Výsledky vyhledávání - "Large-scale data"

TCAD-Net: Integrating Transformer and Convolution for Weakly Supervised Anomaly Data Discovery

Autoři: Hongping Gan Hejie Zheng J. Nescolarde Selva a další

Přispěvatelé: Hongping Gan Hejie Zheng J. Nescolarde Selva a další

Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)

Témata: Transformer, Large-scale data, Consumer electronics, Anomaly data discovery, Imbalanced samples

Přístupová URL adresa: http://hdl.handle.net/10045/152229

Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models

Autoři: Philip Rutten Tim R. Mocking Jacqueline Cloos a další

Zdroj: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-18 (2025)

Témata: Gaussian mixture models, EM algorithm, Flow cytometry, Large-scale data, Clustering, Classification, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1471-2105

Přístupová URL adresa: https://doaj.org/article/9d9f2cf19adf4fea8f9bd7001c32c16d

Equivalence classes and conditional hardness in massively parallel computations

Autoři: Na Nongkai, Danupon Scquizzato, M.

Zdroj: Distributed computing. 35(2):165-183

Témata: Conditional hardness, Fine-grained complexity, Massively Parallel Computation, Complex networks, Computational complexity, Data handling, Graph theory, Complexity class, Fine grained, Large-scale data processing, Low bound, Massively parallels, Parallel Computation, Parallel computation model, Equivalence classes

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-319607
https://doi.org/10.1007/s00446-021-00418-2

Weak ties, strong facts: how influencers amplify public health communication on X (formerly Twitter)

Autoři: Wu Yanfang

Zdroj: Online Media and Global Communication, Vol 4, Iss 3, Pp 487-522 (2025)

Témata: large-scale data, x/twitter, informational social influence, normative social influence, social conformity, public health communication, Communication. Mass media, P87-96

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2749-9049

Přístupová URL adresa: https://doaj.org/article/1417cca9826b43dcb5a2ae5cf1c636f4

Confronting the data deluge: How artificial intelligence can be used in the study of plant stress

Autoři: Eugene Koh Rohan Shawn Sunil Hilbert Yuen In Lam a další

Zdroj: Comput Struct Biotechnol J
Computational and Structural Biotechnology Journal, Vol 23, Iss, Pp 3454-3466 (2024)

Témata: 0301 basic medicine, Artificial intelligence, 0303 health sciences, 03 medical and health sciences, Large-scale data, Plant stress resilience, Review Article, Large language models, TP248.13-248.65, Biotechnology

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39415960
https://doaj.org/article/8e70c6c34ee94438a589ed80497944c7

On the Unequal Burden of Obesity: Obesity’s Adverse Consequences Are Contingent on Regional Obesity Prevalence

Autoři: Jana B. Berkessel Tobias Ebert Jochen E. Gebauer a další

Přispěvatelé: Jana B. Berkessel Tobias Ebert Jochen E. Gebauer a další

Zdroj: Berkessel, J B, Ebert, T, Gebauer, J E & Rentfrow, P J 2024, ' On the Unequal Burden of Obesity : Obesity’s Adverse Consequences Are Contingent on Regional Obesity Prevalence ', Psychological Science, vol. 35, no. 11, pp. 1260-1277 . https://doi.org/10.1177/09567976241265037

Témata: large-scale data, Adult, Male, obesity, open data, Health Status Disparities, Middle Aged, weight bias, United States, United Kingdom, person-culture match, Socioeconomic Factors, Weight Prejudice, Prevalence, regions, Humans, Female, Obesity

Popis souboru: application/pdf; text/xml

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39356556
https://madoc.bib.uni-mannheim.de/68174/
https://doi.org/10.1177/09567976241265037
https://curis.ku.dk/ws/files/416349992/berkessel_et_al_2024_on_the_unequal_burden_of_obesity_obesity_s_adverse_consequences_are_contingent_on_regional_obesity_1.pdf
https://www.repository.cam.ac.uk/handle/1810/376350
https://doi.org/10.1177/09567976241265037

Pipeline for Semantic Segmentation of Large Railway Point Clouds

Autoři: Gabrielidis, Hugo Gatti, Filippo Vialle, Stephane a další

Přispěvatelé: Gabrielidis, Hugo Gatti, Filippo Vialle, Stephane a další

Zdroj: Lecture Notes in Computer Science ISBN: 9783031777301

Témata: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Deep Learning, 3D Point Cloud Deep Learning Semantic Segmentation Large Scale data, 3D Point Cloud, Semantic Segmentation, Large Scale data

