87 citations · 159 across the 11 of their papers we have counts for
13 papers
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification
Alvaro Lopez Pellicer, Kittipos Giatgong, Yi Li +2
As the use of Deep Neural Networks (DNNs) becomes pervasive, their vulnerability to adversarial attacks and limitations in handling unseen classes poses significant challenges. The…
Towards interpretable-by-design deep learning algorithms
Plamen Angelov, Dmitry Kangin, Ziyang Zhang
The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set…
An Interpretable Deep Semantic Segmentation Method for Earth Observation
Ziyang Zhang, Plamen Angelov, Eduardo Soares +2
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role…
Multi-Branch with Attention Network for Hand-Based Person Recognition
Nathanael L. Baisa, Bryan Williams, Hossein Rahmani +2
In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in ca…
Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning
Nathanael L. Baisa, Bryan Williams, Hossein Rahmani +2
In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence i…
Deep Learning based Automated Forest Health Diagnosis from Aerial Images
Chia-Yen Chiang, Chloe Barnes, Plamen Angelov +1
Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of tree…
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference
Plamen Angelov, Eduardo Soares
In this paper we introduce the DMR -- a prototype-based method and network architecture for deep learning which is using a decision tree (DT)-based inference and synthetic data to…
A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion
Xiaowei Gu, Muhammad Aurangzeb Khan, Plamen Angelov +3
A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference m…
Towards Explainable Deep Neural Networks (xDNN)
Plamen Angelov, Eduardo Soares
In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal arch…
A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification
Xiaowei Gu, Plamen P Angelov, Eduardo Almeida Soares
Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50% to 80%) is used for training and the rest for validation. In many problems, h…
Novelty Detection and Learning from Extremely Weak Supervision
Eduardo Soares, Plamen Angelov
In this paper we offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training pri…
Fair-by-design explainable models for prediction of recidivism
Eduardo Soares, Plamen Angelov
Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making. It can al…
Parsimonious Random Vector Functional Link Network for Data Streams
Mahardhika Pratama, Plamen P. Angelov, Edwin Lughofer
The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of r…