OBJECTIVE Serrated (hyperplastic) polyposis (SP) is a rare disorder with multiple colorectal hyperplastic polyps and often sessile serrated adenomas/polyps (SSA/P) or adenomas. Although associated with colorectal cancer, the course of SP is not well described. DESIGN 44 patients with SP were studied. The results of 146 colonoscopies with median follow-up of 2.0 years (range 0-30) and a median...

Depression is a prevailing issue and is an increasing problem in many people’s lives. Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting in high demand for detecting depression in advance automatically. This paper tackles the challenging problem of forecasting severely depressed moods based on self-reported histories. Despite the large amount of research...

The demand forecasting technique which is modeled by artificial intelligence approaches using artificial neural networks. The consumer product causers the difficulty in forecasting the future demand and the accuracy of the forecast In performance of the artificial neural network an advantage in a constantly changing business environment and demand forecasting an organization in order to make ri...

This paper describes methods for synthesizing the internal representation of a compiler into a hardware description language; a process often referred to as hardware compilation. We present a framework for this transformation including methods to control the path of execution and ways to deal with the data communication. We show how static single assignment (SSA) is useful to reduce the amount ...

This study considers using Metropolis-Hastings algorithm for stochastic simulation of chemical reactions. The proposed method uses SSA (Stochastic Simulation Algorithm) distribution which is a standard method for solving well-stirred chemically reacting systems as a desired distribution. A new numerical solvers based on exponential form of exact and approximate solutions of CME (Chemical Master...

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfal...

A reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. It is a...

  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...

A learner’s modifiable components are called its policy. An algorithm that modifies the policy is a learning algorithm. If the learning algorithm has modifiable components represented as part of the policy, then we speak of a self-modifying policy (SMP). SMPs can modify the way they modify themselves etc. They are of interest in situations where the initial learning algorithm itself can be impr...