Search Results for author: Basem Suleiman

Found 8 papers, 1 papers with code

A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health

no code implementations8 Mar 2024 Alexander Marrapese, Basem Suleiman, Imdad Ullah, Juno Kim

In this paper, we propose a novel framework for evaluating the nuanced conversation abilities of LLMs.

A Fast Parallel Tensor Decomposition with Optimal Stochastic Gradient Descent: an Application in Structural Damage Identification

no code implementations4 Nov 2021 Ali Anaissi, Basem Suleiman, Seid Miad Zandavi

Our approach is based on stochastic gradient descent (SGD) algorithm which allows us to parallelize the learning process and it is very useful in online setting since it updates $\mathcal{X}^{t+1}$ in one single step.

Tensor Decomposition

A Personalized Federated Learning Algorithm: an Application in Anomaly Detection

no code implementations4 Nov 2021 Ali Anaissi, Basem Suleiman

Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models.

Anomaly Detection Personalized Federated Learning

Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring

no code implementations20 Jul 2021 Waheeb Yaqub, Manoranjan Mohanty, Basem Suleiman

The proposed image-hashing-based system can detect the student's excessive face and body movement (i. e., anomalies) that is resulted when the student tries to cheat in the exam.

Anomaly Detection Privacy Preserving

NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent

no code implementations18 Mar 2020 Ali Anaissi, Basem Suleiman, Seid Miad Zandavi

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $.

Tensor Decomposition

Online Tensor-Based Learning for Multi-Way Data

no code implementations10 Mar 2020 Ali Anaissi, Basem Suleiman, Seid Miad Zandavi

The online analysis of multi-way data stored in a tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $ has become an essential tool for capturing the underlying structures and extracting the sensitive features which can be used to learn a predictive model.

Cannot find the paper you are looking for? You can Submit a new open access paper.