Search Results - "Data-driven algorithm"

How Much Data Is Sufficient to Learn High-Performing Algorithms?: How much data is sufficient to learn high-performing algorithms?

Authors: Maria-Florina Balcan Dan Deblasio Travis Dick et al.

Source: Journal of the ACM. 71:1-58

Subject Terms: 0301 basic medicine, data-driven algorithm design, 0303 health sciences, 03 medical and health sciences, machine learning, computational biology, General topics in the theory of algorithms, Learning and adaptive systems in artificial intelligence, automated algorithm design, automated algorithm configuration

File Description: application/xml

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

Authors: Long, Mark C Rooklyn, Jordan

Source: Journal of Causal Inference. 12(1)

Subject Terms: Mathematical Sciences, Economics, Statistics, data-driven algorithm, regression discontinuity, bootstrap

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/7w59p8tf
https://escholarship.org/content/qt7w59p8tf/qt7w59p8tf.pdf

Dual-Stage Deep Learning for Long-Term Voltage Instability Prediction and Conditional OLTC Blocking–Load Shedding Decision Support

Authors: Falih K. H. Alabbas Mahtab Khalilifar S. Mohammad Shahrtash et al.

Source: IEEE Access, Vol 13, Pp 196155-196166 (2025)

Subject Terms: Power system stability assessment, voltage instability mitigation, OLTC blocking efficacy, real-time data driven algorithm, dual-stage deep learning model, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/11250623/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/881fc0385f9043da8334c981c7b41c4d

Online Positioning of a Drone-Mounted Base Station in Emergency Scenarios

Authors: T.R. Pijnappel J.L. van den Berg S.C. Borst et al.

Contributors: T.R. Pijnappel J.L. van den Berg S.C. Borst et al.

Source: IEEE Transactions on Vehicular Technology, pp. 1-15.

Subject Terms: Optimization, rechtvaardigheid en sterke instellingen, SDG 16 - Peace, SDG 16 – Vrede, Base stations, 02 engineering and technology, Disasters, 5G mobile communication systems, Search problems, 0202 electrical engineering, electronic engineering, information engineering, data-driven algorithm, Heuristic algorithms, Drones, Dynamic traffic hotspot, Measurement, Resource management, drone base station positioning, drone base stations, Justice and Strong Institutions, Benchmarking, Dynamic traffic, dynamic traffic hotspot, Uncertainty analysis, Heuristics algorithm, Hotspots, 5G

Access URL: https://research.tue.nl/en/publications/1d794fbd-1aea-498e-b798-93867af9868f
https://doi.org/10.1109/TVT.2023.3329960
https://research.utwente.nl/en/publications/b309d135-c2bb-450e-b0eb-076832d57d5e
https://doi.org/10.1109/TVT.2023.3329960
https://resolver.tno.nl/uuid:00251193-7a8f-4315-b8e5-01544c9a2a3f

A Robust and Efficient Ensemble of Diversified Evolutionary Computing Algorithms for Accurate Robot Calibration

Authors: Tinghui Chen Shuai Li Yan Qiao et al.

Source: IEEE Transactions on Instrumentation and Measurement. 73:1-14

Subject Terms: 0209 industrial biotechnology, evolutionary computing (EC), Absolute positioning accuracy, and Infrastructure, 0202 electrical engineering, electronic engineering, information engineering, data-driven algorithm, 02 engineering and technology, Innovation, kinematic parameters, SDG 9 - Industry, industrial robot

Access URL: https://cris.vtt.fi/en/publications/25626da7-a3ee-4f93-97ed-1106d58614f8
https://doi.org/10.1109/TIM.2024.3363783

Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine

Authors: Pulga, Leonardo Forte, Claudio Siliato, Alfio et al.

Source: SAE International Journal of Engines. 17:237-253

Subject Terms: Industrial emissions, Knowledge management, Particles (particulate matter), Particulate emissions, AI-pipeline, Automotives, Current production, Data-driven algorithm, Particle emissions, Particulates, Performance engines, Research and development, Uncertainty evaluation, Virtual sensor, Quality control, PN10, 9. Industry and infrastructure, 13. Climate action, 7. Clean energy

File Description: application/pdf

Real-Time data-driven average active period method for bottleneck detection

Authors: Subramaniyan, Mukund Skoogh, Anders Gopalakrishnan, Maheshwaran et al.

Source: StreaMod - Effektiv modellering och beslutsstöd för faktabaserad produktionsutveckling International Journal of Design and Nature and Ecodynamics. 11(3):428-437

Subject Terms: Bottleneck detection, Average active duration, Maintenance, Data-driven algorithm, Production system

File Description: electronic

Access URL: https://research.chalmers.se/publication/244931
http://publications.lib.chalmers.se/records/fulltext/244931/local_244931.pdf

A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG

Authors: Yaping Zhao Jingsi Huang Endong Xu et al.

Source: IET Renewable Power Generation, Vol 18, Iss 3, Pp 442-455 (2024)

Subject Terms: deep reinforcement learning, 13. Climate action, hydrogen device, data‐driven algorithm, integrated renewable energy system, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, real‐time scheduling, TJ807-830, 02 engineering and technology, 7. Clean energy, Renewable energy sources

Access URL: https://doaj.org/article/9927128f128b47f6ad01f2f1504882fb

Data-Driven Solution Portfolios

Authors: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Contributors: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Subject Terms: FOS: Computer and information sciences, data-driven algorithm design, Computer Science - Data Structures and Algorithms, solution portfolios, Data Structures and Algorithms (cs.DS), ddc:004, matroids

File Description: application/pdf

Access URL: http://arxiv.org/abs/2412.00717
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.46

Designing a Knowledge Graph with Data-Driven Algorithms to Optimize Matching People and Jobs and Ranking Skills

Authors: Elnaz Nasirzadeh

Source: مطالعات منابع انسانی, Vol 13, Iss 3, Pp 166-193 (2023)

Subject Terms: knowledge graph, data-driven algorithm, matching people and jobs, skills ranking, job opportunity, Employee participation in management. Employee ownership. Industrial democracy. Works councils, HD5650-5660

File Description: electronic resource

Relation: http://www.jhrs.ir/article_189966_13ec2eeb4218aacb4d8a1b62bb2acbdc.pdf; https://doaj.org/toc/2783-0624

Access URL: https://doaj.org/article/b0f004f1d6d04241af3f10f43a08c82d

Towards a validated definition of the clinical transition to secondary progressive multiple sclerosis: A study from the Italian MS Register

Authors: Pietro Iaffaldano Giuseppe Lucisano Tommaso Guerra et al.

Contributors: Pietro Iaffaldano Giuseppe Lucisano Tommaso Guerra et al.

Source: Multiple Sclerosis Journal. 28:2243-2252

Subject Terms: Multiple Sclerosis, Multiple Sclerosis, Chronic Progressive, Multiple sclerosis, 03 medical and health sciences, Multiple Sclerosis, Relapsing-Remitting, 0302 clinical medicine, big data, Area Under Curve, data-driven algorithm, disease registry, prognosis, secondary progressive, Humans, Multiple sclerosi, prognosi

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/35971322
https://hdl.handle.net/11573/1683615
https://doi.org/10.1177/13524585221114007
https://hdl.handle.net/11584/346857
https://doi.org/10.1177/13524585221114007

Data-driven estimation of the inertia moment of wind turbines: A new ice-detection algorithm

Authors: Stotsky, Alexander Egardt, Bo

Source: Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering. 227(6):552-555

Subject Terms: Wind turbine, estimation of the inertia moment, data-driven algorithm, icing and shedding events

File Description: electronic

Access URL: https://research.chalmers.se/publication/181531
http://dx.doi.org/10.1177/0959651813489279

Dynamic surface river pollution identification by a hybrid multivariate-based anomaly detection algorithm

Authors: Jiang, Dexun Zhu, Hao Liu, Jie et al.

Source: Journal of Cleaner Production, 467

Subject Terms: River pollution identification, Multi-dimensional anomaly detection, Dynamic warning threshold, Data-driven algorithm, Receiver Operating characteristic curve

File Description: application/application/pdf

Relation: info:eu-repo/semantics/altIdentifier/wos/001325713200001; http://hdl.handle.net/20.500.11850/679840

Availability: https://hdl.handle.net/20.500.11850/679840
https://doi.org/10.3929/ethz-b-000679840

Data-Driven Algorithms for Two-Location Inventory Systems

Authors: Zijun Zhong Mingyang Yuan Zhou He

Source: Systems, Vol 12, Iss 5, p 153 (2024)

Subject Terms: data-driven algorithm, transshipment, inventory management, regret analysis, Systems engineering, TA168, Technology (General), T1-995

Relation: https://www.mdpi.com/2079-8954/12/5/153; https://doaj.org/toc/2079-8954; https://doaj.org/article/fbe6a61d237745e0bbf81fd142aee3ce

Availability: https://doi.org/10.3390/systems12050153
https://doaj.org/article/fbe6a61d237745e0bbf81fd142aee3ce

Data-Driven Contact-Based Thermosensation for Enhanced Tactile Recognition

Authors: Tiancheng Ma Min Zhang

Source: Sensors, Vol 24, Iss 2, p 369 (2024)

Subject Terms: data-driven algorithm, heat transfer modeling, quantitative thermosensation, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/24/2/369; https://doaj.org/toc/1424-8220; https://doaj.org/article/26b4cb2f3c064655afea2ab5358589a5

Availability: https://doi.org/10.3390/s24020369
https://doaj.org/article/26b4cb2f3c064655afea2ab5358589a5

MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia

Authors: Manera, Ana L Dadar, Mahsa Van Swieten, John Cornelis et al.

