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.
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.
In their study published in the journal Smart Cities, researchers employed smart sensing and predictive analytics to address challenges in Japan's urban development and infrastructure resilience. Focusing on Setagaya, Tokyo, the research produced predictive models accurately determining critical bearing layer depths, crucial for government plans and construction risk assessments.
Researchers explore the application of AI and ML in volatility forecasting, revealing their promise in improving accuracy and informing financial decisions. The review underscores the need for further exploration in explainable AI, uncertainty quantification, and alternative data sources to advance forecasting capabilities.
Exploring the financial challenges faced by expanding enterprises like the ZH group, researchers present a strategic financial control strategy incorporating intelligent algorithms. Through practical implementation and theoretical analysis, they highlight the efficacy of reverse neural networks and particle swarm optimization in enhancing decision-making and mitigating financial risks.
Researchers detail advancements in using Random Forest (RF) models to pinpoint tax risks within real estate, providing insights into discrepancies between declared values and model judgments. By meticulously selecting indicators and leveraging historical data, the RF model emerges as a robust tool for tax risk identification, offering decision support for tax authorities and promoting improved compliance levels within the real estate industry.
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