The Neuro-genetic approach for estimating the compression index

In the last decade, a number of empirical correlations have been proposed to connect the compression index to other soil parameters, such as liquid limit, plasticity index and the void index. This paper presents a correlation study between the physical properties and compression index which was conducted on normally consolidated clay by the hybridization of two approaches (artificial neuronal networks and genetic algorithms). A comparison was made between the measured experimentally and predictions compression indexes. The obtained results indicate that the Neuro-genetic model has the ability to accurately predict the compression index thus be used in practice by geotechnicians

The Neuro-genetic approach for estimating the compression index

In the last decade, a number of empirical correlations have been proposed to connect the compression index to other soil parameters, such as liquid limit, plasticity index and the void index. This paper presents a correlation study between the physical properties and compression index which was conducted on normally consolidated clay by the hybridization of two approaches (artificial neuronal networks and genetic algorithms). A comparison was made between the measured experimentally and predictions compression indexes. The obtained results indicate that the Neuro-genetic model has the ability to accurately predict the compression index thus be used in practice by geotechnicians

Using Evolutionary Algorithms for the Scheduling of Aircrew on Airborne Early Warning and Control System

Equipped with an advanced radar and other electronic systems mounted on its body, Airborne Early Warning and Control System (AWACS) enables the airspace to be monitored from medium to long distances and facilitates effective control of friendly aircraft. To operate the complex equipment and fulfill its critical functions, AWACS has a specialised flight and mission crew, all of whom are extensively trained in their respective roles. For mission accomplishment and effective use of resources, tasks should be scheduled, and individuals should be assigned to missions appropriately. In this paper, we implemented evolutionary algorithms for scheduling aircrew on AWACS and propose a novel approach using Genetic Algorithms (GA) with a special encoding strategy and modified genetic operations tailored to the problem. The objective is to assign aircrew to various AWACS tasks such as flights, simulator sessions, ground training classes and other squadron duties while aiming to maximise combat readiness and minimise operational costs. The presented approach is applied to several test instances consisting notional weekly schedules of Turkish Boeing 737 AEW&C Peace Eagle AWACS Base, generated similar to real-world examples. To test the algorithm and evaluate solution performance, experiments have been conducted on a novel scheduling software called AWACS Crew Scheduling (ACS), developed as a test bed. Computational results reveal that presented GA approach proves to be quite successful in solving the AWACS Crew Scheduling Problem and exhibits superior performance when compared to manual methods

The Neuro-genetic approach for estimating the compression index

In the last decade, a number of empirical correlations have been proposed to connect the compression index to other soil parameters, such as liquid limit, plasticity index and the void index. This paper presents a correlation study between the physical properties and compression index which was conducted on normally consolidated clay by the hybridization of two approaches (artificial neuronal networks and genetic algorithms). A comparison was made between the measured experimentally and predictions compression indexes. The obtained results indicate that the Neuro-genetic model has the ability to accurately predict the compression index thus be used in practice by geotechnicians

Incremental Data Mining for Active and Adaptive Knowledge Base for Patient Image Retrieval

Introduction: The general perception that the use of information technology (IT) in health care is 10 to 15 years behind that in other industrial sectors such as banking, manufacturing and airline is rapidly changing. Faced with an unprecedented era of competition and managed care, health providers are now exploring the opportunities for using IT to improve quality while simultaneously reduce the cost of health care. Clinical decision support systems and expert systems (CDSSs / ESs) focus on utilizing artificial intelligence and data mining techniques to provide fast decision support for physicians. Although several success stories about CDSSs / ESs have been reported [Freudenheim 92, Nash 94], these systems usually lack the ability to adapt to pattern changes that are embedded in new data. This is due to the fact that the traditional algorithms utilized by these systems cannot learn on an incremental basis, i.e., once they are built, they cannot adjust their structures in which the knowledge is imbedded. Lack of incremental learning ability is not a unique phenomenon in health care expert systems. In fact, most of the machine learning algorithms developed to date are limited in their ability to adjust learned rules based on new, incoming data. In the Internet Age, when new data keep coming in at a high speed, this is a serious limitation for decision support systems. The main objective of this dissertation is to develop a new incremental neural network technique in order to support decision support systems' adaptive needs. An Incremental Neural Net (INN) algorithm that utilizes hidden layer activations to incrementally learn new patterns from incoming data is proposed. We then applied it to the Image Retrieval Expert System (IRES), a clinical decision support system for radiologists in University Medical Center (UMC), University of Arizona. The performance comparison between the INN and traditional neural net approach are compared. This chapter is organized as follows: section 1.1 briefly introduces the concept of data mining and incremental learning, which serve as technical foundations for this dissertation. Section 1.2 introduces the background of IRES project and describes its adaptive need. Section 1.3 addresses research motivation and objectives. Section 1.4 provides an overview of this dissertation.Digitized from a paper copy provided by the Physiological Sciences Graduate Interdisciplinary Program

PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM

An artificial neural network (ANN), or shortly "neural network" (NN), is a powerful mathematical or computational model that is inspired by the structure and/or functional characteristics of biological neural networks. Despite the fact that ANN has been developing rapidly for many years, there are still some challenges concerning the development of an ANN model that performs effectively for the problem at hand. ANN can be categorized into three main types: single layer, recurrent network and multilayer feed-forward network. In multilayer feed-forward ANN, the actual performance is highly dependent on the selection of architecture and training parameters. However, a systematic method for optimizing these parameters is still an active research area. This work focuses on multilayer feed-forward ANNs due to their generalization capability, simplicity from the viewpoint of structure, and ease of mathematical analysis. Even though, several rules for the optimization of multilayer feed-forward ANN parameters are available in the literature, most networks are still calibrated via a trial-and-error procedure, which depends mainly on the type of problem, and past experience and intuition of the expert. To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. However most, if not all, of the existing approaches are focused partially on the part of architecture and training parameters. On the contrary, the GAANN approach presented here has covered most aspects of multilayer feed-forward ANN in a more comprehensive way. This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. Particularly, GA is utilized to determine the optimal number of hidden layers, number of neurons in each hidden layer, type of training algorithm, type of activation function of hidden and output neurons, initial weight, learning rate, momentum term, and epoch size of a multilayer feed-forward ANN. In this thesis, the approach has been analyzed and algorithms that simulate the new approach have been mapped out

Food security modelling using two stage hybrid model and fuzzy logic risk assessment

Food security has become a key issue worldwide in recent years. According to the Department for Environment Food and Rural Affair (DEFRA) UK, the key components of food security are food availability, global resource sustainability, access, food chain resilience, household food security, safety and confidence of public towards food system. Each of these components has its own indicators which need to be monitored. Only a few studies had been made towards analysing food security and most of these studies are based on conventional data analysis methods such as the use of statistical techniques. In handling food security datasets such as crops yield, production, economy growth, household behaviour and others, where most of the data is imprecise, non-linear and uncertain in nature, it is better to handle the data using intelligent system (IS) techniques such as fuzzy logic, neural networks, genetic algorithm and hybrid systems, rather than conventional techniques. Therefore this thesis focuses on the modelling of food security using IS techniques, and a newly developed hybrid intelligent technique called a 2-stage hybrid (TSH) model, which is capable of making accurate predictions. This technique is evaluated by considering three applications of food security research areas which relate to each of the indicators in the DEFRA key food security components. In addition, another food security model was developed, called a food security risk assessment model. This can be used in assessing the level of risk for food security. The TSH model is constructed by using two key techniques; the Genetic Algorithm (GA) module and the Artificial Neural Network (ANN) module, where these modules combine the global and local search, by optimizing the inputs of ANN in the first stage process and optimizing of weight and threshold of ANN, which is then used to remodel the ANN resulting in better prediction. In evaluating the performance of the TSH prediction model, a total of three datasets have been used, which relate to the food security area studied. These datasets involve the prediction of farm household output, prediction of cereal growth per capita as the food availability main indicators in food security component, and grain security assessment prediction. The TSH prediction model is benchmarked against five others techniques. Each of these five techniques uses an ANN as the prediction model. The models used are: Principal Component Analysis (PCA), Multi-layered Perceptron-Artificial Neural Network (MLP-ANN), feature selection (FS) of GA-ANN, Optimized Weight and Threshold (OWTNN) and Sensitive Genetic Neural Optimization (SGNO). Each of the application datasets considered is used to show the capability of the TSH model in making effective predictions, and shows that the general performance of the model is better than the other benchmarked techniques. The research in this thesis can be considered as a stepping-stone towards developing other tools in food security modelling, in order to aid the safety of food security