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Sandeep Kumar

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23 papers

2 author rows

AIIM Journal 2026 Journal Article

Comprehensive review of heart disease prediction: A comparative study from 2019 onwards

Monali Gulhane
Sandeep Kumar
Shilpa Choudhary
Nitin Rakesh
Narendra Khatri
Chanderdeep Tandon
Balamurugan Balusamy
Anand Nayyar

In recent decades, cardiovascular disease, or heart disease, has been the number one cause of death worldwide, establishing an urgent need for timely and accurate early diagnosis. The primary purpose of this review is to examine the current state of the art in heart disease prediction, addressing a shift from traditional diagnostic techniques to modern machine learning and deep learning methods, while maintaining a systematic and comprehensive approach. A critical review of the literature is conducted to assess the effectiveness and limitations of various predictive algorithms. This approach provides historical context, highlights outstanding research needs, and presents recent advancements. The review provides a comprehensive assessment of the challenges in predicting heart disease, which includes both the identification of specific risk factors and non-linear interactions between selected factors. The study also examines how the relationship between CVDs and kidney stones can influence the development of predictive models in the future. In conclusion, this study summarizes its key findings in a defined roadmap for future research, emphasizing the potential benefits of applying deep learning methods to enhance diagnostic precision and thus optimize patient management and outcomes.

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IS Journal 2026 Journal Article

FairPreprocessor: Better Fairness Via Addressing Imbalanced Data Through Synthetic Data Generation and Mitigating Biased Labels

Hem Chandra Joshi
Sandeep Kumar

A machine learning (ML) model acquires logic from the training dataset, and any bias within it impacts the model’s decision. Previous studies have revealed that biased labels and imbalanced data are significant causes of bias in the training dataset. This study proposes a preprocessing approach, FairPreprocessor, that addresses imbalanced data through rebalancing the internal data distribution by employing synthetic data techniques grounded in differential evolution. It also selects the most suitable crossover rate in synthetic data generation to achieve better fairness. Additionally, it identifies and removes biased labels through situation testing, thereby mitigating their effects and developing fairer ML software.

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TMLR Journal 2026 Journal Article

GraphGini: Fostering Individual and Group Fairness in Graph Neural Networks

Anuj Kumar Sirohi
Anjali Gupta
Sandeep Kumar
Amitabha Bagchi
Sayan Ranu

Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by introducing GraphGini, a novel approach that incorporates the Gini coefficient to enhance both individual and group fairness within the GNN framework. We rigorously establish that the Gini coefficient offers greater robustness and promotes equal opportunity among GNN outcomes, advantages not afforded by the prevailing Lipschitz constant methodology. Additionally, we employ the Nash social welfare program to ensure our solution yields a Pareto optimal distribution of group fairness. Extensive experimentation on real-world datasets demonstrates GraphGini's efficacy in significantly improving individual fairness compared to state-of-the-art methods while maintaining utility and group fairness.

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AAAI Conference 2026 Conference Paper

Leap of FAITH from GNN-to-MLP: Fairness Aware Inference via DisTillation of GrapH Knowledge

Vipul Kumar Singh
Jyotismita Barman
Sandeep Kumar
Tapan K. Gandhi
Jayadeva

Graph Neural Networks (GNNs) are expressive architectures for learning from complex graph-structured data. However, their practical use is often limited by the high computational cost of neighborhood aggregation. Recent efforts have focused on knowledge distillation from GNNs to inference-efficient Multi-Layer Perceptrons (MLPs). However, most existing works treat this distillation as an embedding alignment problem, overlooking the need to replicate the topology-aware smoothing behavior that arises from message passing in GNNs. Moreover, existing methods are primarily performance driven, ignoring critical real-world requirements such as fairness. In this work, we make two key observations: (1) state-of-the-art distillation methods fail to capture the heterogeneous smoothness patterns of GNNs, limiting structural awareness in MLPs, and (2) they introduce significant individual and group fairness violations. We introduce FAITH, the first fair and structurally aware GNN-to-MLP distillation framework with graph-free inference. To improve structural awareness in MLPs, we propose a neighborhood-guided energy alignment objective that transfers not only node-level energy, but also the distribution of energies across local neighborhoods. To improve individual fairness, FAITH introduces a novel ℓ2,1-norm objective that preserves structured similarity in the learned representations. Additionally, we incorporate a counterfactual invariance objective that explicitly encourages the model to learn representations that are statistically independent of the sensitive attribute. We provide a comprehensive theoretical analysis of FAITH, interpreting it through a novel instantiation of the Information Bottleneck principle. Extensive experiments on 11 benchmark datasets show that FAITH achieves stronger structural awareness and delivers a better trade-off between utility and fairness than existing methods.

