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5,005 results on '"ALI, AMJAD"'

1. A transform based local RBF method for 2D linear PDE with Caputo–Fabrizio derivative

Author

Kamran, Ali, Amjad, and Gómez-Aguilar, José Francisco

Subjects
Mathematics, QA1-939
Abstract

The present work aims to approximate the solution of linear time fractional PDE with Caputo Fabrizio derivative. For the said purpose Laplace transform with local radial basis functions is used. The Laplace transform is applied to obtain the corresponding time independent equation in Laplace space and then the local RBFs are employed for spatial discretization. The solution is then represented as a contour integral in the complex space, which is approximated by trapezoidal rule with high accuracy. The application of Laplace transform avoids the time stepping procedure which commonly encounters the time instability issues. The convergence of the method is discussed also we have derived the bounds for the stability constant of the differentiation matrix of our proposed numerical scheme. The efficiency of the method is demonstrated with the help of numerical examples. For our numerical experiments we have selected three different domains, in the first test case the square domain is selected, for the second test the circular domain is considered, while for third case the L-shape domain is selected.

Published
2020
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10. Application of Next-Generation Breeding Tools in Minor Pulses for Major Impact: A Way Ahead

Author

Barpete, Surendra, Altaf, Muhammad Tanveer, Liaqat, Waqas, Ali, Amjad, Bedir, Mehmet, Nadeem, Muhammad Azhar, Cömertpay, Gönül, Xu, Qunale, Bakhsh, Allah, Suleimanova, Gulnur, Aasim, Muhammad, Tripathi, Kuldeep, Das, Arpita, Khawar, Khalid Mahmood, Baloch, Faheem Shehzad, Pandey, Manish K., editor, Mallikarjuna, M. G., editor, Lohithaswa, H. C., editor, S. Aski, Muraleedhar, editor, and Gupta, Sanjeev, editor

Published
2025
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17. Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data

Author

Khan, Zardad, Ali, Amjad, and Aldahmani, Saeed

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, 14J60
Abstract

In this paper, a robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative feature for high dimensional gene expression binary classification with class-imbalance problem. The method addresses one of the most challenging problems of highly skewed class distributions in gene expression datasets that adversely affect the performance of classification algorithms. First, the training dataset is balanced by synthetically generating data points from minority class observations. Second, a minimum subset of genes is selected using a greedy search approach. Third, a novel weighted robust score, where the weights are computed by support vectors, is introduced to obtain a refined set of genes. The highest-scoring genes based on this approach are combined with the minimum subset of genes selected by the greedy search approach to form the final set of genes. The novel method ensures the selection of the most discriminative genes, even in the presence of skewed class distribution, thus improving the performance of the classifiers. The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets. Classification accuracy and sensitivity are used as performance metrics to compare the proposed ROWSU algorithm with several other state-of-the-art methods. Boxplots and stability plots are also constructed for a better understanding of the results. The results show that the proposed method outperforms the existing feature selection procedures based on classification performance from k nearest neighbours (kNN) and random forest (RF) classifiers., Comment: 25 pages

Published
2024

35. A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule

Author

Ali, Amjad, Hamraz, Muhammad, Khan, Dost Muhammad, Deebani, Wajdan, and Khan, Zardad

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, F.2.2
Abstract

Ensembles based on k nearest neighbours (kNN) combine a large number of base learners, each constructed on a sample taken from a given training data. Typical kNN based ensembles determine the k closest observations in the training data bounded to a test sample point by a spherical region to predict its class. In this paper, a novel random projection extended neighbourhood rule (RPExNRule) ensemble is proposed where bootstrap samples from the given training data are randomly projected into lower dimensions for additional randomness in the base models and to preserve features information. It uses the extended neighbourhood rule (ExNRule) to fit kNN as base learners on randomly projected bootstrap samples., Comment: 23 pages, 8 diagrams, 69 references

Published
2023

38. Bicomplex Mergelyan's Approximation Theorem

Author

Ali, Amjad, Arif, Mohd, and Kumar, Romesh

Subjects
Mathematics - Functional Analysis, Mathematics - Complex Variables, 30G35
Abstract

In this paper we prove Area theorem, Biebarbach`s Theorem, Koebe Quarter Theorem and Mergelyan`s Approximation Theorem in the bicomplex framework., Comment: In preparation ! Comments are welcome!

Published
2022

39. Optimal Extended Neighbourhood Rule $k$ Nearest Neighbours Ensemble

Author

Ali, Amjad, Khan, Zardad, Khan, Dost Muhammad, and Aldahmani, Saeed

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, 14J60
Abstract

The traditional k nearest neighbor (kNN) approach uses a distance formula within a spherical region to determine the k closest training observations to a test sample point. However, this approach may not work well when test point is located outside this region. Moreover, aggregating many base kNN learners can result in poor ensemble performance due to high classification errors. To address these issues, a new optimal extended neighborhood rule based ensemble method is proposed in this paper. This rule determines neighbors in k steps starting from the closest sample point to the unseen observation and selecting subsequent nearest data points until the required number of observations is reached. Each base model is constructed on a bootstrap sample with a random subset of features, and optimal models are selected based on out-of-bag performance after building a sufficient number of models. The proposed ensemble is compared with state-of-the-art methods on 17 benchmark datasets using accuracy, Cohen's kappa, and Brier score (BS). The performance of the proposed method is also assessed by adding contrived features in the original data., Comment: This manuscript has been submitted for publication in the esteemed journal Pattern Recognition Letters

Published
2022

42. Ready for Nuclear Energy? a Road to Peaceful Nuclear Power Generation Development in the Kingdom of Saudi Arabia: Vision 2030

Author

Ali, Amjad, Shams, Afaque, Al-Athel, Khaled S., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Shams, Afaque, editor, Al-Athel, Khaled, editor, Tiselj, Iztok, editor, Pautz, Andreas, editor, and Kwiatkowski, Tomasz, editor

Published
2024
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43. Fully Endoscopic Retrosigmoid Approach for Cerebellopontine Angle Tumors

Author

Saied, Mohamed, Najibullah, Mustafa, Shabbir, Zafdam, Saleem, Athary, Ali, Amjad, Azab, Waleed Abdelfattah, Di Rocco, Concezio, Series Editor, Arraez, Miguel A., Editorial Board Member, Boop, Frederick A., Editorial Board Member, Froelich, Sebastien, Editorial Board Member, Kato, Yoko, Editorial Board Member, Pang, Dachling, Editorial Board Member, Tu, Yong-Kwang, Editorial Board Member, and Azab, Waleed Abdelfattah, editor

Published
2024
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