Přístupová URL adresa: https://hal.science/hal-04811058v1
https://doi.org/10.1007/978-3-031-77731-8_16

An automated parallel genetic algorithm with parametric adaptation for distributed data analysis

Autoři: Laila Al-Terkawi Matteo Migliavacca

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Témata: Genetic algorithms (GAs), GAs parameter control, Classification, Large-scale data processing, Spark, Medicine, Science

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Přístupová URL adresa: https://doaj.org/article/5cd225c356bf47a0b1afd822f1560cde

Integrated single-cell and bulk transcriptomic analysis identifies a novel macrophage subtype associated with poor prognosis in breast cancer

Autoři: Qing Wang Yushuai Yu Liqiong Ruan a další

Zdroj: Cancer Cell International, Vol 25, Iss 1, Pp 1-14 (2025)

Témata: Macrophages, Large-scale data analysis, Tumor microenvironment, Prognosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1475-2867

Přístupová URL adresa: https://doaj.org/article/ac3599c985c54ab9b01fd55169c2d966

An Efficient and Scalable Approach to Build Co-occurrence Matrix for DNN's Embedding Layer

Autoři: Petit, Quentin Li, Chong Emad, Nahid a další

Přispěvatelé: Petit, Quentin Li, Chong Emad, Nahid a další

Zdroj: Proceedings of the 38th ACM International Conference on Supercomputing. :286-297

Témata: [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], [INFO.INFO-DC]Computer Science [cs]/Distributed, Information extraction, Natural language processing, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Parallel, Distributed computing, Sparse matrix, Computing methodologies → Massively parallel algorithms, and Cluster Computing [cs.DC], Large-scale data analytics, Control methods Deep Learning, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-CC] Computer Science [cs]/Computational Complexity [cs.CC], Co-occurrence matrix, Computing methodologies → Massively parallel algorithms Natural language processing Information extraction Control methods Deep Learning, Embedding

Popis souboru: application/pdf

Přístupová URL adresa: https://hal.science/hal-04719623v1
https://hal.science/hal-04719623v1/document
https://doi.org/10.1145/3650200.3656629

Optimizing ETL Processes for Big Data Applications

Autoři: Kola, Harish Goud¹

Zdroj: International Journal of Engineering and Management Research 14(5):148-161. 2024

THEORETICAL ASPECTS OF MACHINE LEARNING IN STATISTICAL DATA PROCESSING

Autoři: M.Mukhitdinova

Témata: Abstract: Machine learning (ML) has emerged as a powerful tool in the field of statistical data processing, enabling the analysis and extraction of insights from complex datasets. This theoretical study explores the fundamental concepts of machine learning in the context of statistical data processing, highlighting its application in various domains such as data mining, predictive analytics, and pattern recognition. Key principles of ML, including supervised and unsupervised learning, model selection, overfitting, and validation techniques, are discussed in relation to statistical methods. The integration of machine learning algorithms with statistical models provides a robust framework for analyzing large-scale data, enhancing predictive accuracy and decision-making processes. Furthermore, the paper examines challenges associated with the implementation of ML in statistical data processing, such as data quality, computational complexity, and interpretability of results. Theoretical advancements in ML, such as deep learning and ensemble methods, are also reviewed for their impact on improving statistical models. The study concludes by emphasizing the growing importance of machine learning in modern statistical analysis and its potential to revolutionize data-driven decision-making. Keywords: machine learning, statistical learning, supervised learning, unsupervised learning, reinforcement learning, risk minimization, regularization, optimization, probabilistic models, deep learning

Fast agglomerative clustering using approximate traveling salesman solutions

Autoři: Fränti Pasi Antero Sieranoja Sami Juhani

Zdroj: Journal of Big Data, Vol 12, Iss 1, Pp 1-18 (2025)

Témata: TK7885-7895, Computer engineering. Computer hardware, Travelling salesman problem, Large-scale data, Electronic computers. Computer science, Information technology, QA75.5-76.95, Data clustering, T58.5-58.64