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EAAI Journal 2025 Journal Article

A comprehensive review on computer vision analysis of aerial data

Vivek Tetarwal
Manpreet Kaur
Sandeep Kumar

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.

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JBHI Journal 2025 Journal Article

CoarseFuse: Graph-Coarsening-Based Multi-Atlas Functional Connectivity Fusion for Autism Spectrum Disorder Diagnosis

Ekta Srivastava
Siddhant Ujjain
Tapan Kumar Gandhi
Sandeep Kumar

Autism spectrum disorder (ASD) affects $\sim$ 1–2% of the population, yet reliable imaging biomarkers remain elusive. Resting-state fMRI (rs-fMRI) enables noninvasive mapping of large-scale connectivity, but single-atlas analyses miss multi-scale effects and many fusion methods trade interpretability for complexity. We present CoarseFuse, a subject-specific, graph-coarsening multi-atlas fusion framework that (i) builds a unified supra-graph from multiple parcellations with space+function cross-atlas affinities, (ii) performs a closed-form, correlation-informed Laplacian refinement with row-sum/PSD projection, and (iii) applies feature-aware local-variation coarsening (LVN) to obtain low-dimensional pseudo-atlases that retain ROI-level interpretability. On ABIDE I, CoarseFuse yields a balanced accuracy (BA) of 82. 1% and F1 of 82. 0% under stratified 5-fold cross-validation (multiple backbones), outperforming early/late fusion baselines; LVN reduces dimensionality by $\sim$ 73% (450 $\rightarrow$ 120 nodes). A leave-one-site-out (17-site) evaluation demonstrates robustness to scanner/protocol variation (macro BA $79. 2\%\pm 4. 1$; macro F1 $80. 1\%\pm 3. 9$ ). Ablations show that explicit cross-atlas edges improve BA by $\sim$ 1. 4–1. 5 points and closed-form refinement adds 0. 6–0. 9 points while improving spectral conditioning. The learned super-nodes align with canonical resting-state networks (e. g. , default mode, salience), supporting biological interpretability. To our knowledge, this is the first closed-form Laplacian update tailored for multi-atlas rs-fMRI fusion. CoarseFuse advances rs-fMRI–based ASD diagnosis by combining accuracy, scalability, and transparent network-level insights.

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EAAI Journal 2025 Journal Article

Deep learning-driven channel estimation for Intelligent reflecting surfaces aided networks: A comprehensive survey

Jaya Singh
Kuldeep Singh
Dimpal Janu
Sandeep Kumar
Ghanshyam Singh

Intelligent reflecting surfaces (IRS) technology has demonstrated considerable potential in enhancing wireless communication by improving signal quality and extending coverage. However, IRS-assisted systems face unique issues in channel estimation caused by their passive nature and the complexity of the channel environment. Deep learning-driven methods provide powerful tools to address complexities such as non-linearities and the high dimensionality inherent in these systems. This paper offers an extensive survey of existing channel estimation techniques in IRS-assisted systems, laying a foundation for future research. To achieve this, a comprehensive literature search was conducted across eight reputable databases and search engines, including IEEE Xplore, Google Scholar, and Scopus etc. After applying rigorous inclusion criteria, 57 key articles were identified as highly relevant, forming the basis of this review. The survey covers traditional methods, such as least squares (LS), minimum mean squared error (MMSE), and linear MMSE (LMMSE), and contrasts them with advanced approaches, including matrix decomposition, compressed sensing, and deep learning techniques. The survey then systematically categorizes the selected studies into three groups: discriminative (supervised learning), generative (unsupervised learning), and hybrid learning. This study reveals that convolutional neural networks (CNNs) are well-suited for resource-constrained or real-time applications, while transformers provide excellent adaptability and accuracy, albeit with higher computational demands. The survey concludes with insights into future research directions, emphasizing the need for improved estimation efficiency and robustness in next-generation wireless systems.

