Author name cluster

M. Tanveer

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16 papers

1 author row

EAAI Journal 2026 Journal Article

Hypergraph neural network with state space models for node classification

A. Quadir
M. Tanveer

In recent years, graph neural networks (GNNs) have gained significant attention for node classification tasks on graph-structured data. However, traditional GNNs primarily focus on adjacency relationships between nodes, often overlooking the role-based characteristics that can provide complementary insights for learning expressive node representations. Existing frameworks for extracting role-based features are largely unsupervised and often fail to translate effectively into downstream predictive tasks. To address these limitations, we propose a hypergraph neural network with a state space model (HGMN). The model integrates role-aware representations into GNNs by combining hypergraph construction with state-space modeling in a principled manner. HGMN employs hypergraph construction techniques to capture higher-order relationships and leverages a learnable mamba transformer mechanism to fuse role-based and adjacency-based embeddings. By exploring two distinct hypergraph construction strategies, degree-based and neighborhood-based, the framework reinforces connectivity among nodes with structural similarity, thereby enriching the learned representations. Furthermore, the inclusion of hypergraph convolution layers enables the model to account for complex dependencies within hypergraph structures. To alleviate the over-smoothing problem encountered in deeper networks, we incorporate residual connections, which improve stability and promote effective feature propagation across layers. Comprehensive experiments on benchmark datasets including OGB, ACM, DBLP, IIP TerroristRel, Cora, Citeseer, and Pubmed demonstrate that HGMN consistently outperforms strong baselines in node classification tasks. These results support the claim that explicitly incorporating role-based features within a hypergraph framework offers tangible benefits for node classification tasks.

JBHI Journal 2025 Journal Article

Multimodal Neuroimaging Based Alzheimer's Disease Diagnosis Using Evolutionary RVFL Classifier

Tripti Goel
Rahul Sharma
M. Tanveer
P. N. Suganthan
Krishanu Maji
Raveendra Pilli

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

EAAI Journal 2024 Journal Article

LSTSVR+: Least square twin support vector regression with privileged information

Anuradha Kumari
M. Tanveer

In an educational setting, a teacher plays a crucial role in various classroom teaching patterns. Similarly, mirroring this aspect of human learning, the learning using privileged information (LUPI) paradigm introduces additional information to instruct learning models during the training stage. A different approach to train the twin variant of the regression model is provided by the new least square twin support vector regression using privileged information (LSTSVR+). It integrates the LUPI paradigm to utilize additional sources of information into the least square twin support vector regression. The proposed LSTSVR+ solves system of linear equations which adds up to the efficiency of the model. Further, we also establish a generalization error bound based on the Rademacher complexity of the proposed LSTSVR+ and incorporate the structural risk minimization principle. The proposed LSTSVR+ fills the gap between the contemporary paradigm of LUPI and classical LSTSVR. Further, to assess the performance of the proposed model, we conduct numerical experiments along with the baseline models across artificially generated datasets and 21 real-world datasets. The various experiments and statistical analysis infer the superiority of the proposed model. Moreover, the proposed LSTSVR+ outperforms baseline models in real-world applications on time-series datasets. The link for the code of the proposed LSTSVR+ is as follows: https: //github. com/mtanveer1/LSTSVR-plus.

JBHI Journal 2024 Journal Article

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

Nehal Ahmad
Kuan-Ting Lai
M. Tanveer

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0. 2499 and mean average of 0. 0147, which outperformed the state-of-the-art method by 6. 32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the “STrutred Analysis of the REtina (STARE) Database” accessed on Oct. 2020 ( https://cecas.clemson.edu/~ahoover/stare/ ). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

EAAI Journal 2023 Journal Article

Association of white matter volume with brain age classification using deep learning network and region wise analysis

