K
Kaveh Bastani
Researcher at Virginia Tech
Publications - 13
Citations - 424
Kaveh Bastani is an academic researcher from Virginia Tech. The author has contributed to research in topics: Decision support system & Compressed sensing. The author has an hindex of 11, co-authored 13 publications receiving 300 citations. Previous affiliations of Kaveh Bastani include Oklahoma State University–Stillwater & Chalmers University of Technology.
Papers
More filters
Journal ArticleDOI
Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints
TL;DR: In this paper, an intelligent approach based on latent Dirichlet allocation (LDA) was proposed to analyze the CFPB consumer complaints, and the proposed approach aims to extract latent topics in the consumer complaint narratives, and explores their associated trends over time.
Journal ArticleDOI
Wide and deep learning for peer-to-peer lending
TL;DR: A two-stage scoring approach designed as credit scoring to identify non-default loans while the imbalanced nature of loan status is considered in PD prediction, which outperforms the existing credit scoring and profit scoring approaches.
Journal ArticleDOI
Decision support system for Warfarin therapy management using Bayesian networks
TL;DR: A decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy management is described, built upon previous findings from medical literature, the knowledge of domain experts, and large dataset of patients.
Journal ArticleDOI
An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data
TL;DR: This work applies the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical–mechanical planarization process, and finds process drifts are detected and classified with higher accuracy compared with popular machine learning techniques.
Journal ArticleDOI
Fault Diagnosis Using an Enhanced Relevance Vector Machine (RVM) for Partially Diagnosable Multistation Assembly Processes
TL;DR: A fault diagnosis methodology is proposed by integrating the state space model with the enhanced relevance vector machine (RVM) to identify the process faults through the sparse estimate of the variance change of the process errors.