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TMLR Journal 2025 Journal Article

GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision

Aditya Hemant Shahane
Prathosh AP
Sandeep Kumar

Graphs are growing rapidly and so are the number of different categories associated with it. Applications like e-commerce, healthcare, recommendation systems, and various social media platforms are rapidly moving towards graph representation of data due to their ability to capture both structural and attribute information. One crucial task in graph analysis is node classification, where unlabeled nodes are categorized into predefined classes. In practice, novel classes appear incrementally sometimes with just a few labels (seen classes) or even without any labels (unseen classes), either because they are new or haven't been explored much. Traditional methods assume abundant labeled data for training, which isn't always feasible. We investigate a broader objective: Graph Class Incremental Learning under Weak Supervision (GCL), addressing this challenge by meta-training on base classes with limited labeled instances. During the incremental streams, novel classes can have few-shot or zero-shot representation. Our proposed framework GOTHAM efficiently accommodates these unlabeled nodes by finding the closest prototype representation, serving as class representatives in the attribute space. For Text-Attributed Graphs (TAGs), our framework additionally incorporates semantic information to enhance the representation. By employing teacher-student knowledge distillation to mitigate forgetting, GOTHAM achieves promising results across various tasks. Experiments on datasets such as Cora-ML, Amazon, and OBGN-Arxiv showcase the effectiveness of our approach in handling evolving graph data under limited supervision.

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AAAI Conference 2025 Conference Paper

HyperDefender: A Robust Framework for Hyperbolic GNNs

Nikita Malik
Rahul Gupta
Sandeep Kumar

Graph neural networks for hyperbolic space has emerged as a powerful tool for embedding datasets exhibiting a highly non-Euclidean latent anatomy e.g., graphs with hierarchical structures. While several Hyperbolic Graph Neural Networks (Hy-GNNs) have been developed to enhance the representation of hierarchical datasets, they remain susceptible to noise and adversarial attacks, posing serious risks in critical applications. The absence of robust Hy-GNN frameworks underscores a pressing problem. This research addresses this challenge by introducing HyperDefender—a robust and flexible approach designed to fortify Hy-GNNs against adversarial attacks and noises. HyperDefender aims to secure the reliability of applications that depend on the integrity of hierarchical graph-structured data in real-world scenarios. Experimental results demonstrate that HyperDefender significantly improves node classification accuracy across various attacks, effectively mitigating the performance degradation typically observed in Hy-GNNs when the hierarchy in original datasets is compromised.

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TMLR Journal 2025 Journal Article

Modularity aided consistent attributed graph clustering via coarsening

Yukti Makhija
Samarth Bhatia
Manoj Kumar
Sandeep Kumar

Graph clustering is an unsupervised learning technique for partitioning graphs with attributes and detecting communities. However, current methods struggle to accurately capture true community structures and intra-cluster relations, be computationally efficient, and identify smaller communities. We address these challenges by integrating coarsening and modularity maximization, effectively leveraging both adjacency and node features to enhance clustering accuracy. We propose a loss function incorporating log-determinant, smoothness, and modularity components using a block majorization-minimization technique, resulting in superior clustering outcomes. The method is theoretically consistent under the Degree-Corrected Stochastic Block Model (DC-SBM), ensuring asymptotic error-free performance and complete label recovery. Our provably convergent and time-efficient algorithm seamlessly integrates with Graph Neural Networks (GNNs) and Variational Graph AutoEncoders (VGAEs) to learn enhanced node features and deliver exceptional clustering performance. Extensive experiments on benchmark datasets demonstrate its superiority over existing state-of-the-art methods for both attributed and non-attributed graphs.

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AAAI Conference 2024 Conference Paper

No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

Nimesh Agrawal
Anuj Kumar Sirohi
Sandeep Kumar
Jayadeva

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.) at the central server. This paper addresses the pervasive issue of inherent bias within RSs for different demographic groups without compromising the privacy of sensitive user attributes in FL environment with the graph-based model. To address the group bias, we propose F2PGNN (Fair Federated Personalized Graph Neural Network), a novel framework that leverages the power of Personalized Graph Neural Network (GNN) coupled with fairness considerations. Additionally, we use differential privacy techniques to fortify privacy protection. Experimental evaluation on three publicly available datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47% ∼ 99% compared to the state-of-the-art while preserving privacy and maintaining the utility. The results validate the significance of our framework in achieving equitable and personalized recommendations using GNN within the FL landscape. Source code is at: https://github.com/nimeshagrawal/F2PGNN-AAAI24

