Author Archives: decide_web

Published paper in journal Mathematics!

Paper “Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection“, by D. Jovanovic, M. Antonijevic, M.S. Stankovic, M. Zivkovic, M. Tanaskovic and N. Bacanin, has been published in Mathematics!

The research published in the paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

Published paper in journal Sensors!

Paper “Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach“, by Pavlović, M.S.; Milanović, P.D.; Stanković, M.S.; Perić, D.B.; Popadić, I.V.; Perić, M.V., has been published in Sensors!

By using a multi-spectral imaging setting, the paper proposes a new cross-spectral automatic data annotation methodology for SWIR channel training dataset creation, in which the visible-light channel provides a source for detecting object types and bounding boxes which are then transformed to the SWIR channel. With the proposed cross-spectral methodology, the goal of the paper is to improve object detection in SWIR images captured in challenging outdoor scenes. Experimental tests using a state-of-the-art deep neural network-based YOLOX model demonstrate that retraining with the created SWIR image dataset significantly improves average detection precision.

Special session dedicated to project DECIDE has been organized, with four papers presented and published at International Scientific Conference on Information Technology and Data Related Research – Sinteza 2022

The following four papers have been presented at the special session of Conference Sinteza 2022 dedicated to the project DECIDE:

  1. A. Ćuk, J. Gavrilović, M. Tanasković, M. Stanković, “Mobile Robot Path Planning Optimization by Artificial Bee Colony,” in Sinteza 2022 – International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2022, pp. 399-403.
  2. M. Stanković, M. Beko, M. Pavlović, I. Popadić, S. Stanković, “Distributed On-Policy Actor-Critic Reinforcement Learning,” in Sinteza 2022 – International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2022, pp. 389-393.
  3. U. Dragović, M. Tanasković, M. Stanković, A. Ćuk, “Autonomous Drone Control for Visual Search Based on Deep Reinforcement Learning,” in Sinteza 2022 – International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2022, pp. 382-388.
  4. I. Walter, M. Tanasković, “Image Segmentation Processing for Thermographic Analysis,” in Sinteza 2022 – International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2022, pp. 394-398.

Published paper in IEEE Transactions on Aerospace and Electronic Systems!

Paper “Adaptive Consensus-based Distributed System for Multisensor Multitarget Tracking“, by S.S. Stanković, N. Ilić and M.S. Stanković, has been published in IEEE Transactions on Aerospace and Electronic Systems  (IEEE TAES)!

The paper proposes a new comprehensive system for distributed multisensor multitarget tracking, with all of its functions, including track initiation, confirmation, maintenance and termination, as well as track-to-track association and fusion, built around the concept of the probability of target existence and an adaptive consensus scheme. Stability of the proposed system is studied for the steady state and time-varying regimes. The system as a whole achieves high performance close to the centralized solution, outperforming all the comparable existing state-of-the-art approaches, keeping much lower communication and computation requirements.