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753 results on '"Tanveer, Muhammad"'

1. Sparse identification of time delay systems via pseudospectral collocation

Author

Bozzo, Enrico, Breda, Dimitri, and Tanveer, Muhammad

Subjects
Mathematics - Dynamical Systems, Mathematics - Numerical Analysis
Abstract

We present a pragmatic approach to the sparse identification of nonlinear dynamics for systems with discrete delays. It relies on approximating the underlying delay model with a system of ordinary differential equations via pseudospectral collocation. To minimize the reconstruction error, the new strategy avoids optimizing all possible multiple unknown delays, identifying only the maximum one. The computational burden is thus greatly reduced, improving the performance of recent implementations that work directly on the delay system.

Published
2024

13. Fuzzy Logic Based Lipid Profile Control System

Author

Khokhar, Salah-ud-din, Nadeem, Akif, Rizvi, Arslan A., Tanveer, Muhammad, Inam, Aaqib, Touqir, Raeena, Khan, M. Saleem, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Cevik Onar, Sezi, editor, Cebi, Selcuk, editor, Oztaysi, Basar, editor, Tolga, A. Cagrı, editor, and Ucal Sari, Irem, editor

Published
2024
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50. Fine-Tuning DARTS for Image Classification

Author

Tanveer, Muhammad Suhaib, Khan, Muhammad Umar Karim, and Kyung, Chong-Min

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Neural Architecture Search (NAS) has gained attraction due to superior classification performance. Differential Architecture Search (DARTS) is a computationally light method. To limit computational resources DARTS makes numerous approximations. These approximations result in inferior performance. We propose to fine-tune DARTS using fixed operations as they are independent of these approximations. Our method offers a good trade-off between the number of parameters and classification accuracy. Our approach improves the top-1 accuracy on Fashion-MNIST, CompCars, and MIO-TCD datasets by 0.56%, 0.50%, and 0.39%, respectively compared to the state-of-the-art approaches. Our approach performs better than DARTS, improving the accuracy by 0.28%, 1.64%, 0.34%, 4.5%, and 3.27% compared to DARTS, on CIFAR-10, CIFAR-100, Fashion-MNIST, CompCars, and MIO-TCD datasets, respectively., Comment: 8 pages, 6 figures

Published
2020

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