[PDF][PDF] Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects

MU Hadi, R Qureshi, A Shah, M Irfan, A Zafar… - Authorea …, 2023 - researchgate.net
Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

S Rani, A Kataria, S Kumar, P Tiwari - Knowledge-based systems, 2023 - Elsevier
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Federated learning for smart healthcare: A survey

DC Nguyen, QV Pham, PN Pathirana, M Ding… - ACM Computing …, 2022 - dl.acm.org
Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

Synthetic data generation for tabular health records: A systematic review

M Hernandez, G Epelde, A Alberdi, R Cilla, D Rankin - Neurocomputing, 2022 - Elsevier
Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …

An empirical survey of data augmentation for time series classification with neural networks

BK Iwana, S Uchida - Plos one, 2021 - journals.plos.org
In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Modeling tabular data using conditional gan

L Xu, M Skoularidou, A Cuesta-Infante… - Advances in neural …, 2019 - proceedings.neurips.cc
Modeling the probability distribution of rows in tabular data and generating realistic synthetic
data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous …

Deep learning for time series classification

HI Fawaz - ar** deep learning models using electronic health records data: a systematic review
C **ao, E Choi, J Sun - Journal of the American Medical …, 2018 - academic.oup.com
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

A multifaceted benchmarking of synthetic electronic health record generation models

C Yan, Y Yan, Z Wan, Z Zhang, L Omberg… - Nature …, 2022 - nature.com
Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …