Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Effect of nano black rice husk ash on the chemical and physical properties of porous concrete pavement

Black rice husk is a waste from this agriculture industry. It has been found that majority inorganic element in rice husk is silica. In this study, the effect of Nano from black rice husk ash (BRHA) on the chemical and physical properties of concrete pavement was investigated. The BRHA produced from uncontrolled burning at rice factory was taken. It was then been ground using laboratory mill with steel balls and steel rods. Four different grinding grades of BRHA were examined. A rice husk ash dosage of 10% by weight of binder was used throughout the experiments. The chemical and physical properties of the Nano BRHA mixtures were evaluated using fineness test, X-ray Fluorescence spectrometer (XRF) and X-ray diffraction (XRD). In addition, the compressive strength test was used to evaluate the performance of porous concrete pavement. Generally, the results show that the optimum grinding time was 63 hours. The result also indicated that the use of Nano black rice husk ash ground for 63hours produced concrete with good strengt

An investigation into the use of neural networks for the prediction of the stock exchange of Thailand

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to be studied extensively and research focused on the SET will contribute to understanding its unique characteristics and will lead to identifying relevant information to assist investment in this stock market. Experiments were carried out to determine the best network architecture, training method, and input data to use for this task. With regards network architecture, feedforward networks with three layers were used - an input layer, a hidden layer and an output layer - and networks with different numbers of nodes in the hidden layers were tested and compared. With regards training method, neural networks were trained with back-propagation and with genetic algorithms. With regards input data, three set of inputs, namely internal indicators, external indicators and a combination of both were used. The internal indicators are based on calculations derived from the SET while the external indicators are deemed to be factors beyond the control of the Thailand such as the Down Jones Index

Human-Understandable Explanations of Neural Networks

Das 21. Jahrhundert ist durch Datenströme enormen Ausmaßes gekennzeichnet. Dies hat die Popularität von Berechnungsmodellen, die sehr datenintensiv sind, wie z.B. neuronale Netze, drastisch erhöht. Aufgrund ihres großen Erfolges bei der Mustererkennung sind sie zu einem leistungsstarken Werkzeug für Vorhersagen, Klassifizierung und Empfehlungen in der Informatik, Statistik, Wirtschaft und vielen anderen Disziplinen geworden. Trotz dieser verbreiteten Anwendung sind neuronale Netze Blackbox-Modelle, d.h. sie geben keine leicht interpretierbaren Einblicke in die Struktur der approximierten Funktion oder in die Art und Weise, wie die Eingabe in die entsprechende Ausgabe umgewandelt wird. Die jüngste Forschung versucht, diese Blackboxen zu öffnen und ihr Innenleben zu enthüllen. Bisher haben sich die meisten Forschungsarbeiten darauf konzentriert, die Entscheidungen eines neuronalen Netzes auf einer sehr technischen Ebene und für ein Informatikfachpublikum zu erklären. Da neuronale Netze immer häufiger eingesetzt werden, auch von Menschen ohne tiefere Informatikkenntnisse, ist es von entscheidender Bedeutung, Ansätze zu entwickeln, die es ermöglichen, neuronale Netze auch für Nicht-Experten verständlich zu erklären. Das Ziel ist, dass Menschen verstehen können, warum das neuronale Netz bestimmte Entscheidungen getroffen hat, und dass sie das Ergebnis des Modells durchgehend interpretieren können. Diese Arbeit beschreibt ein Rahmenwerk, das es ermöglicht, menschlich verständliche Erklärungen für neuronale Netze zu liefern. Wir charakterisieren menschlich nachvollziehbare Erklärungen durch sieben Eigenschaften, nämlich Transparenz, Überprüfbarkeit, Vertrauen, Effektivität, Überzeugungskraft, Effizienz und Zufriedenheit. In dieser Arbeit stellen wir Erklärungsansätze vor, die diese Eigenschaften erfüllen. Zunächst stellen wir TransPer vor, ein Erklärungsrahmenwerk für neuronale Netze, insbesondere für solche, die in Produktempfehlungssystemen verwendet werden. Wir definieren Erklärungsmaße auf der Grundlage der Relevanz der Eingaben, um die Vorhersagequalität des neuronalen Netzes zu analysieren und KI-Anwendern bei der Verbesserung ihrer neuronalen Netze zu helfen. Dadurch werden Transparenz und Vertrauen geschaffen. In einem Anwendungsfall für ein Empfehlungssystem werden auch die Überzeugungskraft, die den Benutzer zum Kauf eines Produkts veranlasst, und die Zufriedenheit, die das Benutzererlebnis angenehmer macht, berücksichtigt. Zweitens, um die Blackbox des neuronalen Netzes zu öffnen, definieren wir eine neue Metrik für die Erklärungsqualität ObAlEx in der Bildklassifikation. Mit Hilfe von Objekterkennungsansätzen, Erklärungsansätzen und ObAlEx quantifizieren wir den Fokus von faltenden neuronalen Netzwerken auf die tatsächliche Evidenz. Dies bietet den Nutzern eine effektive Erklärung und Vertrauen, dass das Modell seine Klassifizierungsentscheidung tatsächlich auf der Grundlage des richtigen Teils des Eingabebildes getroffen hat. Darüber hinaus ermöglicht es die Überprüfbarkeit, d. h. die Möglichkeit für den Benutzer, dem Erklärungssystem mitzuteilen, dass sich das Modell auf die falschen Teile des Eingabebildes konzentriert hat. Drittens schlagen wir FilTag vor, einen Ansatz zur Erklärung von faltenden neuronalen Netzwerken durch die Kennzeichnung der Filter mit Schlüsselwörtern, die Bildklassen identifizieren. In ihrer Gesamtheit erklären diese Kennzeichnungen die Zweckbestimmung des Filters. Einzelne Bildklassifizierungen können dann intuitiv anhand der Kennzeichnungen der Filter, die das Eingabebild aktiviert, erklärt werden. Diese Erklärungen erhöhen die Überprüfbarkeit und das Vertrauen. Schließlich stellen wir FAIRnets vor, das darauf abzielt, Metadaten von neuronalen Netzen wie Architekturinformationen und Verwendungszweck bereitzustellen. Indem erklärt wird, wie das neuronale Netz aufgebaut ist werden neuronale Netzer transparenter; dadurch dass ein Nutzer schnell entscheiden kann, ob das neuronale Netz für den gewünschten Anwendungsfall relevant ist werden neuronale Netze effizienter. Alle vier Ansätze befassen sich mit der Frage, wie man Erklärungen von neuronalen Netzen für Nicht-Experten bereitstellen kann. Zusammen stellen sie einen wichtigen Schritt in Richtung einer für den Menschen verständlichen KI dar

