The Performance of Immune Based Neural Network with Financial Time Series Prediction

This paper presents the use of immune based neural networks which include multilayer perceptron and functional neural network for the prediction of financial time series signals. Extensive simulations for the prediction of one and five steps ahead of stationary and non-stationary time series were performed which indicate that immune based neural networks in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and rapid convergence over multilayer perceptrons

The FL-SMIA Network: A Novel Architecture for Time Series Prediction

In this paper we propose the FL-SMIA model, a novel neural network model that combines the principles of the Functional Link Neural Network (FLNN) with the Self-organizing Multilayer Neural Network using the Immune Algorithm (SMIA). We describe the FL-SMIA architecture and operation and evaluate its predictive performance on different financial time series in comparison to other neural network models. The FL-SMIA model combines the higher-order inputs of the tensor-product FLNN, i.e. the products of raw input features, with the self-organizing hidden layer of SMIA that dynamically grows and adapts to the input vectors. The FL-SMIA has two advantages over other models. First, it can dynamically adapt to growing amounts of data with a model that grows increasingly complex. Second, it keeps an explicit representation of the patterns it recognises in the data. Experimental results show that the FL-SMIA improves performance, as measured by annualised return in five-days-ahead and one-day-ahead prediction tasks for share prices and exchange rates, over the SMIA networks alone and over standard multilayer perceptrons. It performs on the same level as the FLNN, sometimes better but not significantly so. The result that FLNN and FL-SMIA outperform other multilayer models indicates that particularly the higher-order features contribute to the improved performance and motivate further research into mixed neural network architectures for financial time series prediction

Comparing unsupervised layers in neural networks for financial time series prediction

In this study, we propose and compare neural network models that use unsupervised layers for the prediction of financial time series. We compare the novel FL-RBM and FL-SMIA-RMB models that integrate a Restricted Boltzmann Machine (RBM) and the self-organizing layer of the Selforganized Multi-Layer Network using the Immune Algorithm (SMIA) with the FL-SMIA network and a standard MLP. We aim to investigate the performance of unsupervised learning in comparison to purely supervised and other mixed models. The FL-RBM model combines the products of raw input features (the Functional Link, FL), with the Restricted Boltzmann Machine RBM as a self-organizing first hidden layer, while the FL-SMIA model uses the Immune Algorithm on the first layer. The FLSMIA- RBM model, combines both self-organizing layers with a back-propagation network. The results show that the FL-SMIA model outperforms the FL-RBM, the FL-SMIA-RBM and the MLP as measured by Annualized Return (AR) in one-day-ahead prediction on exchange rates time series. In terms of volatility, the FL-SMIA and MLP perform similarly

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Neural network architecture called Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm is proposed for the prediction of weather signals. Two sets of experiments have been implemented. The simulation results showed slight improvement achieved by the proposed network when using the average results of 30 simulations. For the second set of experiments, the simulation results indicated that there is no significant improvement over the first set of experiments. Since clustering methods have been widely used in different applications of data mining, the adaption of unsupervised learning in the proposed network might serve these different applications, for example, medical diagnostics and pattern recognition for big data. The structure of the proposed network can be modified for clustering tasks by changing the back-propagation algorithm in the output layer. This can extend the application of the proposed network to scientifically analyse different types of big data

A Machine Learning System for Automatic Detection of Preterm Activity Using Artificial Neural Networks and Uterine Electromyography Data

Preterm births are babies born before 37 weeks of gestation. The premature delivery of babies is a major global health issue with those affected at greater risk of developing short and long-term complications. Therefore, a better understanding of why preterm births occur is needed. Electromyography is used to capture electrical activity in the uterus to help treat and understand the condition, which is time consuming and expensive. This has led to a recent interest in automated detection of the electromyography correlates of preterm activity. This paper explores this idea further using artificial neural networks to classify term and preterm records, using an open dataset containing 300 records of uterine electromyography signals. Our approach shows an improvement on existing studies with 94.56% for sensitivity, 87.83% for specificity, and 94% for the area under the curve with 9% global error when using the multilayer perceptron neural network trained using the Levenberg-Marquardt algorithm

