Articles tagged with Big Data
Researchers at Cornell University have developed a new method for mapping poverty using national surveys, big data, and machine learning. The approach translates Earth observation data into actionable terms for policymakers, providing more accurate estimates of poverty lines.
A new machine-learning approach to mapping poverty has been developed by Cornell University researchers, aiming to help policymakers and NGOs better identify the poorest populations in poor countries. The approach uses national surveys and Earth observation data to create actionable terms for policymakers, outperforming previous methods.
A study analyzing millions of U.S. prescriptions over 10 years found that co-prescribed stimulants and opioids are associated with escalating opioid intake. Patients taking both stimulants and opioids had a higher average monthly intake of morphine milligram equivalents (MME) compared to those taking only opioids.
A team of researchers recommends incorporating principles like collective benefit and ethical governance into ecological data practices to align with existing data infrastructures. They suggest establishing collaborative relationships with Indigenous rights holders and exploring how data can be aligned with Indigenous expertise and wor...
The Korea University College of Medicine and Yale University hosted a joint forum to discuss the latest advancements in medical AI, clinical informatics, and natural language processing. The event highlighted the impact of AI-driven technologies on healthcare systems and patient care.
The Department of Energy's new research centers, led by SLAC National Accelerator Laboratory, aim to make microelectronics more energy efficient and operate in extreme environments. Researchers will focus on innovating material design, devices, and systems architectures to push computing and sensing capabilities.
Over 260 young scientists from around the world have signed a declaration at Wenzhou, China, to promote sustainable innovation. The Wenzhou Declaration emphasizes the importance of leveraging science, technology, and innovation to tackle global challenges and create a resilient future.
A new system, EXPLINGO, enables AI models to generate readable narratives explaining their predictions, helping users make better decisions. The system, developed by MIT researchers, uses large language models to transform complex explanations into plain language and automatically evaluate their quality.
Researchers at Lehigh University are using advanced algorithms and cross-domain data to help cities predict human movement patterns, enabling better planning and preparedness for events and emergencies. The model will account for variations in data streams from different sources, such as cell towers, GPS, and financial transactions.
A study by Osaka University reveals that Japanese consumers value transparency in AI assistants, compromising on performance for greater clarity. Environmental sustainability is also a consideration, but remains secondary to cost and performance.
Researchers found that AI-generated essays are most similar to those written by male students from higher socioeconomic backgrounds and private schools. The writing styles also tend to be less varied than human-written essays, with AI favoring longer words and affiliations.
The Endocrine Society's inaugural Artificial Intelligence in Healthcare Virtual Summit will explore AI's potential to improve medical care, advance research, and leverage big data. Key sessions will discuss predictive analytics, machine learning algorithms, and natural language processing.
A research team developed a computational workflow for analyzing large data sets in metabolomics, speeding up the process to capture chemical profiles of coastal environments. The tool, accessible to researchers worldwide, highlights potential sources of pollution and enables statistical insights within minutes.
Researchers analyzed 2 million Google Street View images to explore the utility of digital data in informing public health decision-making. They found that neighborhoods with more crosswalks had lower rates of obesity and diabetes, but no significant link was found between sidewalks and health outcomes.
A new study shows that urban forests within walkable distance from residential areas are crucial in reducing heat-related health risks. Researchers found that nearby forests have a pronounced impact on reducing mortality risks, particularly those within 1 kilometre of residential areas.
A pioneering £4.8m eight-year programme will harness artificial intelligence to investigate the link between nutrition, health inequality and the development of multiple long-term conditions. The InflAIM project aims to identify new ways to slow the progression of multiple long-term health problems in people most at risk.
A team at NUS Yong Loo Lin School of Medicine leveraged an artificial intelligence-derived platform, CURATE.AI, to guide treatment for a patient with Waldenström macroglobulinemia, a rare blood disorder. The trial demonstrated substantial improvement in red blood cell levels and minimised side effects.
A growing global understanding of drought's impacts, but significant gaps remain in affected communities and developing countries. Long-lasting droughts heighten awareness, while economic factors influence responsiveness to the issue.
Researchers have made a significant breakthrough in vasculitis research using AI-powered big data techniques, enabling more precise identification of disease patterns. The study offers new insights into the diagnosis and treatment of systemic vasculitis, a group of rare autoimmune diseases.
