Computing and information technology has significantly increased the capabilities to collect, store, and analyze freeway traffic surveillance data. The most common forms of such data are collected using the underground loop detectors. In the recent past the potential of using these data for identification of crash-prone conditions has been explored. In the present work, application of probabili...

One of the main objectives of wireless networking is to provide mobile users with a robust connection to different networks so that they can move freely between heterogeneous networks while running their computing applications with no interruption. Horizontal handoff, or generally speaking handoff, is a process which maintains a mobile user’s active connection as it moves within a wireless netw...

In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is u...

This paper proposes an automatic computer aided diagnostic system (CAD) for detection of liver diseases like hepatoma and hemangioma from Abdominal Computed tomography (CT) images. Liver and Lesion is segmented using gray level methods and clustering. Histogram analyzer is used to fix the threshold and morphological operation is used for post processing. Rules are applied to remove the obstacle...

Neural networks have been used prominently in several machine learning and statistics applications. In general, the underlying optimization of neural networks is non-convex which makes their performance analysis challenging. In this paper, we take a novel approach to this problem by asking whether one can constrain neural network weights to make its optimization landscape have good theoretical ...

In this paper, a system for automatic classification of musical instrument sounds is introduced. As features mel-frequency cepstral coefficients and as classifiers probabilistic neural networks are used. The experimental dataset included 4548 solo tones from 19 instruments of MIS database (The University of Iowa Musical Instrument Samples). Experiments for different system structures (hierarchi...

The problem of cloud data classification from satellite imagery using neural networks is considered in this paper. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level coo...

Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.

For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization training method based on GA combined with SA is proposed in this paper. Through building generalized Fréchet distance to measure similarity between time-varying function samples, the learning problem of radial basis centre functions and connection weights is converted into the training on correspondi...