Papers Data Integration
“Data Integration” 태그가 달린 논문 431편 · 필터 해제
From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and het…
Data IntegrationEmpowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research
Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as advers…
Data IntegrationDimensionality ReductionFederated LearningPrivacy PreservingIntelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency
This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative resear…
Data IntegrationModel OptimizationRing-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built upon the publicly available Ling-lite mo…
Data IntegrationLarge Language ModelMixture-of-ExpertsReinforcement Learning (RL)Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research
Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and shown significant promise in medical imaging by enabling robust performance wit…
Data IntegrationLeveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, …
Causal InferenceData IntegrationDimensionality ReductionFederated Learning+2Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effect…
Data IntegrationDiabetes PredictionTime SeriesTime Series ForecastingscSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Self-supervised learning (SSL) has proven to be a powerful approach for extracting biologically meaningful representations from single-cell data. To advance our understanding of SSL methods applied to single-cell data, w…
BenchmarkingData AugmentationData IntegrationSelf-Supervised LearningThe Cell Ontology in the age of single-cell omics
Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets dema…
Data IntegrationDiversityFrom Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge Expansion
Accurate near-real-time precipitation retrieval has been enhanced by satellite-based technologies. However, infrared-based algorithms have low accuracy due to weak relations with surface precipitation, whereas passive mi…
Data IntegrationRetrievalKramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Constructing real-world data-to-insight pipelines often involves data extraction from data lakes, data integration across heterogeneous data sources, and diverse operations from data cleaning to analysis. The design and …
Code GenerationData IntegrationTowards Scalable Schema Mapping using Large Language Models
The growing need to integrate information from a large number of diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which …
Data IntegrationMulti-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings
Multi-task learning (MTL) has become an essential machine learning tool for addressing multiple learning tasks simultaneously and has been effectively applied across fields such as healthcare, marketing, and biomedical r…
Data IntegrationMarketingMulti-Task LearningTowards Unified Neural Decoding with Brain Functional Network Modeling
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and elect…
Data IntegrationSelf-Supervised LearningEvaluating AI capabilities in detecting conspiracy theories on YouTube
As a leading online platform with a vast global audience, YouTube's extensive reach also makes it susceptible to hosting harmful content, including disinformation and conspiracy theories. This study explores the use of o…
Data IntegrationStreamlining Knowledge Graph Creation with PyRML
Knowledge Graphs (KGs) are increasingly adopted as a foundational technology for integrating heterogeneous data in domains such as climate science, cultural heritage, and the life sciences. Declarative mapping languages …
Data IntegrationKnowledge GraphsTowards a Spatiotemporal Fusion Approach to Precipitation Nowcasting
With the increasing availability of meteorological data from various sensors, numerical models and reanalysis products, the need for efficient data integration methods has become paramount for improving weather forecasts…
Data IntegrationMultimodal Generative AI for Story Point Estimation in Software Development
This research explores the application of Multimodal Generative AI to enhance story point estimation in Agile software development. By integrating text, image, and categorical data using advanced models like BERT, CNN, a…
Data IntegrationManagementControl of Renewable Energy Communities using AI and Real-World Data
The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (EV) charg…
Data Integrationenergy managementReinforcement Learning (RL)SchedulingA Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these methods is the assumption that outcome measures are identical across dataset…
Causal InferenceData Integration