Contributors: Manera, Ana L Dadar, Mahsa Van Swieten, John Cornelis et al.

Source: Manera, A L, Dadar, M, van Swieten, J C, Borroni, B, Sanchez-Valle, R, Moreno, F, Laforce, R, Graff, C, Synofzik, M, Galimberti, D, Rowe, J B, Masellis, M, Tartaglia, M C, Finger, E, Vandenberghe, R, de Mendonca, A, Tagliavini, F, Santana, I, Butler, C R, Gerhard, A, Danek, A, Levin, J, Otto, M, Frisoni, G, Ghidoni, R, Sorbi, S, Rohrer, J D, Ducharme, S, Louis Collins, D, Rosen, H, Dickerson, B C, Domoto-Reilly, K, Knopman, D, Boeve, B F, Boxer, A L, Kornak, J, Miller, B L, Seeley, W W, Gorno-Tempini, M-L, McGinnis, S, Mandelli, M L, Afonso, S N, Almeida, M R, Anderl-Straub, S, Andersson, C, Antonell, A, Archetti, S, Arighi, A, Balasa, M, Barandiaran, M, Bargalló, N, Bartha, R, Bender, B, Benussi, A, Benussi, L, Bessi, V, Binetti, G, Black, S, Bocchetta, M, Borrego-Ecija, S, Bras, J, Bruffaerts, R, Caroppo, P, Cash, D, Castelo-Branco, M, Convery, R, Cope, T, Cosseddu, M, de Arriba, M, di Fede, G, Díaz, Z, Duro, D, Fenoglio, C, Ferrari, C, Ferreira, C, Ferreira, C B, Flanagan, T, Fox, N, Freedman, M, Fumagalli, G, Gabilondo, A, Gasparotti, R, Gauthier, S, Gazzina, S, Giaccone, G, Gorostidi, A, Greaves, C, Guerreiro, R, Heller, C, Hoegen, T, Indakoetxea, B, Jelic, V, Jiskoot, L, Karnath, H-O, Keren, R, Leitão, M J, Lladó, A, Lombardi, G, Loosli, S, Maruta, C, Mead, S, Meeter, L, Miltenberger, G, van Minkelen, R, Mitchell, S, Moore, K M, Nacmias, B, Neason, M, Nicholas, J, Öijerstedt, L, Olives, J, Ourselin, S, Padovani, A, Panman, J, Papma, J, Peakman, G, Piaceri, I, Pievani, M, Pijnenburg, Y, Polito, C, Premi, E, Prioni, S, Prix, C, Rademakers, R, Redaelli, V, Rittman, T, Rogaeva, E, Rosa-Neto, P, Rossi, G, Rossor, M, Santiago, B, Scarpini, E, Schönecker, S, Semler, E, Shafei, R, Shoesmith, C, Tábuas-Pereira, M, Tainta, M, Taipa, R, Tang-Wai, D, Thomas, D L, Thonberg, H, Timberlake, C, Tiraboschi, P, Todd, E, Vandamme, P, Vandenbulcke, M, Veldsman, M, Verdelho, A, FTLDNI Investigators, GENFI Consortium, Villanua, J, Warren, J, Wilke, C, Woollacott, I, Wlasich, E, Zetterberg, H & Zulaica, M 2021, 'MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia', Journal of Neurology, Neurosurgery and Psychiatry, vol. 92, no. 6, pp. 608-616. https://doi.org/10.1136/jnnp-2020-324106
Journal of neurology, neurosurgery, and psychiatry 92(6), 608-616 (2021). doi:10.1136/jnnp-2020-324106

Subject Terms: 618.97, 03 medical and health sciences, Frontotemporal dementia, behavioural variant, Cognitive neurology, 0302 clinical medicine, SDG 3 - Good Health and Well-being, MRI data-driven, algorithm, behavioural variant frontotemporal dementia, ddc:610, GENFI Consortium, FTLDNI investigators, 3. Good health

File Description: application/pdf; application/vnd.openxmlformats-officedocument.wordprocessingml.document; image/tiff; text/xml