Researchers found that iBuyers paid Black homeowners $4,376 on average, while paying white homeowners $27,239 less. The companies also favored institutions over individual buyers, with a greater increase in institutional ownership for white-owned homes.
A new study found that children who move multiple times between ages 10 and 15 are 41% more likely to be diagnosed with depression than those who don't move. This effect is stronger than growing up in an income-deprived neighborhood, suggesting a settled home environment may protect against future mental health issues.
Researchers developed a strategy to identify new antimicrobial drugs with therapeutic promise from bacterial datasets. PHAb10 and PHAb11 showed robust antibacterial activity against various bacterial species, including those resistant to traditional antibiotics.
The Kids First DRC has introduced an upgraded data portal to streamline big data search and analysis, improving collaborative pediatric research outcomes. The new portal integrates diverse datasets, including genomic information from the Children's Brain Tumor Network, to foster cross-disciplinary research.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
A team of scientists outlines a bold vision for precision approaches to understanding, preventing, and treating diseases using revolutionary technologies and interdisciplinary collaborations. They emphasize the need for increased funding and research to address future challenges such as antimicrobial resistance and zoonotic illnesses.
Research found that during severe droughts, agricultural reservoirs in Korea's southern region experienced increased total organic carbon concentrations. The study suggests that these reservoirs may shift from carbon storage to carbon sources, emitting carbon into the atmosphere. This finding highlights the need for integrated environm...
A new transit station in Japan significantly reduced average healthcare expenditures per capita over four years, with savings of approximately $929.99. The study used a causal impact algorithm and time series data to analyze the medical expenditure data gathered from a suburban city on the West Japan Railway line.
A new assessment tool developed by researchers at Northwestern University predicts suicidal thoughts and behaviors with an average accuracy of 92%. The tool uses a simple picture-ranking task along with contextual variables to identify individuals at risk of self-harm.
A new COVID-related syndrome, MDA5-autoimmunity and Interstitial Pneumonitis Contemporaneous with COVID-19 (MIP-C), has been identified in patients with severe lung scarring and autoimmune diseases. The syndrome was discovered through a retrospective observational study by UC San Diego researchers and UK colleagues.
Researchers used big data to calculate per-country greenhouse gas emissions from aviation for 197 countries, revealing gaps in reporting requirements under the UNFCCC treaty. Countries like China and the US were found to emit the most aviation-related emissions.
A new study by the University of Exeter finds that China's growing use of emerging technologies in civilian and military domains has escalated its stakes as a threat and near-peer competitor to the US. Western states have responded with diplomatic efforts, bans, and restrictions to undermine China's power.
A new neural network-based system called BONES improves video streaming quality by up to 13% while reducing download size, according to NJIT researcher Jacob Chakareski. The system uses a mathematical function to optimize data transmission and can be applied to various platforms, including popular video conferencing services.
Researchers at PolyU are using GeoAI to monitor and analyze environmental changes, detect disaster-damaged buildings, and optimize traffic flow. The technology has the potential to support sustainable urban development and improve public health.
A new study highlights the need for clear guidelines for synthetic data to prevent adverse effects on individuals and society. The researchers emphasize the importance of transparency, accountability, and fairness in the generation and processing of synthetic data.
This study uses large-scale GPS data to assess pedestrian movement around Tokyo's stations and its relationship with urban characteristics such as density, diversity, and design. The findings highlight that TOD attributes significantly impact pedestrian count, distances, and durations, but the impact varies across different metrics.
Smarter eco-cities harness real-time data to address environmental challenges and implement innovative approaches for ecological conservation. However, high energy demands associated with AI and AIoT applications pose a challenge to attaining environmental objectives.
Researchers developed a novel AI technique to detect Medicare fraud in big data, using Random Undersampling and supervised feature selection. The method improved classification performance, especially when reducing features, and outperformed models with all available features.
Researchers propose integrating smart-city technologies to boost flood resilience in coastal cities. They argue that emerging technologies like IoT, 5G, and big data can enable real-time data collection, analysis, and decision-making, leading to more responsive and resilient urban environments.
A study found that artificial light is a top indicator of where birds will land, leading to ecological traps and collisions. Urban parks can provide decent stopover sites, but competition over limited resources poses a significant risk to migrating birds.
A recent study found that during stringent COVID-19 periods, online searches surged in the health and daily life category. As government policies relaxed, searches shifted towards duty-free and travel-related products. The research team applied PCA to Big Data of internet search activity volume data from NAVER DataLab platform.
