Showing papers on "Artificial immune system published in 2008"

Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

Abstract: New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligenceto mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systemsincluding several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.

An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling

Abstract: The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.

Population-Based Intelligent Search in Reliability Evaluation of Generation Systems With Wind Power Penetration

Abstract: Adequacy assessment of power-generating systems provides a mechanism to ensure proper system operations in the face of various uncertainties including equipment failures. The integration of time-dependent sources such as wind turbine generators (WTGs) makes the reliability evaluation process more challenging. Due to the large number of system states involved in system operations, it is normally not feasible to enumerate all possible failure states to calculate the reliability indices. Monte Carlo simulation can be used for this purpose through iterative selection and evaluation of system states. However, due to its dependence on proportionate sampling, its efficiency in locating failure states may be low. The simulation may thus be time-consuming and take a long time to converge in some evaluation scenarios. In this paper, as an alternative option, four representative population-based intelligent search (PIS) procedures including genetic algorithm (GA), particle swarm optimization (PSO), artificial immune system (AIS), and ant colony system (ACS) are adopted to search the meaningful system states through their inherent convergence mechanisms. These most probable failure states contribute most significantly to the adequacy indices including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). The proposed solution methodology is also compared with the Monte Carlo simulation through conceptual analyses and numerical simulations. In this way, some qualitative and quantitative comparisons are conducted. A modified IEEE Reliability Test System (IEEE-RTS) is used in this investigation.

An interdisciplinary perspective on artificial immune systems

Abstract: This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner.

Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer

Abstract: Lung cancers are cancers that begin in the lungs. Other types of cancers may spread to the lungs from other organs. However, these are not lung cancers because they did not start in the lungs. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lung cancer using principles component analysis (PCA), fuzzy weighting pre-processing and artificial immune recognition system (AIRS). The approach system has three stages. First, dimension of lung cancer dataset that has 57 features is reduced to four features using principles component analysis. Second, a new weighting scheme based on fuzzy weighting pre-processing was utilized as a pre-processing step before the main classifier. Third, artificial immune recognition system was our used classifier. We took the lung cancer dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.

Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies

Abstract: Today, nature is used as a source of inspiration for the development of new techniques for solving complex problems in various domains, from engineering to biology, with innovative adaptations under investigation. The Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies provides the latest empirical research findings, theoretical frameworks, and technologies of natural computing and artificial immune systems (AIS). An excellent reference source for professionals, researchers, and academicians within the AIS and natural computing fields, this comprehensive collection written by leading international experts proposes new ideas, methods, and theories to solve problems of engineering and science.

Weka machine learning for predicting the phospholipidosis inducing potential.

Abstract: The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure-activity relationships that can discriminate between sets of chemicals that are active/inactive towards a certain biological receptor. An adverse effect of some cationic amphiphilic drugs is phospholipidosis that manifests as an intracellular accumulation of phospholipids and formation of concentric lamellar bodies. Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential. All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such as support vector machines and artificial immune systems. The best predictions are obtained with support vector machines, followed by a perceptron artificial neural network, logistic regression, and k-nearest neighbors.

A new hybrid AIS-GA for constrained optimization problems in mechanical engineering

Abstract: A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

A Novel State Transition Method for Metaheuristic-Based Scheduling in Heterogeneous Computing Systems

Abstract: Much of the recent literature shows a prevalance in the use of metaheuristics in solving a variety of problems in parallel and distributed computing. This is especially ture for problems that have a combinatorial nature, such as scheduling and load balancing. Despite numerous efforts, task scheduling remains one of the most challenging problems in heterogeneous computing environments. In this paper, we propose a new state transitionscheme , called the Duplication-based State Transition (DST) method specially designed for metaheuristics that can be used for the task scheduling problem in heterogeneous computing environments. State transition in metaheuristics is a key component that takes charge of generating variants of a given state. The DST method produces a new state by first overlapping randomly generated states with the current state and then the resultant state is refined by removing ineffectual tasks. The proposed method is incorporated into three different metaheuristics: genetic algorithms (GAs), simulated annealing (SA), and artificial immune system (AISs). They are experimentally evaluated and are also compared with existing algorithms. The experimental results confirm DST's promising impact on the performance of metaheuristics.

An immunological approach to mobile robot reactive navigation

Abstract: In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.

Prototype reduction using an artificial immune model

Abstract: Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.

An Artificial Immune System Heuristic for Generating Short Addition Chains

Abstract: This paper deals with the optimal computation of finite field exponentiation, which is a well-studied problem with many important applications in the areas of error-correcting codes and cryptography. It has been shown that the optimal computation of finite field exponentiation is a problem which is closely related to finding a suitable addition chain with the shortest possible length. However, it is also known that obtaining the shortest addition chain for a given arbitrary exponent is an NP-hard problem. As a consequence, heuristics are an obvious choice to compute field exponentiation with a semi-optimal number of underlying arithmetic operations. In this paper, we propose the use of an artificial immune system to tackle this problem. Particularly, we study the problem of finding both the shortest addition chains for exponents e with moderate size (i.e., with a length of less than 20 bits), and for the huge exponents typically adopted in cryptographic applications, (i.e., in the range from 128 to 2048 bits).

Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process

Abstract: The wire bonding process is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to improve the wire bonding process capability. In this study, an integration of artificial neural networks (ANN) with artificial immune systems (AIS) is proposed to optimize parameters for an IC wire bonding process. The algorithm of AIS with memory cell and suppressor cell mechanisms is developed. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and responses. Then a Taguchi method is applied to identify the critical parameters of AIS. Finally, the AIS algorithm is applied to find the optimal parameters by using the output of ANN as the affinity measure. A comparison between the result of the proposed AIS and that of a genetic algorithm (GA) is conducted in this study. The comparison shows that the searching quality of the proposed AIS is more effective than the GA in finding the optimal wire bonding process parameters.

A Fault Prediction Model with Limited Fault Data to Improve Test Process

Abstract: Software fault prediction models are used to identify the fault-prone software modules and produce reliable software. Performance of a software fault prediction model is correlated with available software metrics and fault data. In some occasions, there may be few software modules having fault data and therefore, prediction models using only labeled data can not provide accurate results. Semi-supervised learning approaches which benefit from unlabeled and labeled data may be applied in this case. In this paper, we propose an artificial immune system based semi-supervised learning approach. Proposed approach uses a recent semi-supervised algorithm called YATSI (Yet Another Two Stage Idea) and in the first stage of YATSI, AIRS (Artificial Immune Recognition Systems) is applied. In addition, AIRS, RF (Random Forests) classifier, AIRS based YATSI, and RF based YATSI are benchmarked. Experimental results showed that while YATSI algorithm improved the performance of AIRS, it diminished the performance of RF for unbalanced datasets. Furthermore, performance of AIRS based YATSI is comparable with RF which is the best machine learning classifier according to some researches.

Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates

Abstract: Artificial Immune Systems (AIS) are an emerging new field of research in Computational Intelligence that are applied to many areas of application, eg, optimization, anomaly detection and classification For optimization tasks, the use of hypermutation operators constitutes a common concept in AIS By now, only little theoretical work has been done in this field In this paper, we present a detailed theoretical runtime analysis that gives an insight into the dynamics of fitness based hypermutation processes Two specific mutation rates are considered using a simple immune inspired algorithm Our main focus lies thereby on the influence of parameters embedded in popular immune inspired hypermutation operators from the literature Our theoretical findings are accompanied by some empirical results

Evolution Induced Secondary Immunity: An Artificial Immune System Based Intrusion Detection System

Abstract: The analogy between immune systems and intrusion detection systems encourage the use of artificial immune systems for anomaly detection in computer networks. This paper describes a technique of applying artificial immune system along with genetic algorithm to develop an intrusion detection system. Far from developing primary immune response, as most of the related works do, it attempts to evolve this primary immune response to a secondary immune response using the concept of memory cells prevalent in natural immune systems. A genetic algorithm using genetic operators- selection, cloning, crossover and mutation- facilitates this. Memory cells formed enable faster detection of already encountered attacks. These memory cells, being highly random in nature, are dependent on the evolution of the detectors and guarantee greater immunity from anomalies and attacks. The fact that the whole procedure is enveloped in the concepts of approximate binding and memory cells of lightweight of natural immune systems makes this system reliable, robust and quick responding.

