Explication of Dio 3 -Tuples from Truncated Octahedral Number-I

Saranya, Perumal

doi:10.22214/ijraset.2018.5147

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Validating Trends With Ground Truth1

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2020

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“…The assumption is that food price changes should be positivelycorrelatedwithconversationstrendsonfoodinsecurity.Duetodifferencesinvalueranges (insecuritycountsandfoodpricevalues)thetwodatasetswerenormalizedtocreatesomeuniformity in graphic visualization. Saranya & Manikandan (2013) offers three techniques of normalizing: Z-score,Decimalscalingnormalization,andMin-Max.InthisstudyMin-Maxwasadoptedbecause itisalinearandasimpletechniquetoimplement.Usingthistechnique,datasetswerefittedinto pre-definedboundary.Inthisstudy,[0,1]boundarywaschosen.…”

Section: Validating Trends With Ground Truthmentioning

confidence: 99%

Towards Ensemble Learning for Tracking Food Insecurity From News Articles

Lukyamuzi

Ngubiri

Okori

2020

International Journal of System Dynamics Applications

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The study integrates ensemble learning into a task of classifying if a news article is on food insecurity or not. Similarity algorithms were exploited to imitate human cognition, an innovation to enhance performance. Four out of six classifiers generated performance improvement with the innovation. Articles on food insecurity identified with best classifier were generated into trends which were comparable with official trends. This paper provides information useful to stake holders in taking appropriate action depending on prevailing conditions of food insecurity. Two suggestions are put forth to promote performance: (1) using articles aggregated from several news media and (2) blending more classifiers in an ensemble.

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Section: Validating Trends With Ground Truthmentioning

confidence: 99%