Outputs (7)

Modeling diseases with Stream X Machine (2021)
Conference Proceeding
Jayatilake, S., Ogunshile, E., Aydin, M., & Phung, K. (2021). Modeling diseases with Stream X Machine. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (61-68). https://doi.org/10.1109/CONISOFT52520.2021.00020

At present the world is moving towards alternative medicine and behavioural alteration for treating, managing, and preventing chronical diseases. With the individuality of the human beings has added more complexity in a domain where very high accurac... Read More about Modeling diseases with Stream X Machine.

A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning (2021)
Conference Proceeding
Phung, K., Ogunshile, E., & Aydin, M. (2021). A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (168-179). https://doi.org/10.1109/CONISOFT52520.2021.00032

Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce suffic... Read More about A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning.

Reinforcement learning-based adaptive operator selection (2021)
Conference Proceeding
Durgut, R., & Aydin, M. E. (2021). Reinforcement learning-based adaptive operator selection. In B. Dorronsoro, L. Amodeo, M. Pavone, & P. Ruiz (Eds.), . https://doi.org/10.1007/978-3-030-85672-4_3

Metaheuristic and swarm intelligence approaches require devising optimisation algorithms with operators to let produce neighbouring solutions to conduct a move. The efficiency of algorithms using single operator remains recessive in comparison with t... Read More about Reinforcement learning-based adaptive operator selection.

A strategic search algorithm in multi-agent and multiple target environment (2021)
Conference Proceeding
Afzalov, A., Lotfi, A., & Aydin, M. E. (2021). A strategic search algorithm in multi-agent and multiple target environment. In E. Chew, A. P. Abdul Majeed, P. Liu, J. Platts, H. Myung, J. Kim, & J. Kim (Eds.), RiTA 2020 (195-204). https://doi.org/10.1007/978-981-16-4803-8_21

The aim of this study is to investigate how to solve the path-planning problem of multiple competing players towards moving targets within a dynamically changing environment. A novel approach is needed to exceed the classical solutions for single or... Read More about A strategic search algorithm in multi-agent and multiple target environment.

A comparative analysis for binary search operators used in artificial bee colony (2021)
Conference Proceeding
Atli, I., Durgut, R., & Aydin, M. E. (2021). A comparative analysis for binary search operators used in artificial bee colony. In 2021 29th Signal Processing and Communications Applications Conference (SIU). https://doi.org/10.1109/SIU53274.2021.9477907

Metaheuristic optimization algorithms are developed to find the best or near-best solutions within a reasonable time frame utilizing various neighbourhood functions (i.e., operators). Variety of studies have been proposed for structural modifications... Read More about A comparative analysis for binary search operators used in artificial bee colony.

Multi-agent path planning approach using assignment strategy variations in pursuit of moving targets (2021)
Conference Proceeding
Afzalov, A., He, J., Lotfi, A., & Aydin, M. E. (2021). Multi-agent path planning approach using assignment strategy variations in pursuit of moving targets. In G. Jezic, J. Chen-Burger, M. Kusek, R. Sperka, R. Howlett, & L. Jain (Eds.), Agents and Multi-Agent Systems: Technologies and Applications 2021 (451-463). https://doi.org/10.1007/978-981-16-2994-5_38

This study aims to investigate the problem of assignment strategies for multiple agents. In multi-agent scenarios, agents compute a path towards the goal, while these goal destinations in some cases are predefined in advance. The topic of assignment... Read More about Multi-agent path planning approach using assignment strategy variations in pursuit of moving targets.

Multiple pursuers TrailMax algorithm for dynamic environments (2021)
Conference Proceeding
Afzalov, A., Lotfi, A., Inden, B., & Aydin, M. (2021). Multiple pursuers TrailMax algorithm for dynamic environments. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART (437-443). https://doi.org/10.5220/0010392404370443

Multi-agent multi-target search problems, where the targets are capable of movement, require sophisticated algorithms for near-optimal performance. While there are several algorithms for agent control, comparatively less attention has been paid to ne... Read More about Multiple pursuers TrailMax algorithm for dynamic environments.