By adapting gear changes and cruise control of a Heavy Duty Vehicle (HDV) to road inclination, fuel and time savings can be achieved. In this thesis is presented a novel method of predicting the upcoming road topography, by constructing a geographical and topographical self-learning map from which a route prediction is made. The system is designed to simultaneously produce the desired road grad...

-Many time series forecasting problems require the estimation of possibly inaccurate, but longterm, trends, rather than accurate short-term prediction. In this paper, a double use of the Self-Organizing Map algorithm makes it possible to build a model for longterm prediction, which is proven to be stable. The method uses the information on the structure of the series when available, by predicti...

Recurrent Self Organizing Map RSOM is studied in three di erent time series prediction cases RSOM is used to cluster the series into local data sets for which corresponding local linear models are estimated RSOM includes recurrent di erence vector in each unit which allows storing con text from the past input vectors Multilayer perceptron MLP network and autoregressive AR model are used to comp...

Label ranking is a complex prediction task where the goal is to map instances to a total order over a finite set of predefined labels. An interesting aspect of this problem is that it subsumes several supervised learning problems such as multiclass prediction, multilabel classification and hierarchical classification. Unsurpisingly, there exists a plethora of label ranking algorithms in the lit...

The Kohonen self-organization map is usually considered as a classification or clustering tool, with only a few applications in time series prediction. In this paper, a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting ...

An interscale predictive wavelet coding (IPWC) scheme is proposed in this paper. The image is encoded top-down, from the coarsest scale to the finest scale. At each scale, the current scale coefficients are predicted from the coded coefficients at the coarser scale and the prediction residue is encoded. The interscale prediction is based on MAP estimation with Huber Markov random field statisti...

We explore a previously undescribed regularity in language: a bias for longer words to map to relatively more complex meanings. While theories of communication make the more general prediction that longer utterances should be associated with more complex meanings, this prediction has not yet been explored at the level of words. Through norming and experimental studies, we find evidence in suppo...

Methane gas emission, accumulation, and explosion are the most important risk factors in underground coal mines. Hence, having a knowledge of methane gas emission potential in underground coal mines is of crucial importance in preventing the explosion risk, loss of life, and property, and providing miners' safety. The purpose of this work is to provide the prediction maps for the C1, C2, and B2...

This thesis explores the application of a deep learning approach for the prediction of media interestingness. Two different models are investigated, one for the prediction of image and one for the prediction of video interestingness. For the prediction of image interestingness, the ResNet50 network is fine-tuned to obtain best results. First, some layers are added. Next, the model is trained an...

A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness r...