Supervised remote sensing image classification: An example of a one-against-one multi-class polynomial kernel based support vector machine

Software like ILWIS and GRASS GIS can be employed for remote sensing image processing and geographic information systems applications. The modules of the aforementioned image processing software are based on conventional multi-class classifiers/algorithms such as maximumlikelihood classifier. These conventional multi-class classifiers/algorithms are usually written in programming languages such as C, C++, and python. The objective of this research is to experiment the use of a binary classifier/algorithm for multi-class remote sensing task, implemented in MATLAB. MATLAB is a programming language just like C, C++, and python. In this research, the support vector machine binary classifier/algorithm based on a one-against-one approach implemented in MATLAB isapplied to remote sensing multi-class problem. Both simulated and empirical satellite remote sensing data are used to train and test a one-against-one support vector machine classifier. For the purpose ofvalidating the experiment, the resulting classified satellite image is compared with the ground truth data. The polynomial kernel function is used for the modelling. In the simulated application, 25 pixels are usedfor the experiment, out of which 6 pixels are used for training while 19 pixels are used for testing. Out of the 19 tested pixels 18 pixels are correctly classified while only 1 pixel is left unclassified. In the empirical application, 256 and 7182 pixels are unclassified and misclassified respectively out of a total of 62500 pixels; and the computed overall accuracy of the experiment is 88.1%. The satisfactory result of the experiment indicates substantial agreement between the classification result and the reference data

Uncertainty and Congestion Elimination in 4G Network Call Admission Control using Interval Type-2 Intuitionistic Fuzzy Logic

The management and control of the global growth and complex nature of wireless Fourth Generation (4G) Networks elicits the need for Call Admission Control (CAC). However, CAC faces the challenge of network congestion, thereby deteriorating the network Quality of Service (QoS) due to inherent imprecision and uncertainties in the QoS data which leads to difficulties in measuring some objective and constraints of QoS using crisp values. Previous researches have shown the strength of Interval Type-2 Fuzzy Logic System (IT2FLS) in coping adequately with linguistic uncertainties. Intuitionistic fuzzy sets (IFSs) have indicated their ability to further reduce uncertainty by handling conflicting evaluation involving membership (M), nonmembership (NM) and hesitation. This paper applies the Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) in solving CAC problem in order to achieve a better QoS in 4G Networks

Who knows best? A Q methodology study to explore perspectives of professional stakeholders and community participants on health in low-income communities

Abstract Background Health inequalities in the UK have proved to be stubborn, and health gaps between best and worst-off are widening. While there is growing understanding of how the main causes of poor health are perceived among different stakeholders, similar insight is lacking regarding what solutions should be prioritised. Furthermore, we do not know the relationship between perceived causes and solutions to health inequalities, whether there is agreement between professional stakeholders and people living in low-income communities or agreement within these groups. Methods Q methodology was used to identify and describe the shared perspectives (‘subjectivities’) that exist on i) why health is worse in low-income communities (‘Causes’) and ii) the ways that health could be improved in these same communities (‘Solutions’). Purposively selected individuals (n = 53) from low-income communities (n = 25) and professional stakeholder groups (n = 28) ranked ordered sets of statements – 34 ‘Causes’ and 39 ‘Solutions’ – onto quasi-normal shaped grids according to their point of view. Factor analysis was used to identify shared points of view. ‘Causes’ and ‘Solutions’ were analysed independently, before examining correlations between perspectives on causes and perspectives on solutions. Results Analysis produced three factor solutions for both the ‘Causes’ and ‘Solutions’. Broadly summarised these accounts for ‘Causes’ are: i) ‘Unfair Society’, ii) ‘Dependent, workless and lazy’, iii) ‘Intergenerational hardships’ and for ‘Solutions’: i) ‘Empower communities’, ii) ‘Paternalism’, iii) ‘Redistribution’. No professionals defined (i.e. had a significant association with one factor only) the ‘Causes’ factor ‘Dependent, workless and lazy’ and the ‘Solutions’ factor ‘Paternalism’. No community participants defined the ‘Solutions’ factor ‘Redistribution’. The direction of correlations between the two sets of factor solutions – ‘Causes’ and ‘Solutions’ – appear to be intuitive, given the accounts identified. Conclusions Despite the plurality of views there was broad agreement across accounts about issues relating to money. This is important as it points a way forward for tackling health inequalities, highlighting areas for policy and future research to focus on

Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-Site Integrative Data Analysis by the Autism Sensory Research Consortium

BACKGROUND: Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these supra-modal traits in the autistic population. METHODS: Leveraging a combined sample of 3868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a general response pattern factor for each supra-modal construct and determine the added value of modality-specific response pattern scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO, and SEEK (sub)constructs. RESULTS: All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses supported the validity of a supra-modal HYPER construct (ω LIMITATIONS: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to caregiver report of observable behaviors and excluded multisensory items that reflect many real-world sensory experiences. CONCLUSION: Of the three sensory response patterns, only HYPER demonstrated sufficient evidence for valid interpretation at the supra-modal level, whereas supra-modal HYPO/SEEK constructs demonstrated substantial psychometric limitations. For clinicians and researchers seeking to characterize sensory reactivity in autism, modality-specific response pattern scores may represent viable alternatives that overcome many of these limitations

Mineral and proximate composition of selected forages fed to West African dwarf bucks in Obio Akpa

The nutritive value of leaves from six forages was carried out. The forages were Andropogon tectorum, Panicum maximum, Aspilia aafricana, Gmelina aborea, Alchornea cordifolia and Bambusa vulgaris, and were collected from Obio-Akpa in Akwa Ibom State. The forages were analysed for proximate composition, mineral/vitamin concentrations and anti-nutritive components. Results showed no significant (p>0.05) differences in the dry matter content which ranged from 86.52 to 98.36%. A. africana and G. arborea recorded protein contents which was higher than the crude protein (CP) of other forages analysed. A range of 1.94 to 5.24% and 1.28 to 5.84% were recorded for ether extract (EE) and crude fibre (CF) values for the six forages. The values reported for minerals showed that B. vulgaris had the lowest value of calcium (0.45%) while A. tectorum was low in magnesium (Mg) and potassium (0.57 and 0.22%). Highest content of vitamin A and B12 was recorded in A. tectorum (1.17 and 2.11ìg/100g), respectively. The values reported for anti-nutritive factors ranged from 0.98 to 2.23 for tannins, 1.94 to 3.76 for Saponins, 0.01 to 1.23 for oxalates, 0.22 to 0.71 for hydrogen cyanide (HCN) and 1.05 to 1.55mg/g for phytates. The results showed that the forages studied have good nutrient contents and safe levels of anti-nutritional factors, thus they may be used as feed resources to enhance the production of ruminants.&nbsp