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UAI Conference 2024 Conference Paper

Optimization Framework for Semi-supervised Attributed Graph Coarsening

Manoj Kumar
Subhanu Halder
Archit Kane
Ruchir Gupta
Sandeep Kumar

In data-intensive applications, graphs serve as foundational structures across various domains. However, the increasing size of datasets poses significant challenges to performing downstream tasks. To address this problem, techniques such as graph coarsening, condensation, and summarization have been developed to create a coarsened graph while preserving important properties of the original graph by considering both the graph matrix and the feature or attribute matrix of the original graph as inputs. However, existing graph coarsening techniques often neglect the label information during the coarsening process, which can result in a lower-quality coarsened graph and limit its suitability for downstream tasks. To overcome this limitation, we introduce the Label-Aware Graph Coarsening (LAGC) algorithm, a semi-supervised approach that incorporates the graph matrix, feature matrix, and some of the node label information to learn a coarsened graph. Our proposed formulation is a non-convex optimization problem that is efficiently solved using block successive upper bound minimization(BSUM) technique, and it is provably convergent. Our extensive results demonstrate that the LAGC algorithm outperforms the existing state-of-the-art method by a significant margin.

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NeurIPS Conference 2024 Conference Paper

UGC: Universal Graph Coarsening

Mohit Kataria
Sandeep Kumar

In the era of big data, graphs have emerged as a natural representation of intricate relationships. However, graph sizes often become unwieldy, leading to storage, computation, and analysis challenges. A crucial demand arises for methods that can effectively downsize large graphs while retaining vital insights. Graph coarsening seeks to simplify large graphs while maintaining the basic statistics of the graphs, such as spectral properties and $\epsilon$-similarity in the coarsened graph. This ensures that downstream processes are more efficient and effective. Most published methods are suitable for homophilic datasets, limiting their universal use. We propose **U**niversal **G**raph **C**oarsening (UGC), a framework equally suitable for homophilic and heterophilic datasets. UGC integrates node attributes and adjacency information, leveraging the dataset's heterophily factor. Results on benchmark datasets demonstrate that UGC preserves spectral similarity while coarsening. In comparison to existing methods, UGC is 4x to 15x faster, has lower eigen-error, and yields superior performance on downstream processing tasks even at 70% coarsening ratios.

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JMLR Journal 2023 Journal Article

A Unified Framework for Optimization-Based Graph Coarsening

Manoj Kumar
Anurag Sharma
Sandeep Kumar

Graph coarsening is a widely used dimensionality reduction technique for approaching large-scale graph machine-learning problems. Given a large graph, graph coarsening aims to learn a smaller-tractable graph while preserving the properties of the originally given graph. Graph data consist of node features and graph matrix (e.g., adjacency and Laplacian). The existing graph coarsening methods ignore the node features and rely solely on a graph matrix to simplify graphs. In this paper, we introduce a novel optimization-based framework for graph dimensionality reduction. The proposed framework lies in the unification of graph learning and dimensionality reduction. It takes both the graph matrix and the node features as the input and learns the coarsen graph matrix and the coarsen feature matrix jointly while ensuring desired properties. The proposed optimization formulation is a multi-block non-convex optimization problem, which is solved efficiently by leveraging block majorization-minimization, $\log$ determinant, Dirichlet energy, and regularization frameworks. The proposed algorithms are provably convergent and practically amenable to numerous tasks. It is also established that the learned coarsened graph is $\epsilon\in(0,1)$ similar to the original graph. Extensive experiments elucidate the efficacy of the proposed framework for real-world applications. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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ICML Conference 2023 Conference Paper

Featured Graph Coarsening with Similarity Guarantees

Manoj Kumar
Anurag Sharma
Shashwat Saxena
Sandeep Kumar

Graph coarsening is a dimensionality reduction technique that aims to learn a smaller-tractable graph while preserving the properties of the original input graph. However, many real-world graphs also have features or contexts associated with each node. The existing graph coarsening methods do not consider the node features and rely solely on a graph matrix(e. g. , adjacency and Laplacian) to coarsen graphs. However, some recent deep learning-based graph coarsening methods are designed for specific tasks considering both node features and graph matrix. In this paper, we introduce a novel optimization-based framework for graph coarsening that takes both the graph matrix and the node features as the input and jointly learns the coarsened graph matrix and the coarsened feature matrix while ensuring desired properties. To the best of our knowledge, this is the first work that guarantees that the learned coarsened graph is $\epsilon\in[0, 1)$ similar to the original graph. Extensive experiments with both real and synthetic benchmark datasets elucidate the proposed framework’s efficacy and applicability for numerous graph-based applications, including graph clustering, node classification, stochastic block model identification, and graph summarization.

