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Found 10 papers, 3 papers with code
RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management
In recent years, digital platform companies have faced increasing challenges in managing customer complaints, driven by widespread consumer adoption.
RE-RFME: Real-Estate RFME Model for customer segmentation
Marketing is one of the high-cost activities for any online platform.
RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain
For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario.
DIGITOUR: Automatic Digital Tours for Real-Estate Properties
A virtual or digital tour is a form of virtual reality technology which allows a user to experience a specific location remotely.
DEXTER: An end-to-end system to extract table contents from electronic medical health documents
We design a conventional computer vision-based approach for table type classification and cell detection using parameterized kernels based on image size for detecting rows and columns.
RE-Tagger: A light-weight Real-Estate Image Classifier
Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience.
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data
In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
We tackle the issue of classifier combinations when observations have multiple views.
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework.