Raveendra Pilli
Tripti Goel
R. Murugan
M. Tanveer

Structural magnetic resonance imaging (sMRI) has been used to examine age-related neuroanatomical changes in the human brain. In the present work, a pre-trained deep learning model and an ensemble deep random vector functional link (edRVFL) classifier have been used to create a brain age classification framework from magnetic resonance imaging (MRI) scans. A total of 155 MRI scans of the brain are obtained from the open-access OpenNeuro database and categorized into three age groups (3–5 years old, 7–12 years old, and 18–40 years old). To visualize the age connection across different brain regions, all MRI scans are first segmented into Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). The ResNet-50 network is used to extract features from MRI images, while the edRVFL network is used to classify the retrieved features. Classification accuracy for GM, WM, CSF, and whole brain images are 96. 11%, 98. 33%, 93. 33%, and 94. 00%, respectively, using the edRVFL classifier. Region-wise analysis has also been done using Pearson’s correlation coefficient ( r ), coefficient of determination ( R 2 ), and root mean square error (RMSE) to analyze the relationship between brain age and brain tissue volumes. According to the findings of the suggested deep model for brain age categorization, and region-wise analysis, alterations in WM volume are strongly linked to brain aging.

JBHI Journal 2023 Journal Article

Brain Age Prediction With Improved Least Squares Twin SVR

M. A. Ganaie
M. Tanveer
Iman Beheshti

Alzheimer’s disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR), $\varepsilon$ -TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems. $\varepsilon$ -TSVR and LTSVR models seek a pair of $\varepsilon$ -insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario’s. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle and hence may be prone to overfitting. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario’s. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of $L_2$ norm instead of $L_1$. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer’s disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.

JBHI Journal 2023 Journal Article

Diagnosis of Schizophrenia: A Comprehensive Evaluation

M. Tanveer
Jatin Jangir
M. A. Ganaie
Iman Beheshti
M. Tabish
Nikunj Chhabra

Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.

JBHI Journal 2023 Journal Article

Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging—Part II

M. Tanveer
Chin-Teng Lin
Amit Kumar Singh

The papers in this special section (Part II) focus on advanced machine learning algorithms for biomedical data and imaging. The papers in Part II aim at bringing together contributions from both academia and industry to highlight the recent progress of machine-learning algorithm specific to medical data.

JBHI Journal 2023 Journal Article

Redefining Lobe-Wise Ground-Glass Opacity in COVID-19 Through Deep Learning and its Correlation With Biochemical Parameters

Budhadev Baral
Kartik Muduli
Shweta Jakhmola
Omkar Indari
Jatin Jangir
Ashraf Haroon Rashid
Suchita Jain
Amrut Kumar Mohapatra

During COVID-19 pandemic qRT-PCR, CT scans and biochemical parameters were studied to understand the patients' physiological changes and disease progression. There is a lack of clear understanding of the correlation of lung inflammation with biochemical parameters available. Among the 1136 patients studied, C-reactive-protein (CRP) is the most critical parameter for classifying symptomatic and asymptomatic groups. Elevated CRP is corroborated with increased D-dimer, Gamma-glutamyl-transferase (GGT), and urea levels in COVID-19 patients. To overcome the limitations of manual chest CT scoring system, we segmented the lungs and detected ground-glass-opacity (GGO) in specific lobes from 2D CT images by 2D U-Net-based deep learning (DL) approach. Our method shows $\ >\! 90\%$ accuracy, compared to the manual method ( $\sim\! 80\%$ ), which is subjected to the radiologist's experience. We determined a positive correlation of GGO in the right upper-middle (0. 34) and lower (0. 26) lobe with D-dimer. However, a modest correlation was observed with CRP, ferritin and other studied parameters. The final Dice Coefficient (or the F1 score) and Intersection-Over-Union for testing accuracy are 95. 44% and 91. 95%, respectively. This study can help reduce the burden and manual bias besides increasing the accuracy of GGO scoring. Further study on geographically diverse large populations may help to understand the association of the biochemical parameters and pattern of GGO in lung lobes with different SARS-CoV-2 Variants of Concern's disease pathogenesis in these populations.

