Big Data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It's characterized by its high volume, velocity, and variety (the "3 Vs"), and requires specific tools and methods for storage, processing, and analysis.
Researchers at Florida Atlantic University have developed a new unsupervised machine learning method to accurately label fraud in highly imbalanced financial and healthcare datasets, outperforming traditional techniques and reducing false positives. This scalable solution minimizes manual labeling, streamlining fraud detection in sectors where privacy and labeling costs are major concerns.
Emory University has developed AutoSolvateWeb, a cloud-based platform with a rules-based chatbot that enables non-experts to run sophisticated quantum chemistry simulations through natural-language guidance. This free tool democratizes access to molecular modeling, advancing both education and scientific research.
The 43rd Barcelona BioMed Conference showcased how AI is revolutionizing drug discovery, from generative compound design to robotic synthesis. Experts highlighted the shift toward personalized medicine driven by large-scale biological data and advanced machine learning.
Agentic AI models, like ESCARGOT, represent a major leap beyond traditional AI by autonomously coordinating multiple AI agents to solve complex biomedical problems. In Alzheimer’s research, this model outperformed ChatGPT by delivering significantly more accurate insights using a graph-based reasoning approach.
Korea Institute of Civil Engineering and Building Technology has developed generative AI-powered inspection technology that creates synthetic images of tunnel damage to address data scarcity and enhance safety inspections. Integrated with autonomous drones, this innovation enables precise, efficient tunnel inspections without relying on large datasets.
Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.
Researchers highlight how AI and big data are transforming crop breeding, ushering in "Breeding 4.0" with intelligent, data-driven precision to enhance global food security.
A new study finds that people trust AI for low-stakes decisions like music recommendations but are skeptical in high-stakes areas like healthcare—especially those with strong statistical literacy.
Scientists at the University of California, Riverside, have developed an unsupervised machine learning tool that identifies patterns in LIGO's environmental data, reducing noise and improving gravitational wave detection.
DeepSeek has developed an agile AI model that outperforms larger systems despite using less advanced hardware, reshaping global AI competition and policy discussions.
Study reveals that while Hour of Code activities excel in introducing AI basics, they often lack depth, hands-on creativity, and critical engagement needed for a well-rounded understanding of artificial intelligence
Researchers at MIT have developed EXPLINGO, a system that transforms complex machine-learning explanations into clear, human-readable narratives. By leveraging large language models, EXPLINGO enables users to trust AI predictions with concise, accurate, and fluently graded explanations.
Mining 4.0 technologies are reshaping workforce roles and operational dynamics, emphasizing the need for skills adaptation and well-being strategies in a digitally connected environment.
Researchers introduced innovative computer vision techniques to the maritime industry, incorporating ensemble learning and domain knowledge. These methods significantly improve detection accuracy and optimize video viewing on vessels, offering advancements for marine operations and communication.
Researchers leverage AI to optimize the design, fabrication, and performance forecasting of diffractive optical elements (DOEs). This integration accelerates innovation in optical technology, enhancing applications in imaging, sensing, and telecommunications.
In an article published in Computers and Education: Artificial Intelligence, researchers explored various methods for generating question-answer (QA) pairs using pre-trained large language models (LLMs) in higher education. They assessed pipeline, joint, and multi-task approaches across three datasets through automated metrics, teacher evaluations, and real-world educational settings.
A review in Materials & Design explores how big data, machine learning (ML), and digital twin technologies enhance additive manufacturing (AM). These technologies improve AM by optimizing design, material properties, and process efficiency, offering significant advancements in quality, efficiency, and sustainability.
Researchers reviewed AI advancements in electric power systems, highlighting its transformative potential due to modern microprocessors and data storage. The study categorizes AI applications into four areas and presents detailed case studies in wind power forecasting, smart grid security, and fault detection.
The Laplacian correlation graph (LOG) significantly improves stock trend prediction by modeling price correlations. Experimental results show superior accuracy and returns, highlighting LOG's potential in real-world investment strategies.
A recent review in the Journal of Materials Research and Technology explores machine learning's transformative potential in designing and optimizing magnesium (Mg) alloys. By leveraging ML, researchers can efficiently enhance Mg alloy properties, expediting their development and broadening industrial applications.
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