no code implementations • 27 Aug 2019 • Varun Ojha, Ajith Abraham, Vaclav Snasel
This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other.
1 code implementation • 9 Oct 2020 • Varun Ojha, Giuseppe Nicosia
We propose an algorithm and a new method to tackle the classification problems.
Ranked #1 on General Classification on iris
no code implementations • 31 Aug 2021 • Haoran Duan, Fan Wan, Rui Sun, Zeyu Wang, Varun Ojha, Yu Guan, Hubert P. H. Shum, Bingzhang Hu, Yang Long
Our method achieved competitive performance in semi-supervised learning approaches on these crowd counting datasets.
no code implementations • 28 Jan 2022 • Chandresh Pravin, Varun Ojha
ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of a clinical environment, such as heart rate signatures recorded using non-contact radar systems, the measurements contain greater levels of noise than those acquired under clinical conditions.
no code implementations • 31 Jan 2022 • Chandresh Pravin, Ivan Martino, Giuseppe Nicosia, Varun Ojha
In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks.
1 code implementation • 4 Feb 2022 • Varun Ojha, Giuseppe Nicosia
We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree.
Ranked #1000000000 on Image Classification on MNIST
no code implementations • 15 Apr 2022 • Punitha Jaikumar, Remy Vandaele, Varun Ojha
This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the \textit{mask region proposal convolutional neural network} (Mask R-CNN).
1 code implementation • 11 Jul 2022 • Varun Ojha, Jon Timmis, Giuseppe Nicosia
We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem.
no code implementations • 14 Nov 2022 • Varun Ojha, Bartolomeo Panto, Giuseppe Nicosia
The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures.
no code implementations • 15 Dec 2023 • Chandresh Pravin, Ivan Martino, Giuseppe Nicosia, Varun Ojha
We define three \textit{filtering scores} for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets.
no code implementations • 22 Jan 2024 • Feng Xiong, Thanet Markchom, Ziwei Zheng, Subin Jung, Varun Ojha, HuiZhi Liang
The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C).