AIMC Topic: Big Data

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Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Sensors (Basel, Switzerland)
Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagno...

Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer.

Frontiers in immunology
BACKGROUND: Ovarian cancer (OC) is a severe malignant tumor with a significant threat to women's health, characterized by a high mortality rate and poor prognosis despite conventional treatments such as cytoreductive surgery and platinum-based chemot...

An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using "big data", time-series analyses, and unsupervised machine learning.

Environmental pollution (Barking, Essex : 1987)
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non-E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, "big data" (i.e., large, diverse datase...

Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach.

JMIR formative research
BACKGROUND: The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health ...

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology.

Hematology/oncology clinics of North America
This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) and natural language processing (NLP) techniques. We examined CV-based AI/ML in digital pathology an...

How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant.

Water research
Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However,...

Naturalistic multimodal emotion data with deep learning can advance the theoretical understanding of emotion.

Psychological research
What are emotions? Despite being a century-old question, emotion scientists have yet to agree on what emotions exactly are. Emotions are diversely conceptualised as innate responses (evolutionary view), mental constructs (constructivist view), cognit...

Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry.

PloS one
This paper investigates the impact of artificial intelligence (AI) and big data analytics on optimizing cross-border e-commerce efficiency for straw hat manufacturers in Zhejiang Province, China. It identifies market and consumer demand trends throug...

Can big data policy drive urban carbon unlocking efficiency? A new approach based on double machine learning.

Journal of environmental management
In recent years, data has increasingly become the "new oil" for 21st-century economic development. However, there is still a gap in how the development of big data promotes the improvement of urban carbon unlocking efficiency (UCUE). Utilizing advanc...

Current status and future direction of cancer research using artificial intelligence for clinical application.

Cancer science
The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in socie...