Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

There has been a rapid growth in using Artificial Immune Systems for applications in data mining and computational intelligence recently. There are extensive computational aspects with the natural immune system. Several algorithms have been developed by exploiting these computational capabilities for a wide range of applications. Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. The results achieved by Artificial Immune Recognition Systems have shown the potential of Artificial Immune Systems to perform classification. Artificial Immune Recognition System is a relatively new classifier and has some advantages such as self regularity, parameter stability and data reduction capability. However, the Artificial Immune Recognition System uses a linear resource allocation method. This linearity increases the processing time of generating memory cells from antigens and causes an increase in the training time of the Artificial Immune Recognition System. Another problem with the Artificial Immune Recognition System is related to the resource competition phase which generates premature memory cells and decreases the classification accuracy of system. This thesis proposes new algorithms based on Artificial Immune Recognition System to address the mentioned weaknesses and improve the performance of the Artificial Immune Recognition System. Firstly, exponential-based resource allocation methods are utilized instead of the existing linear resource allocation method. Next, the Real World Tournament Selection method is adapted and incorporated into the resource competition of Artificial Immune Recognition System. The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. The experimental results show that utilizing exponential-based resource allocation methods decreases the training time and increases the data reduction capability of Artificial Immune Recognition System. In addition, incorporating an adapted Real World Tournament Selection technique increases the accuracy of the Artificial Immune Recognition System up to 4%. The difference between the performances of the proposed algorithms and Artificial Immune Recognition System are significant in majority of cases

The effect of noise on RWTSAIRS classifier.

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (apparently better) individuals. However, with this type of selection, there is a high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In a previous study, the Real World Tournament Selection (RWTS) method was incorporated into the resource competition phase of AIRS to prevent this problem. The new classifier, named RWTSAIRS, obtained higher accuracy than AIRS in standard datasets from UCI machine learning repository. Real-world data is not perfect and contains noise that may impact the models created from data and decision made based on data. In this study, the performance of RWTSAIRS is evaluated in noisy environments. For this purpose, class and attribute noise are injected into some datasets

An efficient and effective immune based classifier

Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier

Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (seemly good) individuals. This type of selection has high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In this study, the Real World Tournament Selection (RWTS) method is incorporated in resource competition phase of AIRS to prevent this issue and experiments are conducted to evaluate the accuracy of new algorithm (RWTSAIRS). The combination of cross validation and t test is used as evaluation method. Algorithms tested on benchmark datasets of the UCI machine learning repository show that RWTSAIRS obtained higher accuracy than AIRS in all cases and that the difference between accuracies of two algorithms was significant in majority of cases

Artificial immune recognition system with nonlinear resource allocation method and application to traditional Malay music genre classification

Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems. In this study, the resource allocation method of AIRS was changed with a nonlinear method. This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification. Music genre classification has a great important role in music information retrieval systems nowadays. The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification

Effect of fuzzy resource allocation method on AIRS classifier accuracy

Artificial Immune Recognition System (AIRS) is immune inspired classifier that competes with famous classifiers. Many researches have been conducted to improve the accuracy of AIRS and to investigate the source of power of AIRS. Some of these researches have focused on resource allocation method of AIRS.This study investigates the difference between the accuracy of AIRS with fuzzy resource allocation and the accuracy of original AIRS, by using the reliable statistical method. The combination of ten fold cross validation and t-test was used as evaluation method and algorithms tested on ten benchmark datasets of UCI machine learning repository. Based on the results of experiments, using fuzzy resource allocation increases the accuracy of AIRS in majority of datasets but the increase is significant in minority of datasets

COMPARISON OF DIFFERENT MEDIA TO PRODUCE CYMBIDIUM ORCHIDS BY PSEUDOBULBS

Nowadays, Orchids are one of the most commercial products in flower markets. One of the propagation methods for Cymbidium is using old pseudobulbs that are thrown out after flowering period. This research carried out using standard Cymbidium back-bulbs based on randomized complete block design with 5 treatments in 3 replications. The trial traits were leaf length, root length, leaf number and root number that were studied for 180 days. The results show that minimum length of root was significant under different growth beds. The minimum percent of rooting was observed in pure sand treatment. The maximum length was observed in pure perlite. The shortest of leaves were gained in perlite + sand treatment and the maximum leaf length was observed in pure perlite treatment. The maximum average of root percent was seen in pure perlite treatment

Effect of nonlinear resource allocation on AIRS classifier accuracy

Artificial Immune Recognition System (AIRS) is most popular immune inspired classifier.It also has shown itself to be a competitive classifier.AIRS uses linear method to allocate resources.In this paper, two different nonlinear resource allocation methods apply to AIRS. Then new algorithms are tested on 8 benchmark datasets.Based on the results of experiments, one of them increases the accuracy of AIRS in the majority of cases

A hybrid approach to traditional Malay music genre classification: combining feature selection and artificial immune recognition system

Music genre classification has a great important role in music information retrieval systems. In this study we propose hybrid approach for Traditional Malay Music (TMM) genre classification. The proposed approach consists of three stages: feature extraction, feature selection and classification with Artificial Immune Recognition System (AIRS). The new version of AIRS is used in this study. In Proposed algorithm, the resource allocation method of AIRS has been changed with a nonlinear method. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation. This accuracy is maximum accuracy among the classifiers used in this study

The Effect of a Social Stories Intervention on the Social Skills of Male Students With Autism Spectrum Disorder

The present study aimed to investigate the effect of a social stories intervention on the social skills of male students with autistic spectrum disorder (ASD). The sample included 30 male students with ASD who were selected through convenience sampling and randomly assigned to an experimental group (n = 15) or a control group (n = 15). The social skills of both groups were assessed pre- and post-test using Stone and colleagues’ Social Skills Scale (which included subscales for understanding/perspective-taking, initiating interactions, responding to interactions, and maintaining interactions). The experimental group participated in 16 sessions of social stories training, while the control group did not. Overall, the results showed that the social stories intervention improved the social skills of the children with ASD in the experimental group compared with the control group. The effects of the social stories intervention were mostly evident in the subscales for understanding/perspective-taking, initiating interactions, and maintaining interactions with others. The social stories intervention had no effect on the subscale assessing ability to respond to others. The study findings emphasize the effectiveness of the social stories intervention in improving the social skills of children with ASD, which may be used by teachers, parents, or professionals who work with such children