EAAI Journal 2023 Journal Article

Universum twin support vector machine with truncated pinball loss

Anuradha Kumari
M. Tanveer

For classification problems, twin support vector machine with pinball loss (Pin-GTSVM) is noise insensitive and has better performance than twin support vector machine (TWSVM). However, it lacks sparsity in comparison to TWSVM. In this article, to maintain a trade-off between the noise insensitivity and sparsity of the model along with preserving the theoretical properties of pinball loss, we propose universum twin support vector machine with truncated pinball loss (Tpin-UTWSVM). The proposed Tpin-UTWSVM considers universum data which gives prior information about the distribution of the data, thus improves the generalization performance of the proposed model. Further, the proposed optimization problem is non-convex and non-differentiable which is solved by concave–convex procedure. We employed the SOR approach to train the proposed model effectively with minimum training time. We conducted numerical experiments on 19 UCI binary datasets with different noise levels to validate the noise insensitivity of the proposed Tpin-UTWSVM model. We also conducted numerical experiments for electroencephalogram (EEG) signal classification and Alzheimer’s disease (AD) detection. The overall experimental outcomes and statistical tests demonstrate the superiority of the proposed Tpin-UTWSVM model in comparison to the baseline models. The source code for the proposed Tpin-UTWSVM is available at https: //github. com/mtanveer1/Universum-twin-SVM-with-truncated-pinball-loss.

JBHI Journal 2022 Journal Article

Classification of Alzheimer’s Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning

M. Tanveer
A. H. Rashid
M. A. Ganaie
M. Reza
Imran Razzak
Kai-Lung Hua

Alzheimer’s disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named ‘Deep Transfer Ensemble (DTE)’ trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99. 05% and 85. 27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98. 71% and 83. 11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.

EAAI Journal 2022 Journal Article

Ensemble deep learning: A review

M.A. Ganaie
Minghui Hu
A.K. Malik
M. Tanveer
P.N. Suganthan

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. 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. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorized into bagging, boosting, stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous/heterogeneous ensemble, decision fusion strategies based deep ensemble models. Applications of deep ensemble models in different domains are also briefly discussed. Finally, we conclude this paper with some potential future research directions.

JBHI Journal 2022 Journal Article

Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging

M. Tanveer
Chin-Teng Lin
Amit Kumar Singh

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences.

JBHI Journal 2022 Journal Article

Lightweight Face Anti-Spoofing Network for Telehealth Applications

Jiun-Da Lin
Hung-Hsiang Lin
Jilyan Dy
Jun-Cheng Chen
M. Tanveer
Imran Razzak
Kai-Lung Hua

Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods.

JBHI Journal 2022 Journal Article

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation

Iman Beheshti
M. A. Ganaie
Vardhan Paliwal
Aryan Rastogi
Imran Razzak
M. Tanveer

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals ( $N = 788$ ) as a training set followed by different regression algorithms (22 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimer’s disease patients. The prediction accuracy in the independent test set (i. e. , CH set) varied in regression algorithms mean absolute error (MAE) from 4. 63 to 7. 14 yrs, $R^2$ from 0. 76 to 0. 88. The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE $= 4. 63$ yrs, $R^2 = 0. 88, 95\%$ CI $= [-1. 26, 1. 42]$ ) and Binary Decision Tree algorithm (MAE $= 7. 14$ yrs, $R^2 = 0. 76, 95\%$ CI $= [-1. 50, 2. 62]$ ), respectively. Our experimental results demonstrate that the prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.

JBHI Journal 2021 Journal Article

Sparse Twin Support Vector Clustering Using Pinball Loss

M. Tanveer
Tarun Gupta
Miten Shah
Bharat Richhariya

Clustering is a widely used machine learning technique for unlabelled data. One of the recently proposed techniques is the twin support vector clustering (TWSVC) algorithm. The idea of TWSVC is to generate hyperplanes for each cluster. TWSVC utilizes the hinge loss function to penalize the misclassification. However, the hinge loss relies on shortest distance between different clusters, and is unstable for noise-corrupted datasets, and for re-sampling. In this paper, we propose a novel Sparse Pinball loss Twin Support Vector Clustering (SPTSVC). The proposed SPTSVC involves the $\epsilon$ -insensitive pinball loss function to formulate a sparse solution. Pinball loss function provides noise-insensitivity and re-sampling stability. The $\epsilon$ -insensitive zone provides sparsity to the model and improves testing time. Numerical experiments on synthetic as well as real world benchmark datasets are performed to show the efficacy of the proposed model. An analysis on the sparsity of various clustering algorithms is presented in this work. In order to show the feasibility and applicability of the proposed SPTSVC on biomedical data, experiments have been performed on epilepsy and breast cancer datasets.