Artificial Immune Recognition System

This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics.

With the sharp rise in software dependability and failure cost, high quality has been in great demand. However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on their development has become increasingly difficult, time and resource consuming activity. Consequently, it becomes inevitable to deliver software that have no serious faults. In this case, object-oriented (OO) products being the de facto standard of software development with their unique features could have some faults that are hard to find or pinpoint the impacts of changes. The earlier faults are identified, found and fixed, the lesser the costs and the higher the quality. To assess product quality, software metrics are used. Many OO metrics have been proposed and developed. Furthermore, many empirical studies have validated metrics and class fault proneness (FP) relationship. The challenge is which metrics are related to class FP and what activities are performed. Therefore, this study bring together the state-of-the-art in fault prediction of FP that utilizes CK and size metrics. We conducted a systematic literature review over relevant published empirical validation articles. The results obtained are analysed and presented. It indicates that 29 relevant empirical studies exist and measures such as complexity, coupling and size were found to be strongly related to FP.

This report is about the implementation of a visual tool for Differential Diagnosis of Erythemato-Squamous Diseases based on the classification algorithms; Nearest Neighbor Classifier (NN), Naive Bayesian Classifier using Normal Distribution (NBCN) and Voting Feature Intervals-5 (VFI5). This tool enables the doctors to perform all the necessary operations occurring in the dermatology department of a hospital.

It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data.

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS 2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS 2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS 2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

This paper presents the bond graph model of a vibrating ,rotating shaft with disc mounted on it. Unbalance mass fitted to the disc cause the disc to vibrate. The bond Graph technique has been used to model this vibrating system and SYMBOL Shakti software to simulate the model and determine the frequencies and their corresponding amplitudes.

In software engineering fault proneness prediction is one of the important fields for quality measurement using multiple code metrics. The metrics thresholds are very practical in measuring the code quality for fault proneness prediction. It helps to improvise the software quality in short time with very low cost. Many researchers are in the race to develop a measuring attribute for the software quality using various methodologies. Currently so many fault proneness prediction models are available. Among that most of the methods are used to identify the faults either by data history or by special supervising algorithms. In most of the real time cases the fault data bases may not be available so that the process becomes tedious. This article proposes a hybrid model for identifying the faults in the software models and also we proposed coupling model along with the algorithm so that the metrics are used to identify the faults and the coupling model couples the metrics and the faults for the developed system software.

Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the precise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with publicly available datasets from the Promise repository and object-oriented metrics from an Eclipse repository related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocessing 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.

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This article proposes a new classifier inspired on a biological immune systems' characteristic. This immune based predictor also belongs to the class of k-nearest-neighbors algorithms. Nevertheless, its main features, compared to other artificial immune classifiers, are the assumption that training set is the antibodies' population and a suppression mechanism that tries to reduce the training set into a smaller subset. This subset is supposed to contain the most significative samples, without loosing much capability of generalization. It is known that in prediction problems, the choice of a good training set is crucial for the classification process. And this is the focus of this research. Experiments using some benchmarks and the analysis of the results of our ongoing work are presented.

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. Objectives: This study is aimed at diagnosing TB using hybrid machine learning approaches. Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Results: Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Analysis of anomalies reported during testing of a project can tell a lot about how well the processes and products work. Still, organizations rarely use anomaly reports for more than progress tracking although projects commonly spend a significant part of the development time on finding and correcting faults. This paper presents an anomaly metrics model that organizations can use for identifying improvements in the development process, i.e. to reduce the cost and lead-time spent on rework-related activities and to improve the quality of the delivered product. The model is the result of a four-year research project performed at Ericsson.

