Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena
In this assessment, we have made an effort of synthesis on the role of theoretical and observational investigations in the analysis of the concepts and functioning of different natural biological and artificial phenomena. In this context, we pursued the objective of examining published works relatin...
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| Format: | Article |
| Language: | English |
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Athens Institute for Education and Research
2021-09-01
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| Series: | Athens Journal of Health and Medical Sciences |
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| Online Access: | https://www.athensjournals.gr/health/2021-8-3-3-Razek.pdf |
| _version_ | 1797957491528564736 |
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| author | Adel Razek |
| author_facet | Adel Razek |
| author_sort | Adel Razek |
| collection | DOAJ |
| description | In this assessment, we have made an effort of synthesis on the role of theoretical and observational investigations in the analysis of the concepts and functioning of different natural biological and artificial phenomena. In this context, we pursued the objective of examining published works relating to the behavioral prediction of phenomena associated with its observation. We have examined examples from the literature concerning phenomena with known behaviors that associated to knowledge uncertainty as well as cases concerning phenomena with unknown and changing random behaviors linked to random uncertainty. The concerned cases are relative to brain functioning in neuroscience, modern smart industrial devices, and health care predictive endemic protocols. As predictive modeling is very concerned by the problematics relative to uncertainties that depend on the degree of matching in the link prediction-observation, we investigated first how to improve the model to match better the observation. Thus, we considered the case when the observed behavior and its model are contrasting, that implies the development of revised or amended models. Then we studied the case concerning the practice of modeling for the prediction of future behaviors of a phenomenon that is well known, and owning identified behavior. For such case, we illustrated the situation of prediction matched to observation operated in two cases. These are the Bayesian Brain theory in neuroscience and the Digital Twins industrial concept. The last investigated circumstance concerns the use of modeling for the prediction of future behaviors of a phenomenon that is not well known, or owning behavior varying arbitrary. For this situation, we studied contagion infections with an unknown mutant virus where the prediction task is very complicated and would be constrained only to adjust the principal clinical observation protocol. |
| first_indexed | 2024-04-11T00:04:54Z |
| format | Article |
| id | doaj.art-e3e8ea78464a4dc9893a4ddda22250c3 |
| institution | Directory Open Access Journal |
| issn | 2653-9411 |
| language | English |
| last_indexed | 2024-04-11T00:04:54Z |
| publishDate | 2021-09-01 |
| publisher | Athens Institute for Education and Research |
| record_format | Article |
| series | Athens Journal of Health and Medical Sciences |
| spelling | doaj.art-e3e8ea78464a4dc9893a4ddda22250c32023-01-09T13:09:38ZengAthens Institute for Education and ResearchAthens Journal of Health and Medical Sciences2653-94112021-09-018318920010.30958/ajhms.8-3-3Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena Adel Razek0Emeritus Research Director, C.N.R.S. & Honorary Professor, CentraleSupelec, GeePs, University of Paris-Saclay and Sorbonne University, FranceIn this assessment, we have made an effort of synthesis on the role of theoretical and observational investigations in the analysis of the concepts and functioning of different natural biological and artificial phenomena. In this context, we pursued the objective of examining published works relating to the behavioral prediction of phenomena associated with its observation. We have examined examples from the literature concerning phenomena with known behaviors that associated to knowledge uncertainty as well as cases concerning phenomena with unknown and changing random behaviors linked to random uncertainty. The concerned cases are relative to brain functioning in neuroscience, modern smart industrial devices, and health care predictive endemic protocols. As predictive modeling is very concerned by the problematics relative to uncertainties that depend on the degree of matching in the link prediction-observation, we investigated first how to improve the model to match better the observation. Thus, we considered the case when the observed behavior and its model are contrasting, that implies the development of revised or amended models. Then we studied the case concerning the practice of modeling for the prediction of future behaviors of a phenomenon that is well known, and owning identified behavior. For such case, we illustrated the situation of prediction matched to observation operated in two cases. These are the Bayesian Brain theory in neuroscience and the Digital Twins industrial concept. The last investigated circumstance concerns the use of modeling for the prediction of future behaviors of a phenomenon that is not well known, or owning behavior varying arbitrary. For this situation, we studied contagion infections with an unknown mutant virus where the prediction task is very complicated and would be constrained only to adjust the principal clinical observation protocol.https://www.athensjournals.gr/health/2021-8-3-3-Razek.pdfpredictionobservationbayesianneurosciencebrain functioningmutant virus |
| spellingShingle | Adel Razek Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena Athens Journal of Health and Medical Sciences prediction observation bayesian neuroscience brain functioning mutant virus |
| title | Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena |
| title_full | Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena |
| title_fullStr | Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena |
| title_full_unstemmed | Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena |
| title_short | Pertinence of Predictive Models as Regards the Behavior of Observed Biological and Artificial Phenomena |
| title_sort | pertinence of predictive models as regards the behavior of observed biological and artificial phenomena |
| topic | prediction observation bayesian neuroscience brain functioning mutant virus |
| url | https://www.athensjournals.gr/health/2021-8-3-3-Razek.pdf |
| work_keys_str_mv | AT adelrazek pertinenceofpredictivemodelsasregardsthebehaviorofobservedbiologicalandartificialphenomena |