Image Content Analysis Using Neural Networks and Genetic Algorithms

The analysis of digital images for content discovery is a process of identifying and classifying patterns and sub-images that can lead to recognizing contents of the processed image. The image content analysis system presented in this paper aims to provide the machine with the capability to simulate in some sense, a similar capability in human beings. The developed system consists of three levels. In the low level, image clustering is performed to extract features of the input data and to reduce dimensionality of the feature space. Classification of the scene images are carried out using a single layer neural network, trained through Kohonen's self-organizing algorithm, with conscience function, to produce a set of equi-probable weights vector. The intermediate level consists of two parts. In the first part an image is partitioned into homogeneous regions with respect to the connectivity property between pixels, which is an important concept used in establishing boundaries of objects and component regions in an image. For each component, connected components can be determined by a process of component labeling. In the second part, feature extraction process is performed to capture significant properties of objects present in the image. In the high level; extracted features and relations of each region in the image are matched against the stored object models using the genetic algorithm approach. The implemented system is used in the analysis and recognition of colored images that represent natural scenes. Keywords: genetic algorithms, neural networks, image segmentation, clustering, image content analysis

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time

Psychiatric morbidity in Northern Jordan: a ten-year review

Objective: To study the psychiatric morbidity in the northern part of Jordan and to determine the frequency distribution of various psychiatric disorders, for planning services. Methods: All records of 2,335 psychiatric patients attending the only psychiatric clinic in Northern part of Jordan during a ten-year period from 1984 to 1993 were extensively reviewed and subjected to computerized analysis. Diagnosis was made as per ICD-9. Results: Out of the 2335 patients, who attended the clinic, 55% were males and 45% were females. Those in the age group 25–44 recorded the maximum attendance. Among the male attendees of the clinic, schizophrenia was the commonest diagnosis (19.9%), while among females; affective disorders were the commonest (15.9%). Conclusion: Schizophrenia was found to be the commonest diagnosis in general among attendance of the clinic for the ten-year research period, while anxiety disorders were the commonest diagnosis among attendance of the clinic for the year 1993.