This study analyzes the effectiveness of an Artificial Immune System (AIS) to model and predict the movements of the stock market. To aid in this research the AIS models are compared with a k-Nearest Neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. The analysis shows that the AIS produced overall accuracy results of 67% over a 20 year test period and that the increased complexity of the model was warranted by the statistically significant superior results when compared to the simpler instancebased approach of kNN. The accuracy results were comparable to those obtained from training the ANN and the trading results outperformed the market benchmark, providing evidence that the stock market had a degree of predictability during the time period of 1989-2008. In general the practice of using the natural immune system to inspire a learning algorithm has been established as a viable alternative to modeling the stock market when implementing a supervised learning approach.

The common mechanical defect of rotating machinery is bearing failure which is considered the most common failure mode in rotating machinery. This kind of failure can lead to large losses as financial during work. Early detection of different faults in rotating machines such as bearing fault, misalignment, and others is considered one of the techniques in which is achieved by further signal processing techniques. Thus, using statistical methods such as reverse arrangement tests (RAT) to obtain the best a feature associated with these different faults is the perfect solution to find the failure which is widespread in the early detection of a fault. This type of feature will be used in Artificial Neural networks (ANN) as input for auto diagnosis. These characteristics are independently associated with different types of fault. Using RAT is considered very important in the process of linking different kinds of failures with the most important features.

Software faults can cause trivial annoyance to catastrophic failures. Recent work in software fault prediction (SFP) advocates the need for predicting faults before deployment to aid testing process. Object-oriented programming is complex while comparing it with procedural languages having multiple dimensions wherein inheritance is an important aspect. In this paper, we aim to investigate how much inheritance metrics assist in predicting software fault proneness. We first select the Chidamber and Kemerer (CK) metrics, most accepted metric suite for predicting software faults and inheritance metrics. We use 65 publicly available base datasets having CK metrics and some other inheritance metrics to evaluate the impact of inheritance on SFP. We split each dataset into further two datasets: inheritance with CK and CK without inheritance for comparison of results. An artificial neural network (ANN) is used for model building, and accuracy, recall, precision, F1 measures, and true negative rate (TNR) are used for measuring performance. Comparison is made and the results show an acceptable contribution of inheritance metrics in SFP. The testing community can safely use inheritance metrics in predicting software faults. Moreover, high inheritance is not desirable, as this can potentially lead to software faults.

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.

The monitoring of machine conditions in a plant is crucial for production in manufacturing. A sudden failure of a machine can stop production and cause a loss of revenue. The vibration signal of a machine is a good indicator of its condition. This paper presents a dataset of vibration signals from a lab-scale machine. The dataset contains four different types of machine conditions: normal, unbalance, misalignment, and bearing fault. Three machine learning methods (SVM, KNN, and GNB) evaluated the dataset, and a perfect result was obtained by one of the methods on a 1fold test. The performance of the algorithms is evaluated using weighted accuracy (WA) since the data is balanced. The results show that the best-performing algorithm is the SVM with a WA of 99.75% on the 5fold cross-validations. The dataset is provided in the form of CSV files in an open and free repository at https://zenodo.org/record/7006575.

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.

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, 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.

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. Objectives: This study is aimed at diagnosing TB using hybrid machine learning approaches. Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Results: Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Change is inevitable and an important property of software. Software applications are changed during their life-time in order to remain useful. Nonetheless, changes also come with high risks when it is made. Regardless of their size, they can have significant and unexpected effects elsewhere in the software, degrade software quality or cause them to fail. Change impact analysis is used to preserve software quality. Today, as object-oriented technology has gained worldwide popularity, several object-oriented software applications are currently in use. Given the critical context, it is important that these systems are effectively and efficiently maintained if continuous usefulness is the goal. However, object-oriented paradigm introduces specific features, have different change and complex dependencies types often which makes it hard to identify the impact of changes or it is likely that they might introduce some types of faults which are difficult to detect. In addition, the availabl...

