Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

M Mousavizadegan, A Firoozbakhtian… - TrAC Trends in …, 2023 - Elsevier
Over the past decade, the wide-scale adoption of artificial intelligence (AI) and machine
learning (ML) has transformed the landscape of scientific research and development, which …

[HTML][HTML] Machine learning (ML) in medicine: Review, applications, and challenges

AM Rahmani, E Yousefpoor, MS Yousefpoor… - Mathematics, 2021 - mdpi.com
Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in
various industries, especially medicine. AI describes computational programs that mimic and …

Machine learning-assisted self-powered intelligent sensing systems based on triboelectricity

Z Tian, J Li, L Liu, H Wu, X Hu, M Xie, Y Zhu, X Chen… - Nano Energy, 2023 - Elsevier
The advancement of 5G and the Internet of Things (IoT) has ushered in an era of super-
interconnected intelligence, which promises high-quality social development. Triboelectric …

Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry

A Smith, S Hachen, R Schleifer… - … Journal of Social …, 2023 - journals.sagepub.com
Background: Artificial Intelligence is ever-expanding and large-language models are
increasingly shaping teaching and learning experiences. ChatGPT is a prominent recent …

[PDF][PDF] Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network.

S Mekruksavanich, A Jitpattanakul - Intelligent Automation & Soft …, 2022 - cdn.techscience.cn
Numerous learning-based techniques for effective human activity recognition (HAR) have
recently been developed. Wearable inertial sensors are critical for HAR studies to …

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations

Q Rafique, A Rehman, MS Afghan, HM Ahmad… - Computers in Biology …, 2023 - Elsevier
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …

Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories

C Moya, S Zhang, G Lin, M Yue - Neurocomputing, 2023 - Elsevier
This paper proposes a novel data-driven method for the reliable prediction of the power
grid's post-fault trajectories, ie, the power grid's dynamic response after a disturbance or …

Industry 5.0 is coming: A survey on intelligent nextG wireless networks as technological enablers

S Zeb, A Mahmood, SA Khowaja, K Dev… - arXiv preprint arXiv …, 2022 - arxiv.org
Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry
4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in …

[HTML][HTML] Optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment

DK Jana, P Bhunia, SD Adhikary, B Bej - Cleaner Chemical Engineering, 2022 - Elsevier
The freshwater is a challenge as the world's population grows. The largest sources of water
in this planet are brackish water and sea water. So, water purification process is very …

[HTML][HTML] Towards defining industry 5.0 vision with intelligent and softwarized wireless network architectures and services: A survey

S Zeb, A Mahmood, SA Khowaja, K Dev… - Journal of Network and …, 2023 - Elsevier
Abstract Industry 5.0 vision, a step toward the next industrial revolution and enhancement to
Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric …