A comparison between neural-network forecasting techniques--case study: river flow forecasting.

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of this paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize the time series as a benchmark to compare between several neural-network forecasting methods.We compare between four different methods to preprocess the inputs and outputs, including a novel method proposed here based on the discrete Fourier series. We also compare between three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform longer horizon forecast, and that includes ways to partition the problem into the several subproblems of forecasting K steps ahead.

Landmark identification in computerized posteroanterior cephalometrics.

A lack of cognition regarding the reliability of landmark identification in posteroanterior cephalometrics keeps the extracted data questionable. The present study was designed to analyze this problem for the purpose of providing certain insights. An interactive computer graphic package was developed. This package was called EA-PAX (Error Analysis of Postero-Anterior cephalometric X-ray films). A random sample of 40 clear posteroanterior cephalometric head plates was studied. The reliability of landmark identification was established. The skeletal landmarks seemed more reliable than the dental landmarks. The variation in the direction and magnitude of the error was determined for each landmark. Most landmarks had their own characteristic noncircular envelope of error. It is not the philosophy of the present authors to tell the orthodontists what to use. Rather, the philosophy is to be aware of the amount and direction of variation for a particular landmark. Taking this into consideration will enable the orthodontists to look on their cephalometric numbers with a mental awareness of the possible variations. Several clinical and research studies are suggested in the article.

A modular computer vision system for picture segmentation and interpretation.

The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long-term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task, can communicate with both of these memories. The information in the LTM remains unchanged during the analysis, while the STM is being continually updated and revised by the appropriate processors. The latter may be conceived of as being activated by certain data conditions in the STM, and using the information in both the LTM and STM to alter the status of the STM.