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Empirical investigation of consensus clustering for large ECG data sets

- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: This article investigates a novel machine learning approach applying consensus clustering in conjunction with classification for the data mining of very large and highly dimensional ECG data sets. To obtain robust and stable clusterings, consensus functions can be applied for clustering ensembles combining a multitude of independent initial clusterings. Direct applications of consensus functions to highly dimensional ECG data sets remain computationally expensive and impracticable. We introduce a multistage scheme including various procedures for dimensionality reduction, consensus clustering of randomized samples, followed by the use of a fast supervised classification algorithm. Applying the Hybrid Bipartite Graph Formulation combined with rank ordering and SMO we obtained an area under the receiver operating curve of 0.987. The performance of the classification algorithm at the final stage is crucial for the effectiveness of this technique. It can be regarded as an indication of the reliability, quality and stability of the combined consensus clustering. © 2012 IEEE.

Effect of physicochemical properties on freezing suitability of Lotus (Nelumbo nucifera) root

- Tu, Jing, Zhang, Min, Xu, Baoguo, Liu, Amy

  • Authors: Tu, Jing , Zhang, Min , Xu, Baoguo , Liu, Amy
  • Date: 2015
  • Type: Text , Journal article
  • Relation: International Journal of Refrigeration Vol. 50, no. (2015), p. 1-9
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  • Description: The initial physicochemical properties of different lotus roots were studied with the aim to evaluate their influence on freezing suitability. Six physicochemical properties indicators (starch content, whiteness index, maximum diameter, protein amount, titratable acidity and vitamin C) of lotus root were determined by principal component analysis. It was observed from the experimental results that the initial physicochemical properties of lotus root can directly affect the freezing suitability in terms of initial freezing temperature, transition phase time, total freezing time, relative firmness and drip loss. Correlation analysis suggested that relative firmness increases in proportion to starch content, and initial freezing temperature and drip loss are inversely proportional to the starch content. Compared to immersion freezing, power ultrasound can significantly improve the freezing rate and relative firmness but decrease drip loss. These results suggested that high quality frozen lotus root can be achieved via appropriately selecting high starch content lotus root and using ultrasound-assisted immersion freezing.

A new scale for disaster nursing core competencies : Development and psychometric testing

- Al Thobaity, Abdulellah, Williams, Brett, Plummer, Virginia

  • Authors: Al Thobaity, Abdulellah , Williams, Brett , Plummer, Virginia
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Australasian Emergency Nursing Journal Vol. 19, no. 1 (2016), p. 11-19
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  • Description: Background: All nurses must have core competencies in preparing for, responding to and recovering from a disaster. In the Kingdom of Saudi Arabia (KSA), as in many other countries, disaster nursing core competencies are not fully understood and lack reliable, validated tools. Thus, it is imperative to develop a scale for exploring disaster nursing core competencies, roles and barriers in the KSA. Objectives: This study's objective is to develop a valid, reliable scale that identifies and explores core competencies of disaster nursing, nurses' roles in disaster management and barriers to developing disaster nursing in the KSA. Methods: This study developed a new scale testing its validity and reliability. A principal component analysis (PCA) was used to develop and test psychometric properties of the new scale. The PCA used a purposive sample of nurses from emergency departments in two hospitals in the KSA. Participants rated 93 paper-based, self-report questionnaire items from 1 to 10 on a Likert scale. PCA using Varimax rotation was conducted to explore factors emerging from responses. Findings: The study's participants were 132 nurses (66% response rate). PCA of the 93 questionnaire items revealed 49 redundant items (which were deleted) and 3 factors with eigenvalues of >1. The remaining 44 items accounted for 77.3% of the total variance. The overall Cronbach's alpha was 0.96 for all factors: 0.98 for Factor 1, 0.92 for Factor 2 and 0.86 for Factor 3. Conclusions: This study provided a validated, reliable scale for exploring nurses' core competencies, nurses' roles and barriers to developing disaster nursing in the KSA. The new scale has many implications, such as for improving education, planning and curricula. © 2015.

Robust building roof segmentation using airborne point cloud data

- Gilani, Syed, Awrangjeb, Mohammad, Lu, Guojun

  • Authors: Gilani, Syed , Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2016
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 23rd IEEE International Conference on Image Processing, ICIP 2016; Phoenix, United States; 25th-28th September 2016; published in Proceedings - International Conferenec on Image Processing, ICIP Vol. 2016-August, p. 859-863
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  • Description: Approximation of the geometric features is an essential step in point cloud segmentation and surface reconstruction. Often, the planar surfaces are estimated using principal component analysis (PCA), which is sensitive to noise and smooths the sharp features. Hence, the segmentation results into unreliable reconstructed surfaces. This article presents a point cloud segmentation method for building detection and roof plane extraction. It uses PCA for saliency feature estimation including surface curvature and point normal. However, the point normals around the anisotropic surfaces are approximated using a consistent isotropic sub-neighbourhood by Low-Rank Subspace with prior Knowledge (LRSCPK). The developed segmentation technique is tested using two real-world samples and two benchmark datasets. Per-object and per-area completeness and correctness results indicate the robustness of the approach and the quality of the reconstructed surfaces and extracted buildings. © 2016 IEEE.
  • Description: Proceedings - International Conference on Image Processing, ICIP

Characterization of the gut microbiota of Papua New Guineans using reverse transcription quantitative PCR

- Greenhill, Andrew, Tsuji, Hirokazu, Ogata, Kiyohito, Natsuhara, Kazumi, Morita, Ayako, Soli, Kevin, Larkins, Jo-Ann, Tadokoro, Kiyoshi, Odani, Shingo, Baba, Jun, Naito, Yuichi, Tomitsuka, Eriko, Nomoto, Kriko, Siba, Peter, Horwood, Paul, Umezaki, Masahiro

  • Authors: Greenhill, Andrew , Tsuji, Hirokazu , Ogata, Kiyohito , Natsuhara, Kazumi , Morita, Ayako , Soli, Kevin , Larkins, Jo-Ann , Tadokoro, Kiyoshi , Odani, Shingo , Baba, Jun , Naito, Yuichi , Tomitsuka, Eriko , Nomoto, Kriko , Siba, Peter , Horwood, Paul , Umezaki, Masahiro
  • Date: 2015
  • Type: Text , Journal article
  • Relation: PLoS ONE Vol. 10, no. 2 (2015), p. 1-15
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  • Description: There has been considerable interest in composition of gut microbiota in recent years, leading to a better understanding of the role the gut microbiota plays in health and disease. Most studies have been limited in their geographical and socioeconomic diversity to high-income settings, and have been conducted using small sample sizes. To date, few analyses have been conducted in low-income settings, where a better understanding of the gut microbiome could lead to the greatest return in terms of health benefits. Here, we have used quantitative real-time polymerase chain reaction targeting dominant and sub-dominant groups of microorganisms associated with human gut microbiome in 115 people living a subsistence lifestyle in rural areas of Papua New Guinea. Quantification of Clostridium coccoides group, C. leptum subgroup, C. perfringens, Bacteroides fragilis group, Bifidobacterium, Atopobium cluster, Prevotella, Enterobacteriaceae, Enterococcus, Staphylococcus, and Lactobacillus spp. was conducted. Principle coordinates analysis (PCoA) revealed two dimensions with Prevotella, clostridia, Atopobium, Enterobacteriaceae, Enterococcus and Staphylococcus grouping in one dimension, while B. fragilis, Bifidobacterium and Lactobacillus grouping in the second dimension. Highland people had higher numbers of most groups of bacteria detected, and this is likely a key factor for the differences revealed by PCoA between highland and lowland study participants. Age and sex were not major determinants in microbial population composition. The study demonstrates a gut microbial composition with some similarities to those observed in other low-income settings where traditional diets are consumed, which have previously been suggested to favor energy extraction from a carbohydrate rich diet. © 2015 PLOS ONE.

Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models

- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh

  • Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Sensors (Basel, Switzerland) Vol. 22, no. 20 (2022), p.
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  • Description: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band

- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh

  • Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Environments Vol. 10, no. 5 (2023), p. 77
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  • Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
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