Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

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An independent component analysis based filter design

Abstract — The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA). I.

this article presents a novel approach for ICU mortality prediction, based on the use of daily intermediate events

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In recent years, Clinical Data Mining has gained an increasing acceptance by the research community, due to its potential to find answers that could extend life or give comfort to ill persons. In particular, the use of tools such as Artificial Neural Networks, which have been mostly used in classification tasks. The present work reports the adoption of these techniques for the prediction of organ dysfunction of Intensive Care Unit patients. The novelty of this approach is due to the use intermediate outcomes, defined by the Out of Range Measurements of four bedside monitored variables, which obtained an overall accuracy of 70%.

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Abstract- Most of neural network training algorithms make use of gradient-based search and because of their disadvantages, researchers always interested in using alternative methods. In this paper to train feedforward, neural network for prediction problems a new Hybrid Improved Opposition-based Particle swarm optimization and Genetic Algorithm (HIOPGA) is proposed. The opposition-based PSO is utilized to search better in solution space. In addition, to restrain model overfit with training pattern, a new cross validation method is proposed. Several benchmark problems with varying dimensions are chosen to investigate the capabilities of the proposed algorithm as a training algorithm. The result of HIOPGA is compared with standard backpropagation algorithm with momentum term.