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NeurIPS Conference 2023 Conference Paper

Graph of Circuits with GNN for Exploring the Optimal Design Space

Aditya Shahane
Saripilli Swapna Manjiri
Ankesh Jain
Sandeep Kumar

The design automation of analog circuits poses significant challenges in terms of the large design space, complex interdependencies between circuit specifications, and resource-intensive simulations. To address these challenges, this paper presents an innovative framework called the Graph of Circuits Explorer (GCX). Leveraging graph structure learning along with graph neural networks, GCX enables the creation of a surrogate model that facilitates efficient exploration of the optimal design space within a semi-supervised learning framework which reduces the need for large labelled datasets. The proposed approach comprises three key stages. First, we learn the geometric representation of circuits and enrich it with technology information to create a comprehensive feature vector. Subsequently, integrating feature-based graph learning with few-shot and zero-shot learning enhances the generalizability in predictions for unseen circuits. Finally, we introduce two algorithms namely, EASCO and ASTROG which upon integration with GCX optimize the available samples to yield the optimal circuit configuration meeting the designer's criteria. The effectiveness of the proposed approach is demonstrated through simulated performance evaluation of various circuits, using derived parameters in 180nm CMOS technology. Furthermore, the generalizability of the approach is extended to higher-order topologies and different technology nodes such as 65nm and 45nm CMOS process nodes.

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EAAI Journal 2023 Journal Article

UInDeSI4.0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem

Amit K. Shukla
Shubham Srivastav
Sandeep Kumar
Pranab K. Muhuri

In an Industry 4. 0 ecosystem, all the essential components are digitally interconnected, and automation is integrated for higher productivity. However, it invites the risk of increasing cyber-attacks amid the current cyber explosion. The identification and monitoring of these malicious cyber-attacks and intrusions need efficient threat intelligence techniques or intrusion detection systems (IDSs). Reducing the false positive rate in detecting cyber threats is an important step for a safer and reliable environment in any industrial ecosystem. Available approaches for intrusion detection often suffer from high computational costs due to large number of feature instances. Therefore, this paper proposes a novel unsupervised IDS for Industry 4. 0 which we term as: Unsupervised Intrusion Detection System for Industry 4. 0 (UInDeSI4. 0). We have substantiated the proposed UInDeSI4. 0 approach through its experimentation on the well-known UNSW-NB15 Industry 4. 0 dataset. The proposed UInDeSI4. 0 employs feature selection approaches to obtain minimal and optimal features. These features are then used to train isolation forest to detect network traffic threats in an unsupervised manner. Accordingly, the proposed UInDeSI4. 0 approach can efficiently differentiate between the normal events and the attacks or intrusions in environments with no label information. Experimental results show that the proposed UInDeSI4. 0 provides better accuracy ( ∼ 63%) and a minimal feature set (nine) compared to traditional IDSs. In contrast to deep learning approaches, UInDeSI4. 0 generates faster results with minimum features. In conclusion, we establish the superiority of UInDeSI4. 0 approach as an accurate and computationally efficient IDS for Industry 4. 0.

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AAAI Conference 2022 Conference Paper

ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions

Koushik Biswas
Sandeep Kumar
Shilpak Banerjee
Ashish Kumar Pandey

An activation function is a crucial component of a neural network that introduces non-linearity in the network. The stateof-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments suggest that the proposed functions improve the network performance significantly compared to the widely used activations like ReLU, Swish, and Mish. Replacing ReLU by ErfAct and Pserf, we have 5. 68% and 5. 42% improvement for Top-1 accuracy on Shufflenet V2 (2. 0x) network in CIFAR100 dataset, 2. 11% and 1. 96% improvement for Top-1 accuracy on Shufflenet V2 (2. 0x) network in CIFAR10 dataset, 1. 0%, and 1. 0% improvement on mean average precision (mAP) on SSD300 model in Pascal VOC dataset.

