Predicting Adsorption Behavior in Engineered Floodplain Filtration System Using Backpropagation Neural Networks

Engineered floodplain filtration (EFF) system is an eco-friendly low-cost water treatment process wherein water contaminants can be removed, by adsorption and-or degraded by microorganisms, as the infiltrating water moves from the wastewater treatment plants to the rivers. An artificial neural network (ANN) based approach was used in this study to approximate and interpret the complex input/output relationships, essentially to understand the breakthrough times in EFF. The input parameters to the ANN model were inlet concentration of a pharmaceutical, ibuprofen (ppm) and flow rate (md– 1), and the output parameters were six concentration-time pairs (C, t). These C, t pairs were the times in the breakthrough profile, when 1%, 5%, 25%, 50%, 75%, and 95% of the pollutant was present at the outlet of the system. The most dependable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R2) value (>0.99) achieved during prediction of the testing set. The proposed ANN model for EFF operation could be used as a potential alternative for knowledge-based models through proper training and testing of variables.

Machine Learning Approach for Content Based Image Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Learning with Querying and its Application in Network Security

Understanding the ability of learning in both humans and non-humans is an important research crossing the boundaries between several scientific disciplines from computer science to brain science and psychology. In this chapter, the authors first introduce a query based learning concept (learning with query) in which all the minds’ beliefs and actions will be revised by observing the outcomes of past mutual interactions (selective-attention and self-regulation) over time. That is, moving into an active learning and aggressive querying method will be able to focus on effectiveness to achieve learning goals and desired outcomes. Secondly, they show that the proposed method has better effectiveness for several learning algorithms, such as decision tree, particle swarm optimization, and self-organizing maps. Finally, a query based learning method is proposed to solve network security problems as a sample filter at intrusion detection. Experimental results show that the proposed method can not only increase the accuracy detection rate for suspicious activity and recognize rare attack types but also significantly improve the efficiency of intrusion detection. Therefore, it is good to design and to implement an effective learning algorithm for information security.

Ontology-Based Clustering of the Web Meta-Search Results

The chapter presents a meta-search tool developed in order to deliver search results structured according to the specific interests of users. Meta-search means that for a specific query, several search mechanisms could be simultaneously applied. Using the clustering process, thematically homogenous groups are built up from the initial list provided by the standard search mechanisms. The results are more user oriented, as a result of the ontological approach of the clustering process. After the initial search made on multiple search engines, the results are pre-processed and transformed into vectors of words. These vectors are mapped into vectors of concepts, by calling an educational ontology and using the WordNet lexical database. The vectors of concepts are refined through concept space graphs and projection mechanisms, before applying the clustering procedure. Implementation details and early experimentation results are also provided.

Nature-Inspired Toolbox to Design and Optimize Systems

Nature-Inspired (NI) Toolbox is a Particle Swarm Optimization (PSO) based toolbox which is developed in the MATLAB environment. It has been released under General Public License and hosted at SourceForge.net (http://sourceforge.net/projects/nitool/). The purpose of this toolbox is to facilitate the users/designers in design and optimization of their systems. This chapter discusses the fundamental concepts of PSO algorithms in the initial sections, followed by discussions and illustrations of benchmark optimization functions. Various modules of the Graphical User Interface (GUI) of NI Toolbox are explained with necessary figures and snapshots. In the ending sections, simulations results present comparative performance of various PSO models with concluding remarks.

An Introduction to Pattern Classification

In this chapter the authors present an overview of pattern classification. In particular, they focus on the mathematical background of pattern classification rather than discussing the practical analysis of various pattern classification methods, and present the derivation of classification rules from a mathematical aspect. First, the authors define the pattern space without the loss of generality. Then, the categorisation of pattern classification is presented according to the design of classification systems. The mathematical formulation of each category of pattern classification is also given. Theoretical discussion using mathematical formulations is presented for distance-based pattern classification and statistical pattern classification. For statistical pattern classification, the standard assumption is made where patterns from each class follow normal distributions with different means and variances.

A PSO-Based Framework for Designing Fuzzy Systems from Noisy Data Set

In most of Fuzzy Logic System (FLS) designs, human reasoning is encoded into programs to make decisions and/or control systems. Designing an optimal FLS is equivalent to an optimization problem, in which efforts are made to locate a point in fitness search-space where the performance is better than that of other locations. The number of parameters to be tuned in designing an FLS is quite large. Also, fitness search space is highly non-linear, deceptive, non-differentiable, and multi-modal in nature. Noisy data, from which to construct the FLS, may make the design problem even more difficult. This chapter presents a framework to design Type-1 (T1) and Interval Type-2 (IT2) FLSs (Liang and Mendel, 2000c, Mendel, 2001, 2007, Mendel et al., 2006) using Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995, Kennedy and Eberhart, 1995). This framework includes the use of PSO based Nature Inspired (NI) Toolbox discussed in the chapter titled, “Nature-Inspired Toolbox to Design and Optimize Systems.”

Insights from Jurisprudence for Machine Learning in Law

The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reasoning. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law.

Development of an Intelligent Neural Model to Predict and Analyze the VOC Removal Pattern in a Photocatalytic Reactor

Volatile organic compounds (VOCs) belong to a new class of air pollutant that causes significant effect on human health and environment. Photocatalytic oxidation is an innovative, highly efficient, and promising option to decontaminate air polluted with VOCs, at faster elimination rates. This study pertains to the application of artificial neural networks to model the removal dynamics of an annular type photoreactor for gas – phase VOC removal. Relevant literature pertaining to the experimental work has been reported in this chapter. The different steps involved in developing a suitable neural model have been outlined by considering the influence of internal network parameters on the model architecture. Anew, the neural network modeling results were also subjected to sensitivity analysis in order to identify the most influential parameter affecting the VOC removal process in the photoreactor.

Neural Network Based Classifier Ensembles

This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare the author’s recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. The first type is a classifier ensemble that uses a neural network for all its base classifiers. The second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. The third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting, and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository.