Search Results - Computing methodologies → Machine learning algorithms

High performance computing and artificial intelligence algorithms for dimensionality reduction of turbulent flows

Authors: Eiximeno Franch, Benet

Contributors: University/Department: Universitat Politècnica de Catalunya. Departament de Física

Thesis Advisors: Rodríguez Pérez, Ivette María, Lehmkuhl Barba, Oriol

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: reduced order models, high performance computing, deep learning, singular value decomposition, variational autoencoders, Àrees temàtiques de la UPC::Física, Àrees temàtiques de la UPC::Informàtica, 531/534 - Mecànica. Vibracions. Acústica, 004 - Informàtica

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/695826
https://dx.doi.org/10.5821/dissertation-2117-446576

Exploring genomic datasets through machine learning methods leveraging high-performance computing

Authors: Gómez Sánchez, Gonzalo

Contributors: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Berral García, Josep Lluís, Carrera Pérez, David

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Àrees temàtiques de la UPC::Informàtica

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/693566
https://dx.doi.org/10.5821/dissertation-2117-424249

Performance Comparison of Machine Learning Fault Detection and Diagnosis Algorithms in Organic Rankine Cycle Systems

Authors: Hernandez Naranjo, Jairo Andres Cendoya, Aitor Chaudoir, Basile et al.

Source: In proceedings of the 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 12 (2025-07-04); 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 29/06/2025-04/07/2025

Subject Terms: Fault Detection and Diagnosis, Support Vector Machine, Random Forest, Organic Rankine Cycles, Engineering, computing & technology, Energy, Ingénierie, informatique & technologie, Energie

Relation: info:eu-repo/grantAgreement/EC/HE/101069740; https://ecos2025.org/

Access URL: https://orbi.uliege.be/handle/2268/336998

Measuring Airport Service Quality Using Machine Learning Algorithms

Authors: Mohammed Salih Homaid Irene Moulitsas

Source: 2022 The 6th International Conference on Advances in Artificial Intelligence. :8-14

Subject Terms: airport service quality, machine learning, boosting, machine learning algorithms, sentiment analysis, ensemble methods, 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, computing methodologies, 02 engineering and technology, natural language processing

Access URL: https://dspace.lib.cranfield.ac.uk/handle/1826/18738

Evaluation model of physical education teaching effect based on machine learning algorithm with biomechanical integration.

Authors: Liu, Xiaoli

Source: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 3, p1-18, 18p

Subject Terms: MACHINE learning, BIOMECHANICS, EFFECTIVE teaching, PHYSICAL education, EVALUATION methodology, TEACHING methods, BIOLOGICALLY inspired computing, SCHOOL children

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Authors: Tayyabah Hasan Fahad Ahmad Muhammad Rizwan et al.

Source: Comput Intell Neurosci

Subject Terms: Deep Learning, 0202 electrical engineering, electronic engineering, information engineering, Quantum Theory, 02 engineering and technology, Computing Methodologies, Algorithms, Research Article

Access URL: https://downloads.hindawi.com/journals/cin/2022/6138434.pdf
https://pubmed.ncbi.nlm.nih.gov/35035461

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods

Authors: Muhammad Usman Tariq Shuhaida Binti Ismail

Source: Osong Public Health Res Perspect
Osong Public Health and Research Perspectives, Vol 15, Iss 2, Pp 115-136 (2024)

Subject Terms: 0301 basic medicine, deep learning, RC952-1245, Infectious and parasitic diseases, RC109-216, 02 engineering and technology, algorithms, artificial intelligence, neural networks, 3. Good health, 03 medical and health sciences, Special situations and conditions, 0202 electrical engineering, electronic engineering, information engineering, computing methodologies, Original Article, QA Mathematics, computer

File Description: text

Access URL: https://pubmed.ncbi.nlm.nih.gov/38621765
https://doaj.org/article/14eef2dfd4f24c2e88d4158cd9ec76fc

Quantum Machine Learning Algorithms for Drug Discovery Applications

Authors: Kushal Batra Kimberley M. Zorn Daniel H. Foil et al.

Source: Journal of Chemical Information and Modeling. 61:2641-2647

Subject Terms: Machine Learning, 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Support Vector Machine, SARS-CoV-2, Drug Discovery, COVID-19, Humans, Quantum Theory, Computing Methodologies, Algorithms, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/34032436
https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/zh/covidwho-1241784
https://pubmed.ncbi.nlm.nih.gov/34032436/
https://www.ncbi.nlm.nih.gov/pubmed/34032436
https://dblp.uni-trier.de/db/journals/jcisd/jcisd61.html#BatraZFMGLE21
http://www.ncbi.nlm.nih.gov/pubmed/34032436
https://doi.org/10.1021/acs.jcim.1c00166

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

Authors: Gianluca Mittone Nicoló Tonci Robert Birke et al.

Source: Proceedings of the 20th ACM International Conference on Computing Frontiers. :73-83

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computing methodologies, Distributed computing methodologies, Distributed algorithms, Machine learning, Machine learning approaches, Neural networks, Hardware, Emerging technologies, Analysis and design of emerging devices and systems, Emerging architectures, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Edge Computing, Energy Consumption, Federated Learning, Green Computing, RISC-V, 02 engineering and technology, Distributed, Parallel, and Cluster Computing (cs.DC), 01 natural sciences, RISC-V, Energy Consumption, Federated Learning, Edge Computing, Green Computing, Machine Learning (cs.LG)

File Description: application/pdf

Access URL: http://arxiv.org/abs/2302.07946
https://hdl.handle.net/11568/1202687
https://doi.org/10.1145/3587135.3592211
https://hdl.handle.net/2318/1898473
https://doi.org/10.1145/3587135.3592211
https://arxiv.org/abs/2302.07946

Online Metric Algorithms with Untrusted Predictions

Authors: Antonios Antoniadis Christian Coester Marek Eliáš et al.

Contributors: Antonios Antoniadis Christian Coester Marek Eliáš et al.

Source: Proceedings of the 37th International Conference on Machine Learning
Proceedings of Machine Learning Research

Subject Terms: FOS: Computer and information sciences, Online algorithms, Metrical task systems, 0102 computer and information sciences, 02 engineering and technology, [INFO] Computer Science [cs], 01 natural sciences, Computing methodologies, ML AUGMENTED ALGORITHMS, METRICAL TASK SYSTEMS, CACHING, caching, CACHING, COMPETITIVE ANALYSIS, METRICAL TASK SYSTEMS, Machine learning, 2023 OA procedure, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], competitive analysis, Data Structures and Algorithms (cs.DS), Theory of computation

File Description: application/pdf

Access URL: http://arxiv.org/abs/2003.02144
https://research.utwente.nl/en/publications/adaec16e-45d3-4d0c-a69d-05ea5e899fbf
https://doi.org/10.1145/3582689
https://ora.ox.ac.uk/objects/uuid:f6b96c83-9584-40ee-9df2-228bc6f05638
http://hdl.handle.net/21.11116/0000-0007-93BF-C
https://ruj.uj.edu.pl/xmlui/handle/item/311455
https://proceedings.icml.cc/static/paper_files/icml/2020/6657-Paper.pdf
https://ruj.uj.edu.pl/xmlui/handle/item/272144
https://dl.acm.org/doi/10.1145/3582689
https://hdl.handle.net/11565/4060543
https://doi.org/10.1145/3582689
https://hdl.handle.net/11565/4041704
https://proceedings.mlr.press/v119/antoniadis20a.html

Immunity-Based Orthogonal Weights Modification Algorithm: Immunity-based orthogonal weights modification algorithm

Authors: Xiaotao Liang Shuo Yin Xing Zhao et al.

Source: Communications on Applied Mathematics and Computation. 7:718-732

Subject Terms: neural network, continual learning (CL), computed tomography (CT), Computing methodologies for image processing, Artificial neural networks and deep learning, image processing

File Description: application/xml

Access URL: https://zbmath.org/8034296
https://doi.org/10.1007/s42967-024-00377-y

Towards an Automated AI Based Simulation Framework in Aerospace Engineering

Authors: Neda Ebrahimi Pour Frank Dressel Sabine Roller

Source: Proceedings of the 2025 International Conference on High Performance Computing in Asia-Pacific Region Workshops. :58-60

Subject Terms: Massively parallel algorithms, Machine learning, Applied computing, High-performance simulations, Massively parallel, Computing methodologies, Aerospace

Quantum computing algorithms: getting closer to critical problems in computational biology

Authors: Marchetti, Laura Nifosì, Riccardo Martelli, Pier Luigi et al.

Source: Brief Bioinform
Briefings in bioinformatics 23 (2022). doi:10.1093/bib/bbac437
info:cnr-pdr/source/autori:Marchetti L.; Nifosì R.; Martelli P. L.; Da Pozzo E.; Cappello V.; Banterle F.; Trincavelli M. L.; Martini C.; D'Elia M./titolo:Quantum computing algorithms: getting closer to critical problems in computational biology/doi:10.1093%2Fbib%2Fbbac437/rivista:Briefings in bioinformatics/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume:23

Subject Terms: Biomolecules, 0301 basic medicine, Quantum algorithms, 0303 health sciences, Computational Biology, Review, Genomics, Computing Methodologies, 03 medical and health sciences, biomolecules, genomics, molecular modelling, quantum algorithms, quantum machine learning, Quantum Theory, Algorithms, Molecular modelling, Quantum Machine Learning

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/36220772
https://academic.oup.com/bib/article/23/6/bbac437/6758194
https://doi.org/10.1093/bib/bbac437
https://hdl.handle.net/20.500.14243/463345
https://hdl.handle.net/11568/1156442
https://doi.org/10.1093/bib/bbac437

Towards Automatic Generation of Performance Models for Dynamic Tuning using Machine Learning

Authors: Alcaraz Rodriguez, Jordi

Thesis Advisors: jordi_alc@hotmail.com, Sikora, Anna, César Galobardes, Eduardo

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Aprenentatge automàtic, Aprendizaje automático, Machine learning, Eines de rendiment, Herramientas de rendimiento, Performance tools, Computació d'altes prestacions, Computación de altas prestaciones, High performance computing, Ciències Experimentals

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/675104

Human behavior analysis on political retweets using machine learning algorithms

Authors: Het Patel Aditya Kansara Boppuru Rudra Prathap et al.

