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АСК-анализ, моделирование и идентификация живых существ на основе их фенотипических признаков1

АСК-анализ, моделирование и идентификация живых существ на основе их фенотипических признаков1

Abstract

Since there are many alternatives to artificial intelligence systems, there is a need of assessment of the quality of mathematical models and systems of artificial intelligence that support these models. This work is aimed at studying and developing standard methods of using the database of UCI repository to assess the quality of mathematical models of systems of artificial intelligence. The aim of this work is the development of methods for assessment of the quality of mathematical models of artificial intelligence systems for the classification of animals by external evidence-based database of the UCI repository. The objectives are: systematization, consolidation and expansion of theoretical and practical knowledge in the discipline of Intellectual information systems and technologies; study of "Eidos" intelligent information system; solving the task with the use of "Eidos" intelligent information systems. The object of research is the "zoo" database of UCI repository. In the first Chapter there is an overview of the theory to the solution of the problem, identification of problems, the original data, tools and metrization scales. In the second Chapter of the work we present the solution of the task. In the conclusion, the results of the work have been made; the conclusions on the achievement of goals and objectives have been given

Так как существует множество альтернатив систем искусственного интеллекта, то возникает необходимость оценки качества математических моделей и систем искусственного интеллекта, которые поддерживают эти модели. Данная работа направлена на изучение и разработку типовой методики использования базы данных репозитария UCI для оценки качества математических моделей систем искусственного интеллекта. Целью работы разработка методики оценки качества математических моделей систем искусственного интеллекта для классификации животных по внешним признакам на основе базы данных репозитария UCI. Задачами работы являются: систематизация, закрепление и расширение теоретических и практических знаний по дисциплине "Интеллектуальные информационные системы и технологии"; изучение интеллектуальной информационной системы "Эйдос"; решение поставленной цели с помощью интеллектуальной информационной системы "Эйдос". Объектом исследования является база данных "zoo" репозитария UCI. В первой главе работы происходит обзор теории к решению задачи, выявление проблематики, исходных данных, инструментария и метризации шкал. Во второй главе работы представлены решение поставленной задачи. В заключении приведены результаты работы, сделаны выводы по достижению поставленных целей и задач

Keywords

АСК-АНАЛИЗ МОДЕЛИ ИДЕНТИФИКАЦИЯ ЖИВЫЕ СУЩЕСТВА ФЕНОТИПИЧЕСКИЕ ПРИЗНАКИ

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold