no code implementations • 29 Sep 2022 • Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti, Hussein A. Abbass, Junyu Dong
In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA).
no code implementations • 4 Sep 2022 • Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
no code implementations • 24 Mar 2022 • Adam J. Hepworth, Daniel P. Baxter, Hussein A. Abbass
Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction.
no code implementations • 7 Nov 2021 • Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti, Hussein A. Abbass
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification.
no code implementations • 20 Aug 2021 • Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
no code implementations • 27 Jul 2021 • Tanmoy Dam, Sreenatha G. Anavatti, Hussein A. Abbass
Since the real conditional distribution of data is ignored, the clustering inference network can only achieve inferior clustering performance by considering only uniform prior based generative samples.
no code implementations • 26 Jun 2020 • Tung D. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass
It is well-known that information loss can occur in the classic and simple Q-learning algorithm.
no code implementations • 10 Mar 2020 • Duy T. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass
While the EC-DT translates an ANN in a layer-wise manner to represent exactly the decision boundaries implicitly learned by the hidden layers of the network, the Extended C-Net combines the decompositional approach from EC-DT with a C5 tree learning algorithm to form decision rules.
no code implementations • 7 Feb 2020 • Hussein A. Abbass, Sondoss Elsawah, Eleni Petraki, Robert Hunjet
The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning.
no code implementations • 27 Feb 2018 • Jiangjun Tang, Hussein A. Abbass
The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing.
no code implementations • 16 Mar 2016 • Hussein A. Abbass, George Leu, Kathryn Merrick
Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently.