Artificial Immune Recognition System

Artificial Immune Systems (AIS) is an emerging bio-inspired computer science technique which embody the principles of biological immune systems for tackling complex real-world problems such as pattern recognition. Among the several immune-computing models, Artificial Immune Recognition System (AIRS) is one of the widely used for classification problems. Meanwhile, the issues related to writer identification are currently at the heart of numerous concerns in our modern day's society. Writer identification for Arabic text is receiving a renewed attention. Many popular machine learning techniques have been used in writer identification systems but only one limited attempt has been done with AIS. In this paper, we apply AIRS to perform Arabic writer identification based on a set of features extracted from Grey Level Co-occurrence Matrices. Some feature selection techniques are applied to improve computation time and accuracy results. Three traditional classifiers have also been used in our experiments for performance comparison. The obtained results show the promising ability of AIRS in Writer identification.

In this Paper, surveys, application, and comparison of three types of artificial
intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and
Artificial Immune Recognition System have been introduced.
Selecting the correct features is the most important thing in training and diagnosis
field and it is the core issue of this field, in this thesis, a trial is made to improve the
accuracies of the three proposed methods by trying to select the proper features from time
domain. The training is done by using the data collected from two-channel, horizontal
and vertical in three cases first, both time and frequency domains are used as features
input to the three proposed methods, secondly, using frequency domain only or thirdly,
using part of the time domain features with frequency domain features; for two speed. All
the three methods show excellent accuracy when training and diagnosis at same specific
speed especially SVM, while the accuracy is low when diagnosis at a speed that differs
from training speed. Also all the three methods give excellent diagnosis results when the
applied load at the same speed of training speed

Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.

In this Paper, surveys, application, and comparison of three types of artificial
intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and
Artificial Immune Recognition System have been introduced.
Selecting the correct features is the most important thing in training and diagnosis
field and it is the core issue of this field, in this thesis, a trial is made to improve the
accuracies of the three proposed methods by trying to select the proper features from time
domain. The training is done by using the data collected from two-channel, horizontal
and vertical in three cases first, both time and frequency domains are used as features
input to the three proposed methods, secondly, using frequency domain only or thirdly,
using part of the time domain features with frequency domain features; for two speed. All
the three methods show excellent accuracy when training and diagnosis at same specific
speed especially SVM, while the accuracy is low when diagnosis at a speed that differs
from training speed. Also all the three methods give excellent diagnosis results when the
applied load at the same speed of training speed.

Software testing is a time-consuming and expensive process. Software fault prediction models are used to identify fault-prone classes automatically before system testing. These models can reduce the testing duration, project risks, resource and infrastructure costs. In this study, we propose a novel fault prediction model to improve the testing process. Chidamber-Kemerer Object-Oriented metrics and method-level metrics such as Halstead and McCabe are used as independent metrics in our Artificial Immune Recognition System based model. According to this study, class-level metrics based model which applies AIRS algorithm can be used successfully for fault prediction and its performance is higher than J48 based approach. A fault prediction tool which uses this model can be easily integrated into the testing process. Inputs k PD ACC Prec G-1 G-2 F WMC 1 0.548 0.538 0.506 0.527 0.545 0.526 WMC 3 0.504 0.517 0.576 0.539 0.505 0.538 DIT 1 0.470 0.469 0.459 0.465 0.471 0.465 DIT 3 0.508 0.496 0.447 0.476 0.507 0.475 RFC 1 0.472 0.476 0.506 0.488 0.469 0.488 RFC 3 0.570 0.565 0.553 0.561 0.568 0.561 NOC 1 0.517 0.503 0.447 0.481 0.515 0.480 NOC 3 0.510 0.552 0.729 0.610 0.518 0.600 CBO 1 0.639 0.683 0.765 0.690 0.672 0.696 CBO 3 0.594 0.627 0.706 0.647 0.615 0.645 LCOM 1 0.520 0.524 0.541 0.530 0.520 0.530 LCOM 3 0.541 0.524 0.471 0.505 0.536 0.503 SLOC 1 0.558 0.545 0.506 0.532 0.553 0.531 SLOC 3 0.579 0.552 0.482 0.528 0.568 0.526 6 CK metrics 1 0.633 0.662 0.718 0.674 0.653 0.673 6 CK metrics 3 0.646 0.676 0.729 0.686 0.667 0.685 SLOC + 6 CK metrics 1 0.653 0.655 0.659 0.656 0.654 0.656 SLOC + 6 CK metrics 3 0.712 0.724 0.741 0.726 0.721 0.726 94 metrics 1 0.613 0.662 0.765 0.685 0.649 0.681 94 metrics 3 0.631 0.669 0.741 0.684 0.658 0.682 10 OO metrics 1 0.590 0.614 0.671 0.629 0.604 0.628 10 OO metrics 3 0.621 0.641 0.682 0.651 0.634 0.650 SLOC + 10 OO metrics 1 0.650 0.662 0.682 0.666 0.658 0.666 SLOC + 10 OO metrics 3 0.676 0.710 0.765 0.719 0.702 0.718 cfsSubsetEvaluated metrics 1 0.690 0.710 0.741 0.715 0.705 0.715 cfsSubsetEvaluated metrics 3 0.669 0.696 0.741 0.704 0.689 0.703 consistencySubsetEvaluated metrics 1 0.647 0.690 0.765 0.704 0.679 0.701 consistencySubsetEvaluated metrics 3 0.640 0.676 0.741 0.689 0.666 0.687 SLOC, WMC, RFC, NOC, CBO, LOCM 1 0.633 0.662 0.718 0.674 0.653 0.673 SLOC, WMC, RFC, NOC, CBO, LOCM 3 0.659 0.690 0.741 0.699 0.681 0.698 Fig. 2. Performance effect of each CK metrics for AIRS based model

