Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Living in the Skin That I Am: An Organizational Autoethnography of an Adult Educator's Plight to Survive the Stigma of Invisible and Episodic Disability in an Academy of Administritiva

Through the reflective lens of an adult educator with invisible and episodic disabilities, this paper has been written as an organizational autoethnography. Through a process of autoethnographical sensemaking, it is intended to illuminate important gaps in organizational theory. Feminist/relational care ethics, critical reflection, and transformative learning serve as the educational theories that comprise its framework. In telling my story, embodied writing and performance narrative are used to convey the felt existence of a body exposed through words—where my “abled” and “disabled” professional teaching and learning identities may be studied against the backdrop of organizational policies and procedures. Words used to describe unfamiliar experiences and situations shape meaning for which new meaning may emerge. At the conclusion of this paper, an alternative frame of reference—a view from the margins—may be offered to articulate authenticity in the expectancy of workplace equity for adult educators with disabilities. Taken collectively on a larger level, it is hoped that this research may provide a source of inspiration for systemic organizational change in adult learning environments.

Examining the roles of core and local temperature on forearm skin blood flow

The interaction between local and reflexive control of skin blood flow (SkBF) is unclear. This thesis isolated the roles of rectal (Tre) and local (Tloc) temperature on forearm SkBF regulation at normal and elevated body temperatures, and to investigate the interaction between local and reflexive SkBF control. While either normothermic (Tre ~37.0°C) or hyperthermic (∆Tre +1.1°C), SkBF was assessed on the dorsal aspect of each forearm in 10 participants while Tloc was manipulated in an A-B-A-B fashion between neutral (33.0°C) and hot (38.5°C). Finally, local heating to 44°C was performed to elicit maximal SkBF. Data are presented as a percentage of maximal cutaneous vascular conductance (CVC), calculated as laser-Doppler flux divided by mean arterial pressure. Tloc manipulations performed during normothermia had significantly greater effects on CVC than during hyperthermia. The decreased modification to SkBF from the Tloc changes during hyperthermia suggests that strong reflexive vasodilation attenuates local SkBF control mechanisms.

Registered Nurses' Perceptions about Facilitators and Barriers to Maternal Skin to Skin Contact in the Operating Room

Abstract It is recommended that all new mothers experience skin-to-skin contact (SSC) with their newborns immediately after birth. However, SSC is not commonly practiced after cesarean deliveries. To understand facilitators and barriers regarding SSC in the operating room (OR), a descriptive online and paper survey was conducted with 68 Registered Nurses from four hospitals in Ontario. The theory of planned behavior framed the study. Nurses had positive attitudes, and believed most health care team members supported SSC in the OR, but were uncertain about their control over the behavior. Nurses who had practiced the behavior in the past had more positive attitudinal and normative beliefs, and perceived some barriers as less difficult. Attitude and past behavior were the only significant multivariate predictors of intention to practice SSC in the future. Results suggest that shifting attitude and supporting more experience with the practice may increase nurses’ implementation of SSC in the OR.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.