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Machine learning for network based intrusion detection: an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data.

For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory. Consequently, it is difficult to analyse the current body of research to determine the value in the findings. This thesis reports on an empirical investigation to determine the underlying causes of the discrepancies. Several methodological factors, such as choice of data subset, validation method and data preprocessing, are identified and are found to affect the results significantly. These findings have also enabled a better interpretation of the current body of research. Furthermore, the criticisms in the literature are addressed and future use of the data set is discussed, which is important since researchers continue to use it due to a lack of better publicly available alternatives. Due to the nature of the intrusion detection domain, there is an extreme imbalance among the classes in the KDD Cup '99 data set, which poses a significant challenge to machine learning. In other domains, researchers have demonstrated that well known techniques such as Artificial Neural Networks (ANNs) and Decision Trees (DTs) often fail to learn the minor class(es) due to class imbalance. However, this has not been recognized as an issue in intrusion detection previously. This thesis reports on an empirical investigation that demonstrates that it is the class imbalance that causes the poor detection of some classes of intrusion reported in the literature. An alternative approach to training ANNs is proposed in this thesis, using Genetic Algorithms (GAs) to evolve the weights of the ANNs, referred to as an Evolutionary Neural Network (ENN). When employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. These findings demonstrate that the issues of learning from imbalanced data are not due to limitations of the ANNs; rather the training algorithm. Moreover, the ENN is capable of detecting a class of intrusion that has been reported in the literature to be undetectable by ANNs. One limitation of the ENN is a lack of control of the classification trade-off the ANNs obtain. This is identified as a general issue with current approaches to creating classifiers. Striving to create a single best classifier that obtains the highest accuracy may give an unfruitful classification trade-off, which is demonstrated clearly in this thesis. Therefore, an extension of the ENN is proposed, using a Multi-Objective GA (MOGA), which treats the classification rate on each class as a separate objective. This approach produces a Pareto front of non-dominated solutions that exhibit different classification trade-offs, from which the user can select one with the desired properties. The multi-objective approach is also utilised to evolve classifier ensembles, which yields an improved Pareto front of solutions. Furthermore, the selection of classifier members for the ensembles is investigated, demonstrating how this affects the performance of the resultant ensembles. This is a key to explaining why some classifier combinations fail to give fruitful solutions

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties