26 citations · 87 across the 16 of their papers we have counts for
17 papers
Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics
Abhishek Srinivasan, Juan Carlos Andresen, Sepideh Pashami +1
Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models that capture cumulative degra…
A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles
Maria Bånkestad, Sandra Barman, Magnus Röding +11
Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray…
Diffusion-based Cumulative Adversarial Purification for Vision Language Models
Jia Fu, Yongtao Wu, Yihang Chen +5
Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to th…
DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination
Jia Fu, Xiao Zhang, Sepideh Pashami +2
In the ever-evolving adversarial machine learning landscape, developing effective defenses against patch attacks has become a critical challenge, necessitating reliable solutions t…
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis
Abdallah Alabdallah, Omar Hamed, Mattias Ohlsson +2
The Cox Proportional Hazards (CPH) model has long been the preferred survival model for its explainability. However, to increase its predictive power beyond its linear log-risk, it…
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro +4
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan…
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami +2
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs). Randomization, like weight noise and dropout, aids in reducing overfitting and enha…
Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
Yuantao Fan, Zhenkan Wang, Sepideh Pashami +2
Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understandin…
Component attention network for multimodal dance improvisation recognition
Jia Fu, Jiarui Tan, Wenjie Yin +2
Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, i…
Heterogeneous Federated Learning via Personalized Generative Networks
Zahra Taghiyarrenani, Abdallah Alabdallah, Slawomir Nowaczyk +1
Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistic…
Explainable Predictive Maintenance
Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan +13
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including…
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami +1
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of th…
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami +1
Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel mu…
AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control
Cristofer Englund, Eren Erdal Aksoy, Fernando Alonso-Fernandez +3
Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizin…
Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification
Pablo del Moral, Slawomir Nowaczyk, Anita Sant'Anna +1
Using hierarchies of classes is one of the standard methods to solve multi-class classification problems. In the literature, selecting the right hierarchy is considered to play a k…
Avoiding Improper Treatment of Persons with Dementia by Care Robots
Martin Cooney, Sepideh Pashami, Eric Järpe +1
The phrase "most cruel and revolting crimes" has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the resp…
Exploring home robot capabilities by medium fidelity prototyping
Martin Cooney, Sepideh Pashami, Yuantao Fan +9
In order for autonomous robots to be able to support people's well-being in homes and everyday environments, new interactive capabilities will be required, as exemplified by the so…