Ravindrakumar Purohit

Nirma University

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WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Ravindrakumar Purohit J. P. Verma Rachna Jain Madhuri Bhavsar

Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However, creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semi-supervised task where the algorithm learns to recognize abnormality while being taught the normal class.

Novelty Detection

10.1109/incacct57535.2023.10141778

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FedBlocks: federated learning and blockchainbased privacy-preserved pioneering framework for IoT healthcare using IPFS in web 3.0 era

Cluster Computing (2024)

Ravindrakumar Purohit J. P. Verma Rachna Jain Ashish Kumar

Federated Learning

10.1007/s10586-024-04738-3

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WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

arXiv (Cornell University) (2023)

Ravindrakumar Purohit J. P. Verma Rachna Jain Madhuri Bhavsar

Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However,creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semisupervised task where the algorithm learns to recognize abnormality while being taught the normal class.

Novelty Detection

10.48550/arxiv.2306.06139

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Enhancing Doodling Experience Using Generative Adversarial Networks

Rashmi Ashtagi Devanshu Rathi Ripul Handoo Ravindrakumar Purohit Abbas Madhavaswala

Generative adversarial network

10.1109/gccit63234.2024.10862690

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LaueNN: neural network based hkl recognition of Laue spots and its application to polycrystalline materials

Acta Crystallographica Section A Foundations and Advances (2022)

Ravindrakumar Purohit S. Tardif O. Castelnau J. Eymery R. Guinebretière

et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

10.1107/s2053273322094815

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