Predicting Socio-economic Features for Indian States Using Satellite Imagery

Kherwa, Pooja; Ahlawat, Savita; Sobti, Rishabh; Mathur, Sonakshi; Mohan, Gunjan

doi:10.1007/978-981-15-5148-2_44

Cited by 1 publication

(1 citation statement)

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“…Besides the group-level baseline comparison, we also test the norm error readings with a few user-level prediction schemes. The first method in this queue has made use of probabilistic matrix factorization (PMF) [37,38]. The PMF is a variant of non-negative matrix factorization (NMF).…”

Section: Prediction Analysismentioning

confidence: 99%

Popularity Prediction of Social Media Post Using Tensor Factorization

Bohra¹,

Bhatnagar²,

Choudhary³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user's interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that is tensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation by choosing the top contents that were most like each other. The tensor is factorised with the application of the Adam optimization. It has been modified such that the bias is now included in the gradient function of A-PARAFAC, and the value of the bias is updated after each iteration. The prediction accuracy is improved by 32.25% with this strategy compared to other state of the art methods.

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Section: Prediction Analysismentioning

confidence: 99%