Text classification framework for short text based on TFIDF-FastText

Kaur, Ravreet; Aggarwal, Preeti

doi:10.1007/s11042-023-15211-5

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Comparison With State-of-the-art1

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2023

2024

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Cited by 7 publications

(1 citation statement)

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“…As stated earlier, our proposed approach is based on Latent Semantic Analysis (LSA) to derive semantics of the non-functional attributes of an image service. We compare the performance of our LSA-based framework with a recently proposed approach named as TFIDF-FastText [39]. TFIDF-FastText classifies semantically similar short texts.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services

Umair,

Bouguettaya,

Lakhdari

2023

IEEE Trans. Serv. Comput.

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We propose a novel framework for crowdsourced images to determine the likelihood of an image being fake. We use a service-oriented approach to model and represent crowdsourced images uploaded on social media, as image services. Trust may, in some circumstances, be determined by using only the non-functional attributes of an image service, i.e., image metadata. We define intention of changes as a key parameter to ascertain fake image services. A novel framework is proposed to estimate the intention of underlying changes considering change in semantics of an image. Our experiments show high accuracy using a large real dataset.

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Section: Comparison With State-of-the-artmentioning

confidence: 99%