Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications
This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The au...
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| Veröffentlicht in: | International journal of ambient computing and intelligence 2013-07, Vol.5 (3), p.1-15 |
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| container_title | International journal of ambient computing and intelligence |
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| creator | Bersch, Sebastian Azzi, Djamel Khusainov, Rinat Achumba, Ifeyinwa E |
| description | This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work. |
| doi_str_mv | 10.4018/ijaci.2013070101 |
| format | Article |
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A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. 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A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work.</description><subject>Anomalies</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Assisted living facilities</subject><subject>Biological and medical sciences</subject><subject>Classification</subject><subject>Computer science; control theory; systems</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Data processing. List processing. Character string processing</subject><subject>Detectors</subject><subject>Exact sciences and technology</subject><subject>Immune system</subject><subject>Intelligence</subject><subject>Learning and adaptive systems</subject><subject>Literature reviews</subject><subject>Medical computing and teaching</subject><subject>Medical sciences</subject><subject>Memory organisation. 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| subjects | Anomalies Applied sciences Artificial intelligence Assisted living facilities Biological and medical sciences Classification Computer science control theory systems Computerized, statistical medical data processing and models in biomedicine Data processing. List processing. Character string processing Detectors Exact sciences and technology Immune system Intelligence Learning and adaptive systems Literature reviews Medical computing and teaching Medical sciences Memory organisation. Data processing Monitoring Nucleation Optimization Simulation methods Software |
| title | Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications |
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