no code implementations • 30 Jan 2024 • Mushir Akhtar, M. Tanveer, Mohd. Arshad
It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone.
no code implementations • 5 Dec 2023 • Anuradha Kumari, M. Tanveer
Moreover, as an application, we conduct experiments on time series datasets, which results in the superiority of the proposed LSTSVR-PI.
no code implementations • 30 Oct 2023 • Anuradha Kumari, Mushir Akhtar, Rupal Shah, M. Tanveer
However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors.
no code implementations • 27 Sep 2023 • M. Tanveer, Ritik Mishra, Bharat Richhariya
Therefore, to remove these problems we propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets.
1 code implementation • 5 Sep 2023 • Mushir Akhtar, M. Tanveer, Mohd. Arshad
In the domain of machine learning algorithms, the significance of the loss function is paramount, especially in supervised learning tasks.
no code implementations • 15 Jul 2023 • M. Sajid, A. K. Malik, M. Tanveer
To address this issue, we propose the fuzzy BLS (F-BLS) model, which assigns a fuzzy membership value to each training point to reduce the influence of noises and outliers.
1 code implementation • 15 Jul 2023 • M. A. Ganaie, M. Sajid, A. K. Malik, M. Tanveer
To overcome this limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets.
no code implementations • 13 Apr 2023 • M. A. Ganaie, M. Tanveer, I. Beheshti, N. Ahmad, P. N. Suganthan
Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node.
no code implementations • 7 Dec 2022 • M. Tanveer, M. A. Ganaie, Iman Beheshti, Tripti Goel, Nehal Ahmad, Kuan-Ting Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin
In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.
1 code implementation • 18 Sep 2022 • Ye Shi, Leijie Zhang, Zehong Cao, M. Tanveer, Chin-Teng Lin
In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR.
no code implementations • 23 Mar 2022 • A. H. Rashid, A. Gupta, J. Gupta, M. Tanveer
Biceph-Net is also superior in performance compared to vanilla 2D convolutional neural networks (CNN) for AD diagnosis using 2D MRI slices.
no code implementations • 22 Mar 2022 • M. Tanveer, Jatin Jangir, M. A. Ganaie, Iman Beheshti, M. Tabish, Nikunj Chhabra
Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease.
no code implementations • 13 Feb 2022 • A. K. Malik, Ruobin Gao, M. A. Ganaie, M. Tanveer, P. N. Suganthan
To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed.
no code implementations • 3 Nov 2021 • M. A. Ganaie, M. Tanveer, P. N. Suganthan, V. Snasel
The oblique double random forest models are multivariate decision trees.
no code implementations • 1 May 2021 • M. Tanveer, T. Rajani, R. Rastogi, Y. H. Shao, M. A. Ganaie
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively.
no code implementations • 6 Apr 2021 • M. A. Ganaie, Minghui Hu, A. K. Malik, M. Tanveer, P. N. Suganthan
Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance.
1 code implementation • 16 Jan 2020 • M. A. Ganaie, Saptarshi Ghosh, Naveen Mendola, M. Tanveer, Sarika Jalan
The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance.
no code implementations • 30 Jun 2019 • Rakesh Katuwal, P. N. Suganthan, M. Tanveer
The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed form solution as in a standard RVFL network.
no code implementations • 13 Apr 2019 • Chandan Gautam, Aruna Tiwari, M. Tanveer
By using two types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based one-class classifier has been presented in this paper.
no code implementations • 13 Apr 2019 • Chandan Gautam, Aruna Tiwari, M. Tanveer
This privileged information is available as a feature with the dataset but only for training (not for testing).