Software faults can cause trivial annoyance to catastrophic failures. Recent work in software fault prediction (SFP) advocates the need for predicting faults before deployment to aid testing process. Object-oriented programming is complex while comparing it with procedural languages having multiple dimensions wherein inheritance is an important aspect. In this paper, we aim to investigate how much inheritance metrics assist in predicting software fault proneness. We first select the Chidamber and Kemerer (CK) metrics, most accepted metric suite for predicting software faults and inheritance metrics. We use 65 publicly available base datasets having CK metrics and some other inheritance metrics to evaluate the impact of inheritance on SFP. We split each dataset into further two datasets: inheritance with CK and CK without inheritance for comparison of results. An artificial neural network (ANN) is used for model building, and accuracy, recall, precision, F1 measures, and true negative rate (TNR) are used for measuring performance. Comparison is made and the results show an acceptable contribution of inheritance metrics in SFP. The testing community can safely use inheritance metrics in predicting software faults. Moreover, high inheritance is not desirable, as this can potentially lead to software faults.

Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% ef...

This article characterizes vibration signals using Artificial Neural Network (ANN) method to develop an effective and reliable feature sets for detecting and diagnosing faults in a centrifugal pump (ETRR-2 research reactor core coolant pumps). In this paper, a modular ANN are used for modeling the ETRR-2 research reactor core coolant pumps vibration monitoring. The input and the output signals of the vibration transducer are used as input source data for the ANN model. The amplitudes and frequency domain are inputted to the ANN separately from the vibration data. It is noted that the features statistical have good results based on frequency and amplitudes domains. The ANNs are used to detect the misalignment, unbalance severity, looseness bearing, and anti-fraction. The results are very useful for maintenance making decision. This methodology can be used for the research reactor coolant pumps. Hence, it may turn out to be a powerful tool for early detection of pump fault.

On the basis of analyzing immune learning mechanism, by modeling for image classification, we can solve the problem of remote sensing image classification by using the basic principles of the use of immune learning. We have realized a classification algorithm with a function of the immune learning. Classification algorithm divides each major category into a number of small categories and the antigen population evolutionary process of each category is considered separately, therefore the convergence time is greatly decreased. When classifying, we use a variety of different ways to discriminate and introduce artificial priori knowledge to improve the classification accuracy. The results show that the algorithm can be well applied in remote sensing image classification.

Acoustic Emission (AE) is being extensively used as a Non Destructive Technique (NDT) for diagnosis of rotating components. The main theory behind this diagnosis is that while rotation, acoustic energy level of defective portion in rotating element is different. In the present study a signal processing technique is proposed to identify the variation in acoustic signal and results are verified for misalignment of the bearing shaft. Two different functions Fast Fourier Transformation (FFT) and Decomposition are used to represent the Acoustic signal at different levels of misalignment. Result reveals that the proposed methods are effective in estimating the misalignment present in the bearing shaft. Statistical parameters such as Standard deviation and Shannon entropy are also increases with increase in misalignment in the bearing shaft.

Software faults can cause trivial annoyance to catastrophic failures. Recent work in software fault prediction (SFP) advocates the need for predicting faults before deployment to aid testing process. Object-oriented programming is complex while comparing it with procedural languages having multiple dimensions wherein inheritance is an important aspect. In this paper, we aim to investigate how much inheritance metrics assist in predicting software fault proneness. We first select the Chidamber and Kemerer (CK) metrics, most accepted metric suite for predicting software faults and inheritance metrics. We use 65 publicly available base datasets having CK metrics and some other inheritance metrics to evaluate the impact of inheritance on SFP. We split each dataset into further two datasets: inheritance with CK and CK without inheritance for comparison of results. An artificial neural network (ANN) is used for model building, and accuracy, recall, precision, F1 measures, and true negative rate (TNR) are used for measuring performance. Comparison is made and the results show an acceptable contribution of inheritance metrics in SFP. The testing community can safely use inheritance metrics in predicting software faults. Moreover, high inheritance is not desirable, as this can potentially lead to software faults.

Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis's software metrics used to measure the software product and process. In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.

A new approach based on the implementation of Self Organizing Map is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is in order to determinate the severity of erythematosquamous dermatology diseases. The studied domain contained records of patients with known diagnosis. Self-Organizing Map algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the six clusters (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, dan pityriasis rubra pilaris). The algorithm was used to detect the six erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and SOM algorithm's task achieved high classification accuracies. The best accuration for psoriasis 85

Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of Machine Learning (ML) techniques for fault prediction. However most used ML techniques can not deal with unbalanced data and their results usually have a difficult interpretation. Because of this, this paper introduces a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for fault prediction. It allows the creation of classifiers composed by rules with specific properties by exploring Pareto dominance concepts. These rules are more intuitive and easier to understand because they can be interpreted independently one of each other. Furthermore, an experiment using the approach is presented and the results are compared to the other techniques explored in the area.

A strategy of diagnosing of memory modules implemented using expert system with indistinct rules raises the degree of automation of choice procedure of the effective tests from allowable set. The process of diagnosing adapts for features of tested object, thus the most probable types of malfunctions are taken into account and the most effective tests are applied to their covering.

system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-meansquare error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

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.

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. Objectives: This study is aimed at diagnosing TB using hybrid machine learning approaches. Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Results: Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

This position paper supports the use of Artificial Immune System (AIS) in the area of Ambient Assisted Living (AAL). While AIS has been used for anomaly detection and classification in a wide range of applications, little work has been done on using AIS for detecting abnormal behaviour in health monitoring applications. In this paper, we propose to use AIS for fall detection, since falls can be seen as deviations from the normal behaviour. We justify our proposal by analysing research that has been carried out in the past using AIS in different fields and emphasising on the similarities to the area of AAL. The paper also describes the experimental setup that is currently being used for our current and future work.

Depuis une dizaine d'années, plusieurs méthodes de traitement du signal vibratoire ont été développées pour le diagnostic des machines tournantes en régime stationnaire. Or, de plus en plus de machines sous surveillance fonctionnent en régime variable telles que les éoliennes, les concasseurs, etc... Les méthodes développées pour la surveillance et le diagnostic de ces machines en régime stationnaire ne sont plus adaptées en régime variable. Certains outils proposés pour le régime variable sont limités à des applications bien spécifiques ou offrent un cadre théorique qui limite leur utilisation dans les situations réelles. Le but de cette thèse est justement de pallier ces limitations, et ce, en proposant une nouvelle approche permettant d'analyser les signaux vibratoires acquis en régime variable. La stratégie mise en oeuvre dans cette thèse repose sur une modélisation du signal vibratoire dans l'espace d'état et une estimation H∞ des grandeurs caractéristiques de l...

Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification.

Artificial Immune Systems (AIS) is an emerging computer science technique which is inspired from biological process and has a nonlinear classification property along with biological property such as Negative Selection (NS). AIS has been proved as one of the mechanism that has a good potential in solving complex problem such as pattern recognition. Meanwhile, handwritten writer identification is one of the most popular areas of research in pattern recognition due to its immense potential on commercial perspective. Due to the importance and increasing interest in bio-inspired computing, this paper proposed AIS framework for writer identification. However, early findings on data iris and pendigits show that the proposed framework is convincing with an accuracy of more than 80.0% with various matching techniques.

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.

Background: Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the 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. Objectives: In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patients and Methods: Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. Results: With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. Conclusions: There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.

In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system.

This study analyzes the effectiveness of an Artificial Immune System (AIS) to model and predict the movements of the stock market. To aid in this research the AIS models are compared with a k-Nearest Neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. The analysis shows that the AIS produced overall accuracy results of 67% over a 20 year test period and that the increased complexity of the model was warranted by the statistically significant superior results when compared to the simpler instancebased approach of kNN. The accuracy results were comparable to those obtained from training the ANN and the trading results outperformed the market benchmark, providing evidence that the stock market had a degree of predictability during the time period of 1989-2008. In general the practice of using the natural immune system to inspire a learning algorithm has been established as a viable alternative to modeling the stock market when implementing a supervised learning approach.