An artificial immune system for data analysis

Cells as the first data scientists

The concepts that we generally associate with the field of data science are strikingly descriptive of the way that life, in general, processes information about its environment. The 'information life cycle', which enumerates the stages of information treatment in data science endeavours, also captures the steps of data collection and handling in biological systems. Similarly, the 'data-information-knowledge ecosystem', developed to illuminate the role of informatics in translating raw data into knowledge, can be a framework for understanding how information is constantly being transferred between life and the environment. By placing the principles of data science in a broader biological context, we see the activities of data scientists as the latest development in life's ongoing journey to better understand and predict its environment. Finally, we propose that informatics frameworks can be used to understand the similarities and differences between abiotic complex evolving systems and life.

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

Comparative study on credit card fraud detection based on different support vector machines

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. The Wilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks.

Applications of Artificial Intelligence in Transport: An Overview

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

Towards the development of robot immune system: A combined approach involving innate immune cells and T-lymphocytes

Mobile robots in uncertain and unstructuredenvironments frequently encounter faults. Therefore, an effective fault detection and recovery mechanism is required. One can possibly investigate natural systems to seek inspiration to develop systems that can handle such faults. Authors, in this pursuit, have explored the possibility of designing an artificial immune system, called Robot Immune System (RIS), to maintain a robot's internal health-equilibrium. This contrasts with existing approaches in which specific robotic tasks are performed instead of developing a self-healing robot. In this respect, a fault detection and recovery methodology based on innate and adaptive immune functions has been successfully designed and developed. The immuno-inspired methodology is applied to a simulated robot using Robot Operating System and Virtual Robot Experimentation Platform. Through extensive simulations in increasingly difficult scenarios, the RIS has proven successful in autonomously detecting the abnormal behaviors, performing the recovery actions, and maintaining the homeostasis in the robot. In addition to being multi-tiered, the developed RIS is also a non-deterministic and population-based system.

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.

Conceptual Spaces of the Immune System

The immune system can be looked at as a cognitive system. This is often done in analogy to the neuro-psychological system. Here, it is demonstrated that the cognitive functions of the immune system can be properly described within a new theory of cognitive science. Gärdenfors’ geometrical framework of conceptual spaces is applied to immune cognition. Basic notions, like quality dimensions, natural properties and concepts, similarities, prototypes, saliences, etc., are related to cognitive phenomena of the immune system. Constraints derived from treating the immune system within a cognitive theory, like Gärdenfors’ conceptual spaces, might well prove to be instrumental for the design of vaccines, immunological diagnostic tests, and immunotherapy.

A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan

Classifying imbalanced data in medical informatics is challenging. Motivated by this issue, this study develops a classifier approach denoted as BSMAIRS. This approach combines borderline synthetic minority oversampling technique (BSM) and artificial immune recognition system (AIRS) as global optimization searcher with the nearest neighbor algorithm used as a local classifier. Eight electronic medical datasets collected from University of California, Irvine (UCI) machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS. Comparisons with several well-known classifiers were conducted based on accuracy, sensitivity, specificity, and G-mean. Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems. To further confirm its performance, BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database, Taiwan. This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer.

The Role of Hypermutation and Affinity Maturation in AIS Approaches to Clustering

In recent years, several artificial immune system (AIS) approaches have been proposed for unsupervised learning. Generally, in these approaches antibodies (or B-cells) are considered as clusters and antigens are data samples or instances. Moreover, antigens are trapped through free-floating antibodies or immunoglobulins. In all these approaches, hypermutation plays an important role. Hypermutation is responsible for producing mutated copies of stimulated antibodies/B-cells to capture similar antigens with higher affinity (similarity) measure and responsible to create diverse pool of solutions. Humoral-Mediated Artificial Immune System (HAIS) is an example of such algorithms. However, there is currently little understanding about the effectiveness of hypermutation operator in AIS approaches. In this chapter, we investigate the role of the hypermutation operator as well as affinity threshold (AT) parameters in order to achieve efficient clustering solutions. We propose a three-step methodology to examine the importance of hypermutation and the AT parameters in AIS approaches to clustering using basic concepts of HAIS algorithm. Here, the role of hypermutation in under-fitting and over-fitting the data will be discussed in the context of measure of entropy.

Artificial Immune Optimization Algorithm

Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization. Of particular interest is the way human body responds to diseases and pathogens as well as adapts to remain immune for long periods after a disease has been combated. In this chapter, we are presenting a novel multilayered natural immune system (NIS) inspired algorithms in the domain of optimization. The proposed algorithm uses natural immune system components such as B-cells, Memory cells and Antibodies; and processes such as negative clonal selection and affinity maturation to find multiple local optimum points. Another benefit this algorithm presents is the presence of immunological memory that is in the form of specific memory cells which keep track of previously explored solutions. The algorithm is evaluated on two well-known numeric functions to demonstrate the applicability.

Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach

Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.

A lifelong learning hyper-heuristic method for bin packing

We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification

Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, an AC algorithm that integrates the clonal selection of the immune system along with deterministic data sampling. Upon picking a representative sample of the original data, it proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. In addition, the proposed approach is significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.

Application of immune and genetic algorithms to the identification of a polymer based on its X-ray diffraction curve

This paper describes how a combination of two methods of artificial intelligence, an immune algorithm and a genetic algorithm, can be used to recognize a polymer by the shape of its X-ray diffraction curve. To this end, the hybrid algorithm uses a database which contains theoretical functions describing wide-angle X-ray diffraction curves of different polymers. These curves are compared by the algorithm with the experimental diffraction curve and the most similar are chosen. Such theoretical curves are kept in the immunological memory, and their parameters can be set as the starting ones in the optimization methods used for decomposition of the experimental curve into crystalline peaks and amorphous component. Using this algorithm, the preparation of the starting parameters is much easier and faster. Decomposition is the most important step in polymer crystallinity determination.

Computational intelligence techniques in bioinformatics

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.

IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS

Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.

HUMORAL ARTIFICIAL IMMUNE SYSTEM (HAIS) FOR SUPERVISED LEARNING

Nature over millions of years has found innovative, robust and effective methods through evolution for helping organisms deal with the challenges they face when attempting to survive in hostile and uncertain environments. Two critical natural mechanisms in this evolutionary process are variation and selection, which form the basis of "evolutionary computing" (EC). EC has proved successful when dealing with complex problems, such as classification, clustering and optimization. In recent years, as our knowledge of microbiology has deepened, researchers have turned to micro-level biology for inspiration to help solve complex problems. This paper describes a novel supervised learning algorithm inspired by the humoral mediated response triggered by the adaptive immune system. The proposed algorithm uses core immune system concepts such as memory cells, plasma cells and B-cells as well as parameters and processes inspired by our knowledge of the microbiology of immune systems, such as negative clonal selection and affinity thresholds. In particular, we show how local and global similarity based measures based on affinity threshold can help to avoid over-fitting data. The novelty of the proposed algorithm is discussed in the context of existing immune system-based supervised learning algorithms. The performance of the proposed algorithm is tested on well-known benchmarked real world datasets and the results indicate performance not worse than existing techniques in most cases and improvement over previously reported results in some. The role of memory cells is highlighted as a key feature in AIS-based supervised learning that deserves further exploration and evaluation.

Nisin F, intraperitoneally injected, may have a stabilizing effect on the bacterial population in the gastro-intestinal tract, as determined in a preliminary study with mice as model

AIMS

To determine if nisin F has an effect on the bacterial population in the gastro-intestinal tract.

METHODS AND RESULTS

Six male C57BL/6 mice were intraperitoneally injected with 200 μl sterile saline and six with nisin F (200 μl, equivalent to 640 arbitrary units). Fecal samples were collected before injection and 8, 24 and 48 h after injection, and the bacteria amplified by PCR-DGGE using 16S rDNA primers. The composition of the bacterial population in the gastro-intestinal tract (GIT) of mice that were injected with saline changed during 48 h, whereas the bacterial population in the GIT remained relatively unchanged in animals injected with nisin F.

CONCLUSIONS

These results suggest that nisin F inhibits the growth of specific bacteria in the GIT within the first 4 h. Furthermore, the species remained repressed for at least 44 h after one intraperitoneal injection with nisin F.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first report suggesting that nisin F may have a stabilizing effect on the bacterial population in the gastro-intestinal tract.

Real and artificial immune systems: computing the state of the body

Here I present the idea that the immune system uses a computational strategy to carry out its many functions in protecting and maintaining the body. Along the way, I define the concepts of computation, Turing machines and system states. I attempt to show that reframing our view of the immune system in computational terms is worth our while.

A new approach of data clustering using a flock of agents

This paper presents a new bio-inspired algorithm (FClust) that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move together in a 2D environment with the aim of creating homogeneous groups of data. These groups are visualized in real time, and help the domain expert to understand the underlying structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data. We also present several extensions of this algorithm, which reduce its computational cost, and make use of a 3D display. This algorithm is then tested on artificial and real-world data, and a heuristic algorithm is used to evaluate the relevance of the obtained partitioning.

How do we evaluate artificial immune systems?

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of 'distinctiveness' and 'effectiveness.' In this paper, the standard types of AIS are examined--Negative Selection, Clonal Selection and Immune Networks--as well as a new breed of AIS, based on the immunological 'danger theory.' The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.