Bayesian Classification

The two objectives of this study are to compare the performances of different data fusion techniques for the enhancement of urban features and subsequently to improve urban land cover types classification using a refined Bayesian classification. For the data fusion, wavelet-based fusion, Brovey transform, Elhers fusion and principal component analysis are used and the results are compared. The refined Bayesian

This paper describes a Bayesian technique for unsupervised classification of data and its computer implementation, AutoClass. Given real valued or discrete data, AutoClass determines the most probable number of classes present in the data, the most probable descriptions of those ...

Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the

Quantum-dot infrared photodetectors (QDIPs) are emerging as a promising technology for midwave-and longwave-infrared remote sensing and spectral imaging. One of the key advantages that QDIPs offer is their bias-dependent spectral response, which is brought about by the ...

Traditionally the enormous quantities of medical data are utilized only for clinical and short term use. MediQuery puts to use this vast storage of information so that diagnosis based on this historical data can be made. There are systems to predict diseases of the heart, brain and lungs based on past data collected from the patients. We focus on computing

Two artificial neural networks have been trained to recognise phonemes in continuous speech: multi-layer perceptron (MLP) nets and probabilistic neural networks (PNN). The speech material was recorded by one male Swedish speaker and the sentences were phonetically labelled. Fifty sentences were used for training and another fifty were used for testing. Both networks had a single hidden layer and 38

In almost all languages words usually have multiple senses or meanings. WSD (Word Sense Disambiguation) is a task of recognizing the correc t sense of a word in a particular context. Identifyin g correct sense of an ambiguous word becomes very vital when a language is needed to be translated in to another language or information is needed to be

Mortality rate by the brain tumor was very high some years before. But now this rate is decreased in the recent years due to the earlier diagnosis and proper treatment. Chances of the long survival of the patient can be increased by the accurate brain tumor diagnosis. For this regard we are proposing more accurate and efficient system for brain tumor diagnosis and brain tumor region extraction. Proposed system first diagnosis the tumor from the brain MR images using naïve bayes classification. After diagnosis brain tumor region is extracted using K-means clustering and boundary detection techniques. We are achieving diagnosis accuracy more than 99%. Qualitative results show that accurate tumor region is extracted by the proposed system. The proposed technique is tested against the datasets of different patients received from Holy Family hospital and Abrar MRI&CT Scan center Rawalpindi.

ABSTRACT In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.

Mortality rate by the brain tumor was very high some years before. But now this rate is decreased in the recent years due to the earlier diagnosis and proper treatment. Chances of the long survival of the patient can be increased by the accurate brain tumor diagnosis. For this regard we are proposing more accurate and efficient system for brain tumor diagnosis and brain tumor region extraction. Proposed system first diagnosis the tumor from the brain MR images using naïve bayes classification. After diagnosis brain tumor region is extracted using K-means clustering and boundary detection techniques. We are achieving diagnosis accuracy more than 99%. Qualitative results show that accurate tumor region is extracted by the proposed system. The proposed technique is tested against the datasets of different patients received from Holy Family hospital and Abrar MRI&CT Scan center Rawalpindi.