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AAAI Conference 2021 Conference Paper

Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

Sandeep Kumar
Koushik Biswas
Ashish Kumar Pandey

The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4. 24 knots, 4. 5 hours, 0. 24 degree, and 0. 37 degree respectively, which resulted in a distance error of 51. 7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.

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JMLR Journal 2020 Journal Article

A Unified Framework for Structured Graph Learning via Spectral Constraints

Sandeep Kumar
Jiaxi Ying
José Vinícius de M. Cardoso
Daniel P. Palomar

Graph learning from data is a canonical problem that has received substantial attention in the literature. Learning a structured graph is essential for interpretability and identification of the relationships among data. In general, learning a graph with a specific structure is an NP-hard combinatorial problem and thus designing a general tractable algorithm is challenging. Some useful structured graphs include connected, sparse, multi-component, bipartite, and regular graphs. In this paper, we introduce a unified framework for structured graph learning that combines Gaussian graphical model and spectral graph theory. We propose to convert combinatorial structural constraints into spectral constraints on graph matrices and develop an optimization framework based on block majorization-minimization to solve structured graph learning problem. The proposed algorithms are provably convergent and practically amenable for a number of graph based applications such as data clustering. Extensive numerical experiments with both synthetic and real data sets illustrate the effectiveness of the proposed algorithms. An open source R package containing the code for all the experiments is available at https://CRAN.R-project.org/package=spectralGraphTopology. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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EAAI Journal 2020 Journal Article

Veracity handling and instance reduction in big data using interval type-2 fuzzy sets

Amit K. Shukla
Megha Yadav
Sandeep Kumar
Pranab K. Muhuri

Within the aspect of big data, veracity refers to the existing uncertainty in the dataset. The continuous flow of unstructured data with unwanted noise may bring abnormality in the dataset making them unusable. In this paper, we propose a novel method to handle the veracity characteristic of the big data using the concept of footprint of uncertainty (FOU) in interval type-2 fuzzy sets (IT2 FSs). The proposed method helps in handling the veracity issue in big data and reduces the instances to a manageable extent. We have compared the results with the existing clustering based methods and examined the relationship between the clusters and the FOUs by comparing their centroids and defuzzified values. To scrutinize the validity of our results, we have also performed a number of additional experiments by appending extra instances to the datasets. To check its consistency and efficacy, the proposed methodology is assessed from three different aspects. Experimental result validates that the proposed method can suitably handle the veracity issue in big datasets and is efficient in reducing the instances.

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NeurIPS Conference 2019 Conference Paper

Structured Graph Learning Via Laplacian Spectral Constraints

Sandeep Kumar
Jiaxi Ying
Jose Vinicius de Miranda Cardoso
Daniel Palomar

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. But structured graph learning from observed samples is an NP-hard combinatorial problem. In this paper, we first show, for a set of important graph families it is possible to convert the combinatorial constraints of structure into eigenvalue constraints of the graph Laplacian matrix. Then we introduce a unified graph learning framework lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling, which is capable of learning structures of a large class of graph families. The proposed algorithms are provably convergent and practically amenable for big-data specific tasks. Extensive numerical experiments with both synthetic and real datasets demonstrate the effectiveness of the proposed methods. An R package containing codes for all the experimental results is submitted as a supplementary file.

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KER Journal 2010 Journal Article

Web-based expert systems and services

Sandeep Kumar
Ravi Bhushan Mishra

Abstract Web-based expert systems (WBESs) provide the benefits of both expert system technology and web technology. The use of web services to deliver functionalities of WBESs allows the integration of these systems in web-portals. WBESs are used in a diversity of areas like engineering, management, medicine, agriculture, education, tourism, finance etc. A study on the various features of WBESs like knowledge-representation, reasoning, languages, implementation tools, use of various web services-related processes such as discovery, selection, composition etc. can result into some interesting conclusions. Presented work tries to fulfill the same purpose. The paper presents various WBESs classified according to their use of domain. The comparisons, observations, and assessments of these systems are presented by emphasizing the above-mentioned features of WBESs. A discussion on different web services-related processes and some representative techniques for performing each has also been provided to clarify their use in the WBESs. On the basis of assessments and surveys from different perspectives, some remarkable conclusions are drawn.

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