Přístupová URL adresa: https://doaj.org/article/3cc7e404b8f04c0b81d61102f4bcf078

From prediction to prescription: Machine learning and Causal Inference

Autoři: Abécassis, Judith Dumas, Élise Alberge, Julie a další

Přispěvatelé: Abécassis, Judith Dumas, Élise Alberge, Julie a další

Témata: large scale data, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], machine learning, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], data-driven decision making, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, personalized medicine, causal inference, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]

Popis souboru: application/pdf

Přístupová URL adresa: https://hal.science/hal-04774700v1
https://doi.org/10.1146/annurev-biodatasci-103123-095750
https://hal.science/hal-04774700v1/document

Empowering collaborative and reproducible large-scale data analytics with D4Science

Autoři: Assante M. Lettere M. Oliviero A. a další

Témata: Large-Scale Data Analytics, Reproducible, D4science, Collaborative

Popis souboru: application/pdf

Přístupová URL adresa: https://ercim-news.ercim.eu/en140/special/empowering-collaborative-and-reproducible-large-scale-data-analytics-with-d4science
https://hdl.handle.net/20.500.14243/545886

Data-driven Network Connectivity Analysis: An Underestimated Metric

Autoři: Xu, J Nair, DJ Jayakumar Nair, Divya

Zdroj: IEEE Access, Vol 12, Pp 60908-60927 (2024)

Témata: large-scale data, relative size of largest connected component (RSLCC), network structure analysis, anzsrc-for: 4605 Data Management and Data Science, 05 social sciences, anzsrc-for: 46 Information and Computing Sciences, 0211 other engineering and technologies, 02 engineering and technology, TK1-9971, 4605 Data Management and Data Science, anzsrc-for: 40 Engineering, Network connectivity metric, deep neural network (DNN), 46 Information and Computing Sciences, 0502 economics and business, network disruption degree (NDD), Generic health relevance, Electrical engineering. Electronics. Nuclear engineering, anzsrc-for: 09 Engineering, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 10 Technology

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/e62b4d8ca7df4847814a393e980cf4d5

Efficient Management and Processing of Massive InSAR Images Using an HPC-Based Cloud Platform

Autoři: Zherong Wu Peifeng Ma Xinyang Zhang a další

Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2866-2876 (2024)

Témata: Ocean engineering, time-series interferometric synthetic aperture radar (InSAR), QC801-809, Geophysics. Cosmic physics, 0401 agriculture, forestry, and fisheries, 04 agricultural and veterinary sciences, High-performance computing (HPC), TC1501-1800, 01 natural sciences, large-scale data processing, 0105 earth and related environmental sciences

Přístupová URL adresa: https://doaj.org/article/27166416bc3a4fbebfe1c1b20e2d80c5

Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining

Autoři: Xin Dai Haza Nuzly Abdull Hamed Qichen Su a další

Zdroj: IEEE Access, Vol 12, Pp 195111-195130 (2024)

Témata: inverted index, depth-first search, Frequent itemset mining, finite automata, Electrical engineering. Electronics. Nuclear engineering, large-scale data analysis, TK1-9971

Přístupová URL adresa: https://doaj.org/article/11e4964b767c498bbe45f49a936e6e9a

N6-methyladenosine regulators in hepatocellular carcinoma: investigating the precise definition and clinical applications of biomarkers

Autoři: Xiaokai Yan Yao Qi Xinyue Yao a další

Zdroj: Biology Direct, Vol 19, Iss 1, Pp 1-18 (2024)

Témata: Hepatocellular carcinoma, m6A regulators, Large-scale data analysis, Precise definition of biomarkers, Risk models, Clinical applications, Biology (General), QH301-705.5

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1745-6150

Přístupová URL adresa: https://doaj.org/article/7a4412f01cbd4f63ad65fc124dd4b681

Analysis & Prediction of New York City Taxi and Uber Demands

Autoři: Diego Correa Christian Moyano

Zdroj: Journal of Applied Research and Technology. 21:886-898

Témata: Large scale data analysis, GPS-enabled taxi data, 11. Sustainability, Visual analytics, 0202 electrical engineering, electronic engineering, information engineering, New York City, 02 engineering and technology, Machine learning algorithms, 0204 chemical engineering, Taxi and Uber demand prediction

Popis souboru: application/pdf