Low Query Budget Active Learning for Classification and Regression
B. Jaster, A. Tharwat, E.M. Sheikh, M. Kohlhase, W. Schenck, in: I. Koprinska, J. Mendes-Moreira, P. Branco (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV, Springer Nature Switzerland, Cham, 2026, pp. 5–21.

Active learning evaluation metrics for classification and regression frameworks
A. Tharwat, B. Jaster, W. Schenck, M. Kohlhase, Engineering Applications of Artificial Intelligence 171 (2026).

Using Optimization Algorithms for Effective Missing-Data Imputation: A Case Study of Tabular Data Derived from Video Surveillance
M.M. Eid, K. ElDahshan, A.H. Abouali, A. Tharwat, Algorithms 18 (2025).

Python Adventures for Young Coders. Explore the World of Programming
A. Tharwat, Python Adventures for Young Coders. Explore the World of Programming, Apress, Berkeley, CA, 2025.

Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget
A. Tharwat, W. Schenck, IEEE Transactions on Knowledge and Data Engineering 36 (2024) 4317–4330.

Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget
A. Tharwat, W. Schenck, IEEE Transactions on Knowledge and Data Engineering 36 (2024) 4317–4330.

Active Learning for Handling Missing Data
A. Tharwat, W. Schenck, IEEE Transactions on Neural Networks and Learning Systems 36 (2024) 3273–3287.

Tutorial: Interactive Adaptive Learning
A. Tharwat, M. Herde, M.T. Pham, B. Sick, in: 2024.

Tutorial: Interactive Adaptive Learning.
A. Tharwat, M. Bunse, B. Hammer, G. Krempl, V. Lemaire,  Saadallah Amal, in: 2023.

A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions
A. Tharwat, W. Schenck, Mathematics 11 (2023).

A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data
A. Tharwat, W. Schenck, Mathematics 10 (2022).

Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques
A. Tharwat, W. Schenck, Swarm and Evolutionary Computation 67 (2021).

A conceptual and practical comparison of PSO-style optimization algorithms
A. Tharwat, W. Schenck, Expert Systems with Applications 167 (2021).

Balancing Exploration and Exploitation: A novel active learner for imbalanced data
A. Tharwat, W. Schenck, Knowledge-Based Systems 210 (2020).

How to Label? Combining Experts’ Knowledge for German Text Classification
D. Pelkmann, A. Tharwat, W. Schenck, in: 2020 7th Swiss Conference on Data Science (SDS), IEEE, 2020, pp. 61–62.