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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

An investigation on application of AI techniques on GIS

Kumar, Ravindra C 11 1900 (has links)
Application of AI techniques on GIS

DNN: A new neural network architecture of associative memory with pruning and order-sensitive learning and its applications

Rao, Sreenivasa M 12 1900 (has links)
A new neural network architecture of associative memory

Investigation of capacity and dynamics of hopfield model of neural network

Sharma, Ravindra January 1995 (has links)
Capacity and dynamics of hopfield model of neural network

Trace- An adaptive organizational policy for multi agent systems

Fatima, Shaheen S 12 1900 (has links)
Adaptive organizational policy for multi agent systems

Terms: Tree-Structured reason maintenance system

Kondayil, Shinymol Antony 04 1900 (has links)
Tree-Structured reason maintenance system

Dynamically reconfigurable logical topologies for fiber optic lan/mans

Reddy, Ravi Chandra Mohan E A 09 1900 (has links)
Topologies for fiber optic lan/mans

Database access in Telugu

Shankar, Ravi C 07 1900 (has links)
Database access in Telugu

Predicting Bankruptcy Risk: A Gaussian Process Classifciation Model

Seidu, Mohammed Nazib January 2015 (has links)
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 stimuli

Wickman, 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.

Automating model building in ligand-based predictive drug discovery using the Spark framework

Arvidsson, Staffan January 2015 (has links)
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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