A Framework to Support E-Commerce Development for People with Visual Impairment

The World Wide Web Consortium provides software developers with guidelines for designing accessible, cross browser compatible websites. Currently however, there are no guidelines in this area specific to the features of an e-commerce website. This paper explores the current usability issues relevant to users with visual impairment and further proposes a framework that seeks to ensure the site is suitable for visually impaired users. This paper includes data analysis which compares current issues in web technologies suitable for e-commerce and proposes system adaptations which can be conducted to portray the improvements in overall user experience. To this extent, several validation tools and testing techniques have been used to identify the usability issues that visually impaired users currently face when shopping online

A Smart Framework for Predicting the Onset of Nocturnal Enuresis (PrONE) in Children and Young People

Bed wetting during normal sleep in children and young people has a significant impact on the child and their parents. The condition is known as nocturnal enuresis and its underlying cause has been subject to different explanatory factors that include, neurological, urological, sleep, genetic and psychosocial influences. Several clinical and technological interventions for managing nocturnal enuresis exist that include the clinician’s opinions, pharmacology interventions, and alarm systems. However, most have failed to produce any convincing results; clinical information is often subjective and often inaccurate, the use of desmopression and tricyclic antidepressants only report between 20% and 40% success, and alarms only a 50% success fate. This paper posits an alternative research idea concerned with the early detection of impending involuntary bladder release. The proposed framework is a measurement and prediction system that processes moisture and bladder volume data from sensors fitted into undergarments that are used by patients suffering with nocturnal enuresis. The proposed framework represents a level of sophistication and accuracy in nocturnal enuresis treatment not previously considered

Simulation of Area of Interest Management for Massively Multiplayer Online Games Using OPNET

In recent years, there has been an important growth of online gaming. Today’s Massively Multiplayer Online Games (MMOGs) can contain millions of synchronous players scattered across the world and participating with each other within a single shared game. The increase in the number of players in MMOGs has led to some issues with the demand of server which generates a significant increase in costs for the game industry and impacts to the quality of service offered to players. With the number of players gradually increasing, servers still need to work efficiently under heavy load and, new researches are required to improve the established MMOG system architectures. In dealing with a considerable scale of massively multiplayer online games, several client-server and peer-to-peer solutions have been proposed. Although they have improved the scalability of MMOGs in different degrees, they faced new serious challenges in interest management. In this paper, we propose a novel static area of interest management in order to reduce the delay and traffic of Hybrid P2P MMOGs. We propose to use OPNET Modeler 18.0, and in particular the custom application to simulate the new architecture, which required the implementation of new nodes models and behaviors in the simulator to emulate correctly the new architecture. The scenarios include both client-server and hybrid P2P system to evaluate the communication of games with (125, 500, and 1000) peers. The simulation results show that area of interest management for MMOGs based on the hybrid P2P architectures have low delay and traffic received compared with MMOGs based on client-server system

Evaluation of Scalability and Communication in MMOGs

Massively Multiplayer Online Games (MMOGs) can involve millions of synchronous players scattered across the world and participating with each other within a single shared game. One of the most significant issues in MMOGs is scalability and it is impact on the responsiveness and the quality of the game. In this paper, we propose a new architecture to increase the scalability without affecting the responsiveness of the game, using a hybrid Peer-to-Peer system. This mechanism consists of central servers to control and manage the game state, as well as super-peer and clone-super-peer to control and manage sub-networks of nodes sharing common regions of the game world. We use the OPNET Modeler to simulate the system and compare the results with client/server system to show the difference in delay and traffic received for various applications such as remote login, database, HTTP, and FTP sessions which are all part of an MMOG system. We use four scenarios for each system to evaluate the scalability of the system with different number of peers (i.e.125, 250, 500, and 1000 peers). The results show that the hybrid P2P system is more scalable for MMOGs when compared with client/server system