Publications

Engineering Sciences

Failure prognostics under sensor degradation: A comparative study of online and batch Bayesian inference

Accurate failure prognostics and Remaining Useful Life (RUL) prediction are critical for prognostics and health management, but remain challenging when sensor degradation and stochastic dependencies are present. This study compares two Bayesian…

Hai-Canh Vu, Mitra Fouladirad, David Bassir, Thao Nguyen-Trang

Reliability Engineering and System Safety - 2027

Mathematics

A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection

We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its importance in real-world inspection systems where the labels of greatest…

Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos

Engineering Sciences

Hybrid AI and Computer Vision Framework for PCB/SMD Defect Detection with GUI-Enabled IoT Deployment

Ata Jahangir Moshayedi, Dong Haoren, Amir Sohail Khan, S.Ali Eftikhari, Ming Jun Zhang, David Bassir

2026 IEEE 3rd International Conference on Big Data Science and Engineering (ICBDSE) - 2026

Computer Vision and Pattern Recognition

FBENet: A Highway Road Debris Detection Network Based on Frequency-Aware Bidirectional Feature Fusion and Efficient Attention Enhancement

Highlights What are the main findings? FBENet (Frequency-Aware Bidirectional Feature Fusion and Efficient Attention Enhancement Network) achieved an mAP@0.5 of 0.862 and an F1-score of 0.811 on Synthetic-Debris. FBENet (Frequency-Aware Bidirectional…

Yange Chen, Baohua Guo, Sen Wang, Anthony Sigama, David Bassir

Sensors - 2026

Optimization and Control

A Framework for Joint Affine and Diffeomorphic Image Registration

Anatomical image registration commonly relies on a sequential pipeline where an affine alignment is estimated first and then held fixed while a non-rigid diffeomorphic deformation is applied. This two-step process often leads to suboptimal results,…

Anton François, Rayane Mouhli, Thomas Pierron

Atmospheric and Oceanic Physics

Feature-Based Machine Learning Framework for Multi-Source NH₃ Dataset Analysis

Accurate monitoring of atmospheric ammonia (NH3) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature…

Ata Jahangir Moshayedi, Babar Hussain Shah, Amir Sohail Khan, Seyyed Ali Eftekhari, Amin Kolahdooz, David Bassir

Automation - 2026

Discrete Mathematics

How Not to Tabulate the 2028 US Democratic Primary

The US Democratic Party introduced a novel electoral system in 2020, combining instant runoff voting (IRV) and proportional representation (PR) with a 15% viability threshold. This tabulation system was chosen over more established methods, such as…

Max Dunitz

Machine Learning

Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. However, assessing the…

Edgar Jaber, Rémy Vallot, Thibault Dairay, Mathilde Mougeot

Computation and Language

Improving Thematic Balance in Press Release Generation with GMMs and Dimensionality Reduction

Large Language Models (LLMs) are widely used to generate high-quality textual content, such as news articles and business reports. However, their performance is highly dependent on the quality and balance of the training data. In real-world…

Imed Keraghel, Mohamed Nadif

2026 International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML 2026) - 2026

Analysis of PDEs

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

It is well known that Newton's method converges faster when the initial guess is closer to a root of a system of nonlinear equations. In this paper, a two-stage Newton initial guess strategy is proposed by learning features from a parameter-space…

Rémy Vallot, Florian de Vuyst, Thibault Dairay, Mathilde Mougeot