Lıttle, James

Profile URL
https://hdl.handle.net/20.500.12416/9433
Name Variants
Little, J.
Job Title
Doç. Dr.
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Main Affiliation
Matematik
Status
Former Staff
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NO POVERTY1
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ZERO HUNGER2
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GOOD HEALTH AND WELL-BEING3
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QUALITY EDUCATION4
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GENDER EQUALITY5
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CLEAN WATER AND SANITATION6
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AFFORDABLE AND CLEAN ENERGY7
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DECENT WORK AND ECONOMIC GROWTH8
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
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REDUCED INEQUALITIES10
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SUSTAINABLE CITIES AND COMMUNITIES11
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
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CLIMATE ACTION13
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LIFE ON LAND15
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
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PARTNERSHIPS FOR THE GOALS17
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Scholarly Output

1

Articles

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5/0

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JournalCount
CEUR Workshop Proceedings -- 25th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2017 -- 7 December 2017 through 8 December 2017 -- Dublin -- 1357941
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Author of Publication: search.filters.isAuthorOfPublication.73fb2343-a551-4590-8e38-161146772f4c

Scholarly Output Search Results

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  • Conference Object
    Evaluation of Data Mining for Two Child-Related, Social Risk Issues
    (CEUR-WS, 2017) Lıttle, James; Little, J.; Waheed, H.A.; Rixon, A.; Matematik
    Two child-related social issues are examined using data mining to determine successful ways of predicting risk. The issues of child truancy and child abuse can be considered similar as both are influenced by, the child’s characteristics, family and environment. The results show that from an initial portfolio of algorithms, a one-nearest neighbour approach works well. We believe that reflects the nature of the problem, where expert opinion classifies each new pupil /case in terms of similar ones, while the one-nearest aspect, reflects the small amount of data we had access to. © 2017 CEUR-WS. All rights reserved.