Ahmad P. Tafti

TL;DR: This work enhances the reliability, accuracy, and speed of 3D SEM surface reconstruction by designing and developing an optimized multi-view framework and considers several real-world experiments as well as synthetic data to examine the qualitative and quantitative attributes of the proposed framework.

TL;DR: This contribution explores the expanding body of the Apache Spark MLlib 2.0 as an open-source, distributed, scalable, and platform independent machine learning library, and performs several real world machine learning experiments to examine the qualitative and quantitative attributes of the platform.

TL;DR: This work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media and shows possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

TL;DR: Experimental results show that the proposed Poisson Dirichlet Model (PDM) could effectively identify distinguished disease clusters based on the latent patterns hidden in the EHR data by alleviating the impact of age and sex, and that LDA could stratify patients into more differentiable subgroups than PDM in terms of p-values.

TL;DR: The present evaluation is expected to advance OCR research, providing new insights and consideration to the research area, and assist researchers to determine which service is ideal for optical character recognition in an accurate and efficient manner.