10 citations · 11 across the 4 of their papers we have counts for
6 papers
Algorithmic drift in generative adaptive learning
Firuz Kamalov, Hana Sulieman, Ahmed El Sayed +2
Generative artificial intelligence promises to democratize personalized tutoring, yet the longitudinal stability of adaptive systems remains under-theorized. Current ethical discou…
Theoretical Convergence of SMOTE-Generated Samples
Firuz Kamalov, Hana Sulieman, Witold Pedrycz
Imbalanced data affects a wide range of machine learning applications, from healthcare to network security. As SMOTE is one of the most popular approaches to addressing this issue,…
Synthetic Data for Feature Selection
Firuz Kamalov, Hana Sulieman, Aswani Kumar Cherukuri
Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that ca…
Time series signal recovery methods: comparative study
Firuz Kamalov, Hana Sulieman
Signal data often contains missing values. Effective replacement (imputation) of the missing values can have significant positive effects on processing the signal. In this paper, w…
Improving D-Optimality in Nonlinear Situations
Hana Sulieman
Experimental designs based on the classical D-optimal criterion minimize the volume of the linear-approximation inference regions for the parameters using local sensitivity coeffic…
Machine learning applications for COVID-19: A state-of-the-art review
Firuz Kamalov, Aswani Cherukuri, Hana Sulieman +2
The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applicat…