Deep learning modelling techniques: current progress, applications, advantages, and challenges

SF Ahmed, MSB Alam, M Hassan, MR Rozbu… - Artificial Intelligence …, 2023 - Springer
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Data-centric artificial intelligence: A survey

D Zha, ZP Bhat, KH Lai, F Yang, Z Jiang… - ACM Computing …, 2025 - dl.acm.org
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

[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 …

A survey on sentiment analysis methods, applications, and challenges

M Wankhade, ACS Rao, C Kulkarni - Artificial Intelligence Review, 2022 - Springer
The rapid growth of Internet-based applications, such as social media platforms and blogs,
has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis …

[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications

M Javaid, A Haleem, RP Singh, R Suman… - International Journal of …, 2022 - Elsevier
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …

Multimodal deep learning for biomedical data fusion: a review

SR Stahlschmidt, B Ulfenborg… - Briefings in …, 2022 - academic.oup.com
Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

A Rahman, MS Hossain, G Muhammad, D Kundu… - Cluster computing, 2023 - Springer
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

Piezoelectric nanogenerators for personalized healthcare

W Deng, Y Zhou, A Libanori, G Chen, W Yang… - Chemical Society …, 2022 - pubs.rsc.org
The development of flexible piezoelectric nanogenerators has experienced rapid progress
in the past decade and is serving as the technological foundation of future state-of-the-art …

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

K Bera, N Braman, A Gupta, V Velcheti… - Nature reviews Clinical …, 2022 - nature.com
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

S Aminizadeh, A Heidari, M Dehghan, S Toumaj… - Artificial Intelligence in …, 2024 - Elsevier
The healthcare sector, characterized by vast datasets and many diseases, is pivotal in
sha** community health and overall quality of life. Traditional healthcare methods, often …