arXiv subjects

M. Sajid

Publications and source records attributed to M. Sajid.

23 records · Page 2Linked to original sources

arXiv2024

Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern

The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state-of-the-art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane-based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin-GTSVM-K) excels in CT and WM features, whereas Linear Pin-GTSVM (Pin-GTSVM-L) and Linear intuitionistic fuzzy TSVM (IFTSVM-L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.

cs.LG↗

arXiv2024

Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers

In the realm of data classification, broad learning system (BLS) has proven to be a potent tool that utilizes a layer-by-layer feed-forward neural network. However, the traditional BLS treats all samples as equally significant, which makes it less robust and less effective for real-world datasets with noises and outliers. To address this issue, we propose fuzzy broad learning system (F-BLS) and the intuitionistic fuzzy broad learning system (IF-BLS) models that confront challenges posed by the noise and outliers present in the dataset and enhance overall robustness. Employing a fuzzy membership technique, the proposed F-BLS model embeds sample neighborhood information based on the proximity of each class center within the inherent feature space of the BLS framework. Furthermore, the proposed IF-BLS model introduces intuitionistic fuzzy concepts encompassing membership, non-membership, and score value functions. IF-BLS strategically considers homogeneity and heterogeneity in sample neighborhoods in the kernel space. We evaluate the performance of proposed F-BLS and IF-BLS models on UCI benchmark datasets with and without Gaussian noise. As an application, we implement the proposed F-BLS and IF-BLS models to diagnose Alzheimer's disease (AD). Experimental findings and statistical analyses consistently highlight the superior generalization capabilities of the proposed F-BLS and IF-BLS models over baseline models across all scenarios. The proposed models offer a promising solution to enhance the BLS framework's ability to handle noise and outliers. The source code link of the proposed model is available at https://github.com/mtanveer1/IF-BLS.

cs.LG↗

arXiv2024

Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning

The domain of machine learning is confronted with a crucial research area known as class imbalance learning, which presents considerable hurdles in precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. 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. The proposed GE-IFRVFL-CIL model offers plethora of benefits: $(i)$ leveraging graph embedding to preserve the inherent topological structure of the datasets, $(ii)$ employing intuitionistic fuzzy theory to handle uncertainty and imprecision in the data, $(iii)$ and the most important, it tackles class imbalance learning. The amalgamation of a weighting scheme, graph embedding, and intuitionistic fuzzy sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the ADNI dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the class imbalance issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.

cs.LG↗

arXiv2014

Chaos in Dynamics of a Family of Transcendental Meromorphic Functions

The characterization and properties of Julia sets of one parameter family of transcendental meromorphic functions $ζ_λ(z)=λ\frac{z}{z+1} e^{-z}$, $λ>0$, $z\in \mathbb{C}$ is investigated in the present paper. It is found that bifurcations in the dynamics of $ζ_λ(x)$, $x\in {\mathbb{R}}\setminus \{-1\}$, occur at several parameter values and the dynamics of the family becomes chaotic when the parameter $λ$ crosses certain values. The Lyapunov exponent of $ζ_λ(x)$ for certain values of the parameter $λ$ is computed for quantifying the chaos in its dynamics. The characterization of the Julia set of the function $ζ_λ(z)$ as complement of the basin of attraction of an attracting real fixed point of $ζ_λ(z)$ is found here and is applied to computationally simulate the images of the Julia sets of $ζ_λ(z)$. Further, it is established that the Julia set of $ζ_λ(z)$ for $λ>(\sqrt{2}+1) e^{\sqrt{2}}$ contains the complement of attracting periodic orbits of $ζ_λ(x)$. Finally, the results on the dynamics of functions $λ\tan z$, $λ\in {\mathbb{\hat{C}}}\setminus\{0\}$, $E_λ(z) = λ\frac{e^{z} -1}{z}$, $λ> 0$ and $f_λ=λf(z)$, $λ>0$, where $f(z)$ has certain properties, are compared with the results found in the present paper.

math.DS↗

arXiv2010

K-Slicing the Reissner-Nordstrom Spacetime: Some New Observations

There were problems encountered in extending the K-slicing of the Schwarzschild and Reissner-Nordstrom (RN) spacetimes [1, 2] to the extreme case, when charge equals mass (in gravitational units). The earlier procedure is here modified so as to allow us to obtain a K-slicing of the region outside the horizon of the extreme metric by spacelike hypersurfaces. We checked this new procedure by applying it to the Schwarzschild and usual RN metrics and recovering the previous foliation with an improved accuracy. We have also checked the asymptotic behaviour of the K-slicing for large K by extrapolation.

gr-qc↗