Search Results for author: Sarkar Snigdha Sarathi Das

Found 5 papers, 2 papers with code

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

no code implementations20 Nov 2020 Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali

Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features.

Recommendation Systems

BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data

no code implementations2 Nov 2020 Sarkar Snigdha Sarathi Das, Subangkar Karmaker Shanto, Masum Rahman, Md. Saiful Islam, Atif Rahman, Mohammad Mehedy Masud, Mohammed Eunus Ali

The increasing popularity of smartwatches as affordable and longitudinal monitoring devices enables us to capture photoplethysmography (PPG) sensor data for detecting Atrial Fibrillation (AF) in real-time.

Atrial Fibrillation Detection Photoplethysmography (PPG)

Boosting House Price Predictions using Geo-Spatial Network Embedding

1 code implementation1 Sep 2020 Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali, Yuan-Fang Li, Yong-Bin Kang, Timos Sellis

Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.

Network Embedding

CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting

no code implementations12 Dec 2019 Sarkar Snigdha Sarathi Das, Syed Md. Mukit Rashid, Mohammed Eunus Ali

In this paper, we introduce a new variant of crowd counting problem, namely "Categorized Crowd Counting", that counts the number of people sitting and standing in a given image.

Crowd Counting

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