no code implementations • 27 Oct 2023 • Ren Ozeki, Haruki Yonekura, Aidana Baimbetova, Hamada Rizk, Hirozumi Yamaguchi
Experimental results demonstrate the effectiveness of the proposed system in accurately forecasting taxi demand, even in previously unobserved regions, thus showcasing its potential for optimizing taxi services and improving transportation efficiency on a broader scale.
no code implementations • 30 Jul 2023 • Kaede Shintani, Hamada Rizk, Hirozumi Yamaguchi
With the increasing number of IoT devices, there is a growing demand for energy-free sensors.
no code implementations • 3 Jun 2023 • Mohamed Mohsen, Hamada Rizk, Moustafa Youssef
Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services.
no code implementations • 14 May 2023 • Yumeki Goto, Tomoya Matsumoto, Hamada Rizk, Naoto Yanai, Hirozumi Yamaguchi
Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services.
no code implementations • 17 Mar 2023 • Masakazu Ohno, Riki Ukyo, Tatsuya Amano, Hamada Rizk, Hirozumi Yamaguchi
In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views.
no code implementations • 26 Apr 2022 • Betty Lala, Hamada Rizk, Srikant Manas Kala, Aya Hagishima
To the best of our knowledge, this work is the first application of Multi-task Learning to thermal comfort prediction in classrooms.