Kumar, Ravindra C
Application of AI techniques on GIS
DNN: A new neural network architecture of associative memory with pruning and order-sensitive learning and its applicationsRao, Sreenivasa M 12 1900 (has links)
A new neural network architecture of associative memory
Capacity and dynamics of hopfield model of neural network
Fatima, Shaheen S
Adaptive organizational policy for multi agent systems
Kondayil, Shinymol Antony
Tree-Structured reason maintenance system
Reddy, Ravi Chandra Mohan E A
Topologies for fiber optic lan/mans
Shankar, Ravi C
Database access in Telugu
Seidu, Mohammed Nazib
This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the context of bankruptcy prediction. The posterior distributions of the GPs are non-Gaussian, and we investigate the effectiveness of the Laplace approximation and the expectation propagation approximation across several different kernels for the Gaussian process. The approximate methods are compared to the gold standard of Markov Chain Monte Carlo (MCMC) sampling from the posterior. The dataset is an unbalanced panel consisting of 21846 yearly observations for about 2000 corporate firms in Sweden recorded between 1991−2008. We used 5000 observations to train the models and the rest for evaluating the predictions. We find that the choice of covariance kernel affects the GP model’s performance and we find support for the squared exponential covariance function (SEXP) as an optimal kernel. The empirical evidence suggests that a multivariate Gaussian processes classifier with squared exponential kernel can effectively improve bankruptcy risk prediction with high accuracy (90.19 percent) compared to the linear logistic model (83.25 percent).
TECHNOLOGY MEETS THE EYE : Utveckling av system för att jämföra eye tracking data med visuellt stimuliWickman, Erik, Mårtenson, Adam, Rivera Öman, Marcus January 2015 (has links)
The purpose of the project was to make a system that could extract data from a mobile eye tracker and make it comparable with data from visual stimuli. The produced system was programmed in Java and provided all the necessary parts that were required to achieve the purpose. This provides a foundation for further research to determine whether the eye tracker is sufficiently accurate to diagnose Parkinson’s disease.
Automation of model building enables new predictive models to be generated in a faster, easier and more straightforward way once new data is available to predict on. Automation can also reduce the demand for tedious bookkeeping that is generally needed in manual workflows (e.g. intermediate files needed to be passed between steps in a workflow). The applicability of the Spark framework related to the creation of pipelines for predictive drug discovery was here evaluated and resulted in the implementation of two pipelines that serves as a proof of concept. Spark is considered to provide good means of creating pipelines for pharmaceutical purposes and its high level approach to distributed computing reduces the effort put on the developer compared to a regular HPC implementation.
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