A research team employed deep learning techniques to scrutinize dam operation patterns, achieving remarkable accuracy in forecasting dam water levels. The study demonstrates the potential of an artificial intelligence model trained on extensive big data to surpass conventional physical models.
A new database combines distribution and lifecycle datasets to examine the prevalence of different lifecycles globally. The study found that annuals are expected to benefit from climate change, but their increased presence could be devastating for ecosystems.
Researchers found that green spaces alleviate extreme heat's negative impacts on human health, while densely packed buildings increase mortality risk. Urban design strategies incorporating different types of greenery are recommended to mitigate heatwave-associated mortality.
A study by the Complexity Science Hub analyzed work discourse on Reddit between 2018 and 2021, finding that mental health concerns significantly contributed to the surge in quit rates. The study suggests that companies can improve working conditions by prioritizing employees' relational and self-fulfillment needs.
Researchers at the University of Texas at Austin developed an AI algorithm that accurately predicted 14 earthquakes within about 200 miles of their location and strength, with only one false warning. The system detected statistical bumps in real-time seismic data and paired them with previous earthquakes to make predictions.
Research highlights the challenges of rural hospital-based maternity unit closures and transportation barriers, leading to delays in care and adverse outcomes. The study suggests that telehealth services can provide a solution to address these disparities in maternal health outcomes.
A study analyzed millions of tweets by Republican and Democratic US politicians over a decade, finding that Republicans were more likely to share untrustworthy information. The researchers identified linguistic signals associated with low-quality information and suggested potential solutions for the public to recognize these signals
Wogrin aims to improve data aggregation and create more meaningful models with the same computing power, resulting in suboptimal investment decisions and costly restructuring of energy systems. Her research approach takes into account different supply situations, enabling compressed and differentiated model data.
A new series of research papers addresses safety concerns in the Internet of Things, including data analysis and protection. The collection proposes solutions to challenges such as retrieving video data with textual queries and regulating media data dissemination.
A new study reveals that using big data and machine learning can improve antimicrobial resistance surveillance in livestock production. The research found correlations between environmental variables, microbial communities, and antimicrobial resistance, suggesting multiple routes for improving surveillance.
Scientists at Michigan State University used big data to study bird coexistence in the Albertine Rift ecosystem. They found that birds partition their habitat use along environmental gradients and adopt different strategies to survive, allowing them to coexist without driving each other to extinction.
The University of Missouri is using a $1 million grant to develop an Industry 4.0 lab, providing engineering students with hands-on learning experiences in the latest industrial revolution's technology-centered job market. The lab will integrate skills at a higher level and keep students at the state-of-the-art level for industry.
A groundbreaking Oxford study reveals a significant genetic component to people's probability of participating in genetic studies. The research identified detectable 'footprints' in genetics data that can be exploited statistically to enhance research accuracy for both participants and non-participants alike.
A research team at the University of Tsukuba has developed a secure AI technology that allows for the shared analysis of personal and identifiable data from multiple organizations. This will enhance the accuracy of AI analysis, particularly in disease prediction and educational effectiveness.
A new approach to enhance artificial intelligence-powered computer vision technologies has been developed by UCLA researchers, adding physics-based awareness to data-driven techniques. This hybrid methodology aims to improve how AI-based machinery sense, interact, and respond to their environment in real time.
A new framework developed by Carnegie Mellon University researchers significantly reduces consumers' privacy risk while preserving advertisers' utility in mobile location data analysis. The framework uses machine learning to quantify personalized privacy risks and performs personalized data obfuscation.
A new study maps French Canadian populations using a unique dataset of over five million records spanning 400 years, revealing the complex relationship between human migration and genetic variation. The research shows that the genetic structure of French Canadians is encoded within its genealogy.
A new study assesses racial bias in type 2 diabetes risk prediction algorithms. It finds significant disparities between US-based risk scores for non-Hispanic Blacks and non-Hispanic Whites.
Researchers from Universitat Oberta de Catalunya studied sidewalk networks in Barcelona and found that even pedestrian-friendly cities like Barcelona struggle with mobility constraints. The study proposes a framework for assessing multi-factor walkability using percolation theory and insights into pedestrian behavior to improve sidewal...
Researchers found that people dislike fitness recommendations from apps using social media data, instead favoring personalized approaches with control options. Users prefer systems allowing them to choose between different recommendation methods, making them feel more in control.