Dynamic detection for computer virus based on immune system

Abstract: Inspired by biological immune system, a new dynamic detection model for computer virus based on immune system is proposed. The quantitative description of the model is given. The problem of dynamic description for self and nonself in a computer virus immune system is solved, which reduces the size of self set. The new concept of dynamic tolerance, as well as the new mechanisms of gene evolution and gene coding for immature detectors is presented, improving the generating efficiency of mature detectors, reducing the false-negative and false-positive rates. Therefore, the difficult problem, in which the detector training cost is exponentially related to the size of self-set in a traditional computer immune system, is thus overcome. The theory analysis and experimental results show that the proposed model has better time efficiency and detecting ability than the classic model ARTIS.

A hardware/software partitioning algorithm based on artificial immune principles

Abstract: Hardware/software codesign is the main approach to designing the embedded systems. One of the primary steps of the hardware/software codesign is the hardware/software partitioning. A good partitioning scheme is a tradeoff of some constraints, such as power, size, performance, and so on. Inspired by both negative selection model and evolutionary mechanism of the biological immune system, an evolutionary negative selection algorithm for hardware/software partitioning, namely ENSA-HSP, is proposed in this paper. This ENSA-HSP algorithm is proved to be convergent, and its ability to escape from the local optimum is also analyzed. The experimental results demonstrate that ENSA-HSP is more efficient than traditional evolutionary algorithm.

Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection

Abstract: A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to successfully recognize and confront any foreign antigen, and at the same time allows the amelioration of the immune response across successive appearances of the same antigen. On the other hand, gene expression programming is the descendant of genetic algorithms and genetic programming and eliminates their main disadvantages, such as the genotype-phenotype coincidence, though it preserves their advantageous features. In order to perform the data mining task, the proposed algorithm introduces the notion of a data class antigen, which is used to represent a class of data, the produced rules are evolved by our clonal selection algorithm (CSA), which extends the recently proposed CLONALG algorithm. In CSA, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies that are coded as GEP chromosomes in order to exploit the flexibility and the expressiveness of such encoding. The proposed hybrid technique is tested on a set of benchmark problems in comparison to GEP. In almost all problems considered, the results are very satisfactory and outperform conventional GEP both in terms of prediction accuracy and computational efficiency.

Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm

Abstract: In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system. The approach system has two stages. In the first stage, dimension of lung cancer dataset that has 57 features is reduced to 4 features using principal component analysis. In the second stage, artificial immune recognition system (AIRS) was our used classifier. We took the lung cancer dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) Machine Learning Database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.