Access URL: https://discovery.ucl.ac.uk/10127855/3/Rohrer_MRI%20data-driven%20algorithm%20for%20the%20diagnosis%20of%20behavioural%20variant%20frontotemporal%20dementia_AAM.pdf
https://pubmed.ncbi.nlm.nih.gov/33722819
https://hdl.handle.net/11368/3097048
https://jnnp.bmj.com/content/92/6/608
https://pure.eur.nl/en/publications/93f5105e-0ca9-4a81-a5ba-a961b0c4d20c
https://doi.org/10.1136/jnnp-2020-324106
https://www.scilit.net/article/6f7ea9d019179cc867b55a9b364ff351?action=show-references
https://europepmc.org/article/MED/33722819
https://research.vumc.nl/en/publications/mri-data-driven-algorithm-for-the-diagnosis-of-behavioural-varian
https://jnnp.bmj.com/content/jnnp/early/2021/03/14/jnnp-2020-324106.full.pdf
https://oparu.uni-ulm.de/xmlui/handle/123456789/38372
https://pubmed.ncbi.nlm.nih.gov/33722819/
https://research.vumc.nl/en/publications/ca61a396-c782-478c-98b3-58c04ab9c669
https://pub.dzne.de/record/154809
https://archive-ouverte.unige.ch/unige:173329
https://doi.org/10.1136/jnnp-2020-324106
https://hdl.handle.net/10451/50229
https://hdl.handle.net/11379/551135
https://doi.org/10.1136/jnnp-2020-324106
https://hdl.handle.net/11368/3097048
https://doi.org/10.1136/jnnp-2020-324106
https://jnnp.bmj.com/content/92/6/608
https://www.repository.cam.ac.uk/handle/1810/316875
https://doi.org/10.1136/jnnp-2020-324106
https://doi.org/10.17863/cam.63987
https://hdl.handle.net/2158/1258897
https://doi.org/10.1136/jnnp-2020-324106
https://pubmed.ncbi.nlm.nih.gov/33722819/
https://discovery-pp.ucl.ac.uk/id/eprint/10127855/

Transition to secondary progression in relapsing-onset multiple sclerosis: Definitions and risk factors

Authors: Pietro Iaffaldano Giuseppe Lucisano Francesco Patti et al.

Contributors: Pietro Iaffaldano Giuseppe Lucisano Francesco Patti et al.

Source: Multiple Sclerosis Journal. 27:430-438

Subject Terms: Big data, Data-driven algorithm, Disease registry, Multiple sclerosis, Prognosis, Secondary progressive, Multiple Sclerosis, Multiple Sclerosis, Chronic Progressive, 3. Good health, 03 medical and health sciences, Multiple Sclerosis, Relapsing-Remitting, 0302 clinical medicine, big data, Recurrence, Risk Factors, data-driven algorithm, disease registry, prognosis, secondary progressive, Disease Progression, Humans, Multiple sclerosi, 10. No inequality, prognosi

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/33210986
https://pubmed.ncbi.nlm.nih.gov/33210986/
https://cris.unibo.it/handle/11585/802574
https://iris.unimore.it/handle/11380/1251077
https://moh-it.pure.elsevier.com/en/publications/transition-to-secondary-progression-in-relapsing-onset-multiple-s-4
https://www.ncbi.nlm.nih.gov/pubmed/33210986
https://journals.sagepub.com/doi/full/10.1177/1352458520974366

Data-Driven Solution Portfolios

Authors: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Subject Terms: data-driven algorithm design, matroids, solution portfolios

Relation: 16th Innovations in Theoretical Computer Science Conference (ITCS 2025); Leibniz International Proceedings in Informatics (LIPIcs); 325; The 16th Annual Innovations in Theoretical Computer Science (ITCS) conference; #PLACEHOLDER_PARENT_METADATA_VALUE#; MB22.00054; https://infoscience.epfl.ch/handle/20.500.14299/247349

Availability: https://doi.org/10.4230/LIPIcs.ITCS.2025.46
https://infoscience.epfl.ch/handle/20.500.14299/247349
https://hdl.handle.net/20.500.14299/247349

A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network

Authors: Huan Long Chang Chen Wei Gu et al.

Source: IEEE Access, Vol 8, Pp 24675-24686 (2020)

Subject Terms: abnormal detection, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, data-driven algorithm, risk assessment, control chart, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Power loss, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08976185.pdf
https://doaj.org/article/0b982505c351483dbe0dacb2d835fa4d
https://ieeexplore.ieee.org/document/8976185/
https://doi.org/10.1109/ACCESS.2020.2970548
https://dblp.uni-trier.de/db/journals/access/access8.html#LongCGXWL20
https://doaj.org/article/0b982505c351483dbe0dacb2d835fa4d

Dynamic surface river pollution identification by a hybrid multivariate-based anomaly detection algorithm

Authors: Dexun Jiang Hao Zhu Jie Liu et al.

Source: Journal of Cleaner Production, 467

Subject Terms: 13. Climate action, Multi-dimensional anomaly detection, 4. Education, River pollution identification, Dynamic warning threshold, Data-driven algorithm, Receiver Operating characteristic curve, 14. Life underwater, 6. Clean water

File Description: application/application/pdf

Access URL: http://hdl.handle.net/20.500.11850/679840