Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object has been chosen. Improving the traditional controller or constructing the new controller is an unfading study field of control theory and application. The control system is greatly enriched and developed by this way. As a complicated self-adaptable system, the biological immune system can effectively and smoothly stand against antigens and viruses intruded into organism. It is possible to improve the self-learning, adaptive and robustness capability of the control system through embedded an artificial immune controller in control system. Based on the biological immune mechanism and artificial immune model, this chapter attempts to study the immune controller design and application in traditional control system..First, a kind of artificial immune controller is proposed based on the T-B cells immunity. The boundedness and the stability of SISO control systems, which constructed by the artificial immune controller, are proved by the little gain theorem. A general controller structure frame based on the T-B cells immunity is proposed, which includes the same kind of controller proposed previously. The validity of this artificial immune controller is verified by simulation. Second, a new type of artificial immune controllers is constructed according to a simple double-cell immune dynamics model. The non-error characteristic of SISO control systems, which constructed by the artificial immune controller, is proved by the nonlinear theory in this chapter. The I/O stability and no-error characteristic of the system are verified by simulations, which show that the kind of artificial immune system have good anti-lag capability. Third, the Varela immune network model has been improved based on which an artificial immune system is proposed. The odd linearization method of the non-linear system is used to prove the stability and non-error characteristic of the SISO system constructed by the artificial immune control system. Its I/O stability, non-error characteristic and strong anti-lag capability are also verified by simulation. Finally, based on the comparison of the three kinds of immune controllers, a general structure of the artificial immune controller is proposed. The further study on this field is indicated in this chapter lastly.

Suggests that immune algorithms can be used effectively for the tuning of PID control structures for nonlinear processes. The controller's attribute behavior mechanism in the plant and the artificial immune system have certain similarities, since both systems deal with various attribute inputs and outputs through interactions among multiple-attribute modules. Since antibodies communicate with each other among different species of antibodies/B-cells through the stimulation and suppression chains among the antibodies that form a large-scale network, the artificial immune network system always has a new parallel decentralized processing mechanism for various situations. In addition to that, the structure of the network is not fixed but varies continuously, i.e. the artificial immune network flexibly self-organizes according to dynamic changes of the external environment. On the other hand, a number of tuning methods on the PID controller have been considered but, with only the P, I and D parameters, it is very difficult to control a plant with complex dynamics, such as large dead time, inverse response and highly nonlinear characteristics. An possibility of applying the flexible arbitration abilities of an artificial immune network has been suggested for the PID controller tuning. Simulation results reveal that immune network algorithms are effective for searching for optimal control against disturbances.

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

Most primary headaches are classified into a few categories, such as migraine or muscle contraction headache, and patients suffering from these headaches are common. On the other hand, other primary headaches are very rare. In this section entitled "Other primary headaches", eight headaches, including primary stabbing headache, primary cough headache, primary exertional headache, primary headache associated with sexual activity, hypnic headache, primary thunderclap headache, hemicrania continua, and new daily-persistent headache, are described. Some characteristics of other primary headaches are common in symptomatic headaches, such as subarachnoid hemorrhage or arterial dissection. Therefore, careful evaluations including neuroimaging are necessary to exclude organic diseases.

This paper studies the structural damage detection and classification problems by using the artificial immune system which has the extremely powerful capabilities of adaptive and the bionic principle between learning and memory. We proposed an artificial immune pattern recognition and structural detection classification algorithm through imitating the immune recognition and learning mechanism. With the structure of benchmark, the damage detection and classification are tested. The simulation results show the classification rate is very well. The algorithm based on the immune learning and evolution can produce the high quality memory cells which effectively identify all kinds of structural damage model.

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism

The Artificial Immune System (AIS) is a biologically inspired computation system based on specifically human immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimisation and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This article after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.

Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research.

A control chart is a tool for statistical process control (SPC) used to determine variations in manufacturing processes resulting from common or assignable causes. The presence of unnatural patterns in control charts is an indication that the process has been influenced by assignable causes, and corrective actions must be taken. However, the assumption of uncorrelated or independent observations is not suitable for all of the characteristics pertaining to a product. This study presents a method with which to improve the accuracy of pattern recognition in control charts, through the autocorrelation of product characteristics. Particularly, we developed an artificial algorithm-based machine learning model to recognize unnatural patterns and processes in AR(1) simultaneously. The proposed method integrates an artificial immune system and support vector machine within a recognition system. This study evaluated the accuracy with which four patterns could be recognized. The four patterns include trends, sudden shifts, cyclic patterns, and normal patterns. Identifying unnatural patterns can greatly narrow the range of possible causes that must be investigated, thereby speeding up the diagnostic processes. Key words: Pattern recognition, artificial immune algorithm, support vector machine, autocorrelation.

Introduction:Headache is the most common neurologic symptom among referees to the emergency department (ED), while the best treatment has not yet been found. Therefore, in the present study pain relief effects of metoclopramide and acetaminophen were compared in patients suffered acute primary headache. Methods: This study was a double-blind randomized clinical trial performed in Imam Khomeini Hospital, Urmia, Iran, through July to October 2014. All adult patients, with acute primary (migraine, tension type and cluster) headache referred to the ED were included in this study. Pain severity was measured with 10 centimeters numeric rating scales. The patients were randomized into two groups of intravenous (IV) metoclopramide (10 milligrams) and acetaminophen (1 gram). Pain score, success rate, and drug complications were compared between the 2 groups at 0, 15, 30, 60, and 120 minutes after injection. Results: 100 patients were equally categorized into two groups (mean age of 32 ± 13.2 years; 51.2% male). Initial pain score in metoclopramide and acetaminophen groups were 9.1 and 9.4, respectively (p = 0.46). IV metoclopramide did not have any analgesic effect at 15 minutes, but had good effect at 30 minutes. While, the analgesic effect of acetaminophen initiated after 15 minutes. After 2 hours, both drugs had good therapeutic effect on primary headaches (p < 0.001). Conclusion:The present study demonstrated that efficacy of metoclopramide for pain relief in primary headaches is lower than acetaminophen. In this regard, success rate of acetaminophen was 42.0% versus 0% for metoclopramide within 15 minutes. The efficacy of acetaminophen continued until 60 minutes.

The Future of Migraine: Beyond Just Another Pill

This work focuses on an aspect of artificial immune systems not yet addressed by research, and demonstrates that as immune system metaphors grow and evolve in complexity, overall network health can play a role in the vulnerability and strength of the network. Recent works have suggested the analogous role of the immune system to computer security systems. As artificial immune system research progresses, a growing number of components of the human immune system have been incorporated, raising opportunities for further research and understanding. In particular, this work focuses on the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS) in artificial immune systems. CRS stems from a comparison of the overall strength of the host entity of an immune system to individual agent strength, this case in an artificial immune system (AIS). This principle has been employed to facilitate selection in a model of an AIS. Using the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS), this research introduces this concept.