Source: Measurement: Sensors, Vol 27, Iss , Pp 100768- (2023)

Subject Terms: Political retweet, Opinion mining, Text classification, Twitter, Computing methodologies, Machine learning algorithms, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2665917423001046; https://doaj.org/toc/2665-9174

Access URL: https://doaj.org/article/00468d3e8e5549e1ae6eac72e0a2118d

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

Authors: Petit, Quentin Li, Chong Emad, Nahid et al.

Contributors: Petit, Quentin Li, Chong Emad, Nahid et al.

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

Subject Terms: [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

File Description: application/pdf

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

Enhanced Methodology and Experimental Research for Caged Chicken Counting Based on YOLOv8.

Authors: Wu, Zhenlong Yang, Jikang Zhang, Hengyuan et al.

Source: Animals (2076-2615); Mar2025, Vol. 15 Issue 6, p853, 22p

Subject Terms: COMPUTER vision, POULTRY farms, POULTRY farming, CHICKENS, LIVESTOCK farms, DEEP learning, EDGE computing

Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey

Authors: Jon Perez-Cerrolaza Jaume Abella Markus Borg et al.

Contributors: Jon Perez-Cerrolaza Jaume Abella Markus Borg et al.

Source: Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Subject Terms: Artificial intelligence, Computer systems organization, Functional safety, Artificial intelligence--Computer programs, Intel·ligència artificial, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Safety critical systems, Robotics, Electrical Engineering, Electronic Engineering, Information Engineering, Computing methodologies, Autonomous systems, Dependable and fault-tolerant systems and networks, Datorsystem, Hardware, Computer Systems, Machine learning, Robotic autonomy, Inbäddad systemteknik, Elektroteknik och elektronik, Embedded Systems, Automated vehicles, Accident prevention, Engineering education, Ethical technology, Artificial intelligence technologies, Autonomous system, Cutting edges, Functional Safety, Human safety, Learn+, Machine learning algorithms, State of the art, Transportation domain, Security systems

File Description: application/pdf

Access URL: http://hdl.handle.net/10261/377985
https://hdl.handle.net/2117/407331
https://doi.org/10.1145/3626314
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-105483
http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-73311

Tabular and Deep Learning for the Whittle Index

Authors: Robledo Relaño, Francisco Borkar, Vivek Ayesta, Urtzi et al.

Contributors: Robledo Relaño, Francisco Borkar, Vivek Ayesta, Urtzi et al.

Source: ACM Transactions on Modeling and Performance Evaluation of Computing Systems. 9:1-21

Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, CCS Concepts: Computing methodologies → Sequential decision making Machine learning Reinforcement Learning Whittle Index Markov Decision Problem Multi-armed Restless Bandit, Computer Science - Machine Learning, 0209 industrial biotechnology, Computer Science - Artificial Intelligence, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 02 engineering and technology, Reinforcement Learning, 01 natural sciences, Markov Decision Problem, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), CCS Concepts, Artificial Intelligence (cs.AI), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Whittle Index, Computing methodologies → Sequential decision making Machine learning, 0101 mathematics

File Description: application/pdf

Access URL: http://arxiv.org/abs/2406.02057
https://hal.science/hal-04599291v1/document
https://doi.org/10.1145/3670686
https://hal.science/hal-04599291v1

GeoTS: A TSC framework for estimating geological formation to model carbon storage reservoirs

Authors: Salimath, Shwetha Bugiotti, Francesca Wlodarczyk, Sylvain et al.

Contributors: Salimath, Shwetha Bugiotti, Francesca Wlodarczyk, Sylvain et al.

Source: SIGKDD ; https://hal.science/hal-05184905 ; SIGKDD, ACM, Aug 2025, Toronto, Canada. pp.4773-4783, ⟨10.1145/3711896.3737228⟩

Subject Terms: Deep Learning Time series classification Carbon storage Lithography, Machine Learning, • Applied computing → Environmental sciences Deep Learning, Linear algebra algorithms, Feature selection, Cross-validation, Anomaly detection, Cluster analysis, • Computing methodologies → Supervised learning by classification, Information extraction, Autonomous database administration, Information systems → Data cleaning, CCS Concepts, Reservoirs, CCS Concepts Information systems → Data cleaning Autonomous database administration Information extraction • Computing methodologies → Supervised learning by classification Cluster analysis Anomaly detection Cross-validation Feature selection Linear algebra algorithms • Applied computing → Environmental sciences Deep Learning, Deep Learning, Time series classification, Carbon storage, Lithography, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDE]Environmental Sciences

Subject Geographic: Toronto

Time: Toronto, Canada

Availability: https://hal.science/hal-05184905
https://hal.science/hal-05184905v1/file/KDD%20GeoTS%20framework%20paper%20final_ver.pdf
https://doi.org/10.1145/3711896.3737228