New Directions in Intelligent Interactive Multimedia

Abstract: Morphic Computing.- Amplifying Video Information-Seeking Success through Rich, Exploratory Interfaces.- Privacy-Enhanced Personalization.- Narrative Interactive Multimedia Learning Environments: Achievements and Challenges.- A Support Vector Machine Approach for Video Shot Detection.- Comparative Performance Evaluation of Artificial Neural Network-Based vs. Human Facial Expression Classifiers for Facial Expression Recognition.- Histographic Steganographic System.- Moving Object Detection and Tracking for the Purpose of Multimodal Surveillance System in Urban Areas.- Image Similarity Search in Large Databases Using a Fast Machine Learning Approach.- Fast Segmentation of Ovarian Ultrasound Volumes Using Support Vector Machines and Sparse Learning Sets.- Fast and Intelligent Determination of Image Segmentation Method Parameters.- Fast Image Segmentation Algorithm Using Wavelet Transform.- Musical Instrument Category Discrimination Using Wavelet-Based Source Separation.- Music Perception as Reflected in Bispectral EEG Analysis under a Mirror Neurons-Based Approach.- Automatic Recognition of Urban Soundscenes.- Low Bitrate Coding of Spot Audio Signals for Interactive and Immersive Audio Applications.- Extracting Input Features and Fuzzy Rules for Forecasting Exchange Rate Using NEWFM.- Forecasting Short-Term KOSPI Time Series Based on NEWFM.- The Convergence Analysis of an Improved Artificial Immune Algorithm for Clustering.- Artificial Immune System-Based Music Genre Classification.- Semantic Information Retrieval Dedicated to Multimedia Systems: A Platform Based on Conceptual Graphs.- Interactive Cluster-Based Personalized Retrieval on Large Document Collections.- Decision Support Services Facilitating Uncertainty Management.- Efficient Knowledge Transfer by Hearing a Conversation While Doing Something.- On Managing Users' Attention in Knowledge-Intensive Organizations.- Two Applications of Paraconsistent Logical Controller.- Encoding Modalities into Extended Petri Net for Analyzing Discrete Event Business Process.- Paraconsistent Before-After Relation Reasoning Based on EVALPSN.- Image Representation with Reduced Spectrum Pyramid.- Constructive Logic and the Sorites Paradox.- Resource Authorization in IMS with Known Multimedia Service Adaptation Capabilities.- Visualizing Ontologies on the Web.- Performance Analysis of ACL Packets Using Turbo Code in Bluetooth Wireless System.- Design and Implementation of Remote Monitoring System for Supporting Safe Subways Based on USN.- Evaluation of PC-Based Real-Time Watermark Embedding System for Standard-Definition Video Stream.- User Authentication Scheme Using Individual Auditory Pop-Out.- Combined Scheme of Encryption and Watermarking in H.264/Scalable Video Coding (SVC).- Evaluation of Integrity Verification System for Video Content Using Digital Watermarking.- Improving the Host Authentication Mechanism for POD Copy Protection System.- User Stereotypes Concerning Cognitive, Personality and Performance Issues in a Collaborative Learning Environment for UML.- Intelligent Mining and Indexing of Multi-language e-Learning Material.- Classic and Multimedia Based Activities to Teach Colors for Both Teachers and Their Pre-school Kids at the Kindergarten of Arab Schools in South of Israel.- TeamSim: An Educational Micro-world for the Teaching of Team Dynamics.- The Computerized Career Gate Test K.17.- Fuzzy Logic Decisions and Web Services for a Personalized Geographical Information System.- Design Rationale of an Adaptive Geographical Information System.- Multimedia, User-Centered Design and Tourism: Simplicity, Originality and Universality.- Dynamically Extracting and Exploiting Information about Customers for Knowledge-Based Interactive TV-Commerce.- Caring TV as a Service Design with and for Elderly People.- A Biosignal Classification Neural Modeling Methodology for Intelligent Hardware Construction.- Virtual Intelligent Agents to Train Abilities of Diagnosis in Psychology and Psychiatry.- The Role of Neural Networks in Biosignals Classification.- Medical Informatics in the Web 2.0 Era.- Affective Reasoning Based on Bi-modal Interaction and User Stereotypes.- General-Purpose Emotion Assessment Testbed Based on Biometric Information.- Realtime Dynamic Multimedia Storyline Based on Online Audience Biometric Information.- Assessing Separation of Duty Policies through the Interpretation of Sampled Video Sequences: A Pair Programming Case Study.- Trellis Based Real-Time Depth Perception Chip Using Interline Constraint.- Simple Perceptually-Inspired Methods for Blob Extraction.- LOGOS: A Multimodal Dialogue System for Controlling Smart Appliances.- One-Channel Separation and Recognition of Mixtures of Environmental Sounds: The Case of Bird-Song Classification in Composite Soundscenes.- Evaluating the Next Generation of Multimedia Software.- Evaluation Process and Results of a Middleware System for Accessing Digital Music LIbraries in MObile Services.- Interactive Systems, Design and Heuristic Evaluation: The Importance of the Diachronic Vision.

Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems

Abstract: This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm --- for predicting protein functions described in the Gene Ontology (GO) The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical classification problem where a protein can be assigned multiple classes (functions, GO terms) across several levels of the GO's term hierarchy Hence, the proposed approach, called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification The first version of the MHC-AIS builds a global classifier to predict all classes in the application domain, whilst the second version builds a local classifier to predict each class In both versions of the MHC-AIS the classifier is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users The two MHC-AIS versions are evaluated on a dataset of DNA-binding and ATPase proteins

An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease

Abstract: This paper presents a novel evolutionary algorithm inspired by protein/substrate binding exploited in enzyme genetic programming (EGP) and artificial immune networks. The immune network-inspired evolutionary algorithm has been developed in direct response to an application in clinical neurology, the diagnosis of Parkinson's disease. The inspiration for, and implementation of the algorithm is described and its performance to the application area considered.