CLASSIFICATION OF TODDLER’S NUTRITIONAL STATUS USING THE ROUGH SET ALGORITHM

The health and nutrition of children at the age of five are very important aspects in the children’s growth and development. An assessment of the nutritional status of toddlers that is commonly used is anthropometry. This study aims to obtain the decision rules used to classify toddlers into nutritional status groups using the rough set algorithm and determine the level of classification accuracy of the resulting decision rules. The index used in this study is the weight-for-age index. Attributes used in this study were the mother’s education level, mother’s level of knowledge, the status of exclusive breastfeeding, history of illness in the last month, and nutritional status of toddlers. The results of the analysis show that there are 21 decision rules. In this study, the resulting decision rules experience inconsistencies. The selection of decision rules that experience inconsistencies is based on each decision rule’s highest strength value.  The rough set algorithm can be used for the classification process with an accuracy rate of 86.36%

FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS

Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen

A Simulation of Land Use / Cover Change for Urbanization on Chennai Metropolitan area, India

Remote sensing and GIS technologies are very much useful for finding the Land Use/Cover maps. This is the paper which deals with the Land Use/Cover Change (LUCC) especially to urbanization in Chennai metropolitan area, India for past two decades till present. Chennai is the fourth largest metropolitan city in India with area of 1189 km2 with 4.68 million of population, which is developing rapidly into urban in past few decades. There is heavy need of urban planning for future in Chennai. This research will be a support for urban planning of the future. The Land satellite data for three decades (1989, 2000 and 2012) and Digital Elevation Model (DEM) for present were collected with 30 meter resolution. Preprocessing of all images was completed. Image classification for mapping LUCC was performed by supervised classification through the maximum likelihood classification for four classes: Water, Rough land, Crop land and Urban. An accuracy assessment has been checked to find the accuracy of the Classification and the overall accuracy is about 87%. Transition probability matrices were calculated for all three time points and compared with each other (1989 with 2000, 2000 with 2012). The result shows that the increase in Urban and decrease in Rough land. Slope map has been created from DEM. Analyses of neighborhood effects were done to find the probability of land changes due to existing urban cells, which is calculated for each cells surrounded by its three neighborhood cells. Analyses of slope effects for urbanization was done by comparing the slope and the possibilities of change from Rough land and Crop land to Urban. A simple model structure for simulation was created using VBA and GIS. The model applies the neighborhood effects which are similar to Cellular Automata but in this model it is modified by slope effects. Using the simulation urban map was predicted for future trends. These predicted urban maps will provide critical input to resource management and planning support applications, and have substantial social and economic benefit for metropolitan planning and development

Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters

Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

New South Wales Vegetation classification and Assessment: Part 3, plant communities of the NSW Brigalow Belt South, Nandewar and west New England Bioregions and update of NSW Western Plains and South-western Slopes plant communities, Version 3 of the NSWVCA database

This fourth paper in the NSW Vegetation Classification and Assessment series covers the Brigalow Belt South-/1(BBS) and Nandewar (NAN) Bioregions and the western half of the New England Bioregion (NET), an area of 9.3 million hectares being 11.6% of NSW. It completes the NSWVCA coverage for the Border Rivers-Gwydir and Namoi CMA areas and records plant communities in the Central West and Hunter–Central Rivers CMA areas. In total, 585 plant communities are now classified in the NSWVCA covering 11.5 of the 18 Bioregions in NSW (78% of the State). Of these 226 communities are in the NSW Western Plains and 416 are in the NSW Western Slopes. 315 plant communities are classified in the BBS, NAN and west-NET Bioregions including 267 new descriptions since Version 2 was published in 2008. Descriptions of the 315 communities are provided in a 919 page report on the DVD accompanying this paper along with updated reports on other inland NSW bioregions and nine Catchment Management Authority areas fully or partly classified in the NSWVCA to date. A read-only version of Version 3 of the NSWVCA database is on the DVD for use on personal computers. A feature of the BBS and NAN Bioregions is the array of ironbark and bloodwood Eucalyptusdominated shrubby woodlands on sandstone and acid volcanic substrates extending from Dubbo to Queensland. This includes iconic natural areas such as Warrumbungle and Mount Kaputar National Parks and the 500,000 ha Pilliga Scrub forests. Large expanses of basalt-derived soils support grassy box woodland and native grasslands including those on the Liverpool Plains; near Moree; and around Inverell, most of which are cleared and threatened. Wetlands occur on sodic soils near Yetman and in large clay gilgais in the Pilliga region. Sedgelands are rare but occupy impeded creeks. Aeolian lunettes occur at Narran Lake and near Gilgandra. Areas of deep sand contain Allocasuarina, eucalypt mallee and Melaleuca uncinata heath. Tall grassy or ferny open forests occur on mountain ranges above 1000m elevation in the New England Bioregion and on the Liverpool Range while grassy box woodlands occupy lower elevations with lower rainfall and higher temperatures. The vegetation classification and assessment is based on over 100 published and unpublished vegetation surveys and map unit descriptions, expert advice, extra plot sampling and data analysis and over 25 000 km of road traverse with field checking at 805 sites. Key sources of data included floristic analyses produced in western regional forest assessments in the BBS and NAN Bioregions, floristic analyses in over 60 surveys of conservation reserves and analysis of plot data in the western NET Bioregion and covering parts of the Namoi and Border Rivers- Gwydir CMA areas. Approximately 60% of the woody native vegetation in the study area has been cleared resulting in large areas of “derived” native grasslands. As of June 2010, 7% of the area was in 136 protected areas and 127 of the 315 plant communities were assessed to be adequately protected in reserves. Using the NSWVCA database threat criteria, 15 plant communities were assessed as being Critically Endangered, 59 Endangered, 60 Vulnerable, 99 Near Threatened and 82 Least Concern. 61 of these communities are assessed as part of NSW or Commonwealth-listed Threatened Ecological Communities. Current threats include expanding dryland and irrigated cropping on alluvial plains, floodplains and gently undulating topography at lower elevations; over-grazing of steep hills; altered water tables and flooding regimes; localized mining; and the spread of exotic species, notably Coolatai Grass (Hyparrhenia hirta)