1 code implementation • 10 Mar 2023 • Rodi Laanen, Maedeh Nasri, Richard van Dijk, Mitra Baratchi, Alexander Koutamanis, Carolien Rieffe
However, to date, few studies have investigated the performance of different localisation systems regarding the classification of human movement patterns in small areas.
no code implementations • 20 Jan 2022 • Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani
The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e. g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets.
1 code implementation • 11 Feb 2021 • Nuno Cesar de Sa, Mitra Baratchi, Leon T. Hauser, Peter van Bodegom
We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN).
1 code implementation • 2 Jun 2020 • Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen
As the flexibility of our model class comes at the cost of a vast search space, we introduce a heuristic algorithm, named PALM, which Partitions each dimension ALternately and then Merges neighboring regions, all using the MDL principle.
no code implementations • 22 May 2020 • Laurens Arp, Dyon van Vreumingen, Daniela Gawehns, Mitra Baratchi
This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion.
Physics and Society Multiagent Systems I.2.1; I.5.4
1 code implementation • 24 Jan 2020 • Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani
Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem.
1 code implementation • 14 Sep 2019 • Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani
To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.
no code implementations • 31 Jul 2019 • Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani
With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation.
no code implementations • 29 Mar 2017 • Mitra Baratchi, Geert Heijenk, Maarten van Steen
In this paper, we address the problem of how automated situation-awareness can be achieved by learning real-world situations from ubiquitously generated mobility data.