no code implementations • 31 Mar 2021 • Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim
Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics.
no code implementations • 12 Mar 2021 • Ahmed Ben Said, Abdelkarim Erradi
To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic.
no code implementations • 10 Sep 2020 • Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.
no code implementations • 2 Nov 2019 • Ahmed Ben Said, Abdelkarim Erradi
This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas.
no code implementations • 4 Sep 2018 • Ahmed Ben Said, Abdelkarim Erradi, Azadeh Ghari Neiat, Athman Bouguettaya
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally.