no code implementations • 5 Apr 2024 • Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhadi
In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets.
no code implementations • 5 Apr 2024 • Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhadi, Julia Handl
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs).
no code implementations • 27 Nov 2023 • Yuantao Fan, Zhenkan Wang, Sepideh Pashami, Slawomir Nowaczyk, Henrik Ydreskog
Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters.
no code implementations • 25 Aug 2023 • Zahra Taghiyarrenani, Abdallah Alabdallah, Slawomir Nowaczyk, Sepideh Pashami
In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client.
1 code implementation • 24 Aug 2023 • Jia Fu, Jiarui Tan, Wenjie Yin, Sepideh Pashami, Mårten Björkman
Motion analysis of improvised dance can be challenging due to its unique dynamics.
no code implementations • 8 Jun 2023 • Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski, Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi, Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed-Mouchaweh, Lala Rajaoarisoa, Grzegorz J. Nalepa, João Gama
We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 28 Feb 2022 • Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami, Thorsteinn Rögnvaldsson
In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
1 code implementation • 17 Nov 2021 • Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhad
Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets.
no code implementations • 7 Apr 2021 • Cristofer Englund, Eren Erdal Aksoy, Fernando Alonso-Fernandez, Martin Daniel Cooney, Sepideh Pashami, Bjorn Astrand
This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
no code implementations • 26 Jan 2021 • Pablo del Moral, Slawomir Nowaczyk, Anita Sant'Anna, Sepideh Pashami
In these datasets, the right hierarchy can dramatically improve classification performance.