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting preprocessing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

We have investigated the real-world task of recognizing biological concepts in DNA sequences in this work. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on feature selection (FS) and Artificial Immune Recognition System (AIRS) with Fuzzy resource allocation mechanism (Fuzzy-AIRS), which is first proposed by us. The aim of this study is to improve the prediction accuracy of Escherichia coli promoter gene sequences using a novel system based on FS and Fuzzy-AIRS. The E. coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 nonpromoters. The proposed system consists of two parts. Firstly, we have reduced the dimension of E. coli promoter gene sequences dataset from 57 attributes to 4 attributes by means of FS process. Second, Fuzzy-AIRS classifier algorithm has been run to predict the E. coli promoter gene sequences. The robustness of the proposed method is examined using prediction accuracy, sensitivity and specificity analysis, k-fold cross-validation method and confusion matrix. Whilst only Fuzzy-AIRS classifier has obtained 50% prediction accuracy using 10-fold cross-validation, the proposed system has obtained 90% prediction accuracy in the same conditions. These obtained results have indicated that the proposed system obtain the success rate in recognizing promoters in strings that represent nucleotides.

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.

Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.

Power distribution systems have been significantly affected by many fault causing events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many realworld data often degrades the outage cause identification performance. In this paper, artificial immune recognition system (AIRS), an immune-inspired algorithm for supervised classification task is applied to the Duke Energy outage data for outage cause identification using three major causes (tree, animal, and lightning) as prototypes. The performance of AIRS on these real-world imbalanced data is compared with an artificial neural network (ANN). The results show that AIRS can greatly improve the performance by as much as 163% when the data are imbalanced and achieve comparable performance with ANN for relatively balanced data.

In this Paper, surveys, application, and comparison of three types of artificial intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and Artificial Immune Recognition System have been introduced. Selecting the correct features is the most important thing in training and diagnosis field and it is the core issue of this field, in this thesis, a trial is made to improve the accuracies of the three proposed methods by trying to select the proper features from time domain. The training is done by using the data collected from two-channel, horizontal and vertical in three cases first, both time and frequency domains are used as features input to the three proposed methods, secondly, using frequency domain only or thirdly, using part of the time domain features with frequency domain features; for two speed. All the three methods show excellent accuracy when training and diagnosis at same specific speed especially SVM, while the accuracy is low when diagnosis at a speed that dif ers from training speed. Also all the three methods give excellent diagnosis results when the applied load at the same speed of training speed.

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital’s database using patient’s epicrisis reports.

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.

This study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonograms derived from Fast Fourier Transformation–Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully.

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student's t test, sensitivity Communicated by V. Loia.

Abstract-The method based on wavetlet singular entropy(WSE) and artificial immune recognition system (AIRS) for transmission line fault classification is presented in this paper. Wavelet singular entropy is used to quantify uncertainty of fault high frequency transient voltages so as to ...

In this paper, we have proposed a novel similarity-based weighting method (SBWM), which combines similarity measure and weighting based on trend association (WBTA) method proposed by Sun Yi et al. (ICNN&B international conference, vol 1, pp 266-269, 2005). The aim of this study is to improve the classification accuracy of atherosclerosis, which is a common disease among the public. The proposed method consists of three parts: (1) feature extraction part related with atherosclerosis disease using fast Fourier transformation (FFT) modeling and calculation of maximum frequency envelope of sonograms, (2) data pre-processing part using SBWM, including different similarity measures such as cosine amplitude method, max-min method, absolute exponential method, and exponential similarity coefficient, and (3) classification part using artificial immune recognition system (AIRS) and Fuzzy-AIRS classifier algorithms. While AIRS and Fuzzy-AIRS algorithms obtained 71.92 and 78.94% success rates, respectively, the combination of SBWM with classifier algorithms including AIRS and Fuzzy-AIRS obtained 100% success rate on all the similarity measures. These results show that SBWM has produced very promising results in the classification of atherosclerosis from carotid artery Doppler signals. In future, we will use a larger dataset to test the proposed method.

Lately, a lot of new illnesses are frequently obser ved in our societies, that it can be avoid by daily visits to the doctor. Cancer is one of these illnesses where patients discover it only when it is too late. In this work we propose an artificial Cancer diagnostic which can classify patients if they are affected by Cancer or

In this Paper, surveys, application, and comparison of three types of artificial
intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and
Artificial Immune Recognition System have been introduced.
Selecting the correct features is the most important thing in training and diagnosis
field and it is the core issue of this field, in this thesis, a trial is made to improve the
accuracies of the three proposed methods by trying to select the proper features from time
domain. The training is done by using the data collected from two-channel, horizontal
and vertical in three cases first, both time and frequency domains are used as features
input to the three proposed methods, secondly, using frequency domain only or thirdly,
using part of the time domain features with frequency domain features; for two speed. All
the three methods show excellent accuracy when training and diagnosis at same specific
speed especially SVM, while the accuracy is low when diagnosis at a speed that differs
from training speed. Also all the three methods give excellent diagnosis results when the
applied load at the same speed of training speed.

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has showed an effective and intriguing performance on the problems it was applied. Previously, AIRS was applied a range of problems including machinelearning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification problems. The conducted medical classification task was performed for ECG arrhythmia data taken from UCI repository of machine-learning. Firsly, ECG dataset is normalized in the range of [0,1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can be improved by ours, is a new method and firstly, it is applied to ECG dataset. Classifier system consists of three stages: 50-50% of traing-test dataset, 70-30% of traing-test dataset and 80-20% of traing-test dataset, subsequently, the obtained classification accuries: 78.79, 75.00 and 80.77%. q

This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose.

In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose.