society, almost all human endeavours depend on software products. Lack of quality software is one of the software industry's most important problems. Hence, it would be beneficial to access the quality of software to improve and enhance software products while increasing customer satisfaction. This paper assesses software product quality using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) software quality (SQ) framework was adopted in this work. Dimension reduction of the product metric category dataset and the entire PM dataset was conducted using linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian and coarse gaussian) were used to model each classifier. The combinations of the results from the multiple SVMs used AdaBoost, bagging, and random subspace ensemble methods for the assessment of SQ. All three ensemble learning methods performed better than the individual SVM, however, the bagging stood out with an accuracy of 93.0%. Hence, it was adopted in the fusion of the SVM results and classification of SQ into classes. Results from the confusion matrix and receivers’ operating characteristics were greater than 97.99% and confirm significant improvements with an ensemble of homogenous classifiers based on SVM.

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Unsupervised mining of under-resourced speech corpora for tone features classification

Cited by 7 publications

References 20 publications

A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction

A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction

Unsupervised Characterization and Visualization of Students’ Academic Performance Features

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

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