Výsledky vyhľadávania - "Green AI"

AI as an Environmental Challenge: Mapping Safeguards in EU Environmental and Climate Law to Address the ‘Silence’ in the EU AI Act: Mapping Safeguards in EU Environmental and Climate Law to Address the ‘Silence’ in the EU AI Act

Autori: Pouikli, Kleoniki Tsakalogianni, Ifigeneia

Zdroj: European Energy and Environmental Law Review. 34:25-37

Predmety: sustainability reporting, Artificial Intelligence, AI Act, sustainability by design, twin transitions, SDG 13 - Climate Action, Green AI, Lifecycle assessment, SDG 7 - Affordable and Clean Energy, sustainability, SDG 12 - Responsible Consumption and Production, EU Green Deal

Responsible and Sustainable AI: Considering Energy Consumption in Automated Text Classification Evaluation Tasks

Autori: Kaplan, Angelika Keim, Jan Greiner, Lukas a ďalší

Zdroj: 2025 IEEE/ACM 9th International Workshop on Green and Sustainable Software (GREENS). :76-83

Predmety: ddc:004, text classification, energy consumption, DATA processing & computer science, Green AI, natural language processing, sustainability metrics

Popis súboru: application/pdf

Toward trustworthy and sustainable clinical decision support by training ensembles of specialized logistic regressors

Autori: Cuzzocrea, Alfredo Folino, Francesco Pontieri, Luigi a ďalší

Zdroj: Neural Computing and Applications. 37:18233-18274

Predmety: XAI, Clinical DSSs, Machine learning, Green AI, Outcome prediction

Popis súboru: application/pdf

Prístupová URL adresa: https://hdl.handle.net/20.500.14243/547685
https://doi.org/10.1007/s00521-025-11360-w
https://hdl.handle.net/20.500.11770/388160
https://doi.org/10.1007/s00521-025-11360-w

Innovating for Tomorrow: The Convergence of Software Engineering and Green AI

Autori: Luís Cruz Xavier Franch Silverio Martínez-Fernández

Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Predmety: Software engineering, Àrees temàtiques de la UPC::Informàtica::Enginyeria del software, Green software, Sustainability, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Green AI

Popis súboru: application/pdf

Prístupová URL adresa: https://hdl.handle.net/2117/439624
https://doi.org/10.1145/3712007

The force of few: boosting deviance detection in data scarcity scenarios through self-supervised learning and pattern-based encoding

Autori: Francesco Folino Gianluigi Folino Massimo Guarascio a ďalší

Zdroj: Soft Computing. 29:3675-3690

Predmety: Container log analysis, Multi-task deep learning, Green AI, Process deviance detection

Popis súboru: application/pdf

Prístupová URL adresa: https://hdl.handle.net/20.500.14243/544032
https://doi.org/10.1007/s00500-025-10646-4

Designing ML-Enabled Software Systems with ML Model Composition: A Green AI Perspective: A Green AI Perspective

Autori: Chitakunye, Rumbidzai

Zdroj: 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). :253-255

Predmety: Machine Learning Model Composition, Green AI, Machine Learning-Enabled Systems

Prístupová URL adresa: https://research.vu.nl/en/publications/c2900789-5581-4084-8ade-e7cccd1dce9d
https://doi.org/10.1109/CAIN66642.2025.00040
https://hdl.handle.net/1871.1/c2900789-5581-4084-8ade-e7cccd1dce9d

Energy-efficient green AI architectures for circular economies through multi-layered sustainable resource optimization framework

Autori: Ripal Ranpara

Zdroj: Discover Sustainability, Vol 6, Iss 1, Pp 1-15 (2025)

Predmety: Green AI, Circular economies, Energy efficiency, Sustainable resource management, Machine learning frameworks, Optimization algorithms, Environmental sciences, GE1-350

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2662-9984

Prístupová URL adresa: https://doaj.org/article/63fab4e7b11345f0a6e69cc65ac6e122

kausalnotation & ::kausalmodus – Eine semantische Syntax zur kausalen Analyse mit KI

Autori: Schloemer, Joost H.

Predmety: Kausalität, Causal AI, Promptnotation, Disambiguierung, kausalmodus, kausalnotation, Governance, Arbeitsschutz, Green AI, Reproduzierbarkeit

Zwei Doppelpunkte als Schlüssel zur Generative Engine-Optimization

Autori: Schloemer, Joost H. Schloemer

Predmety: Notation, Semantik, Künstliche Intelligenz, Generative Engine Optimization, GEO, SEO, Search Generative Experience, SGE, Disambiguierung, Ambiguität, Bias, Probability, Energieeffizienz, Green AI, Sprachmodelle, KI-Transparenz, Promptnotation, Open Science, Attribution, Digitalisierung, Vereinskontext

Route-centric ant-inspired memories enable panoramic route-following in a car-like robot

Autori: Gattaux, Gabriel Wystrach, Antoine Serres, Julien a ďalší

Prispievatelia: Gattaux, Gabriel Wystrach, Antoine Serres, Julien a ďalší

Zdroj: Nature Communications. 16

Predmety: Bionics, [SCCO.NEUR] Cognitive science/Neuroscience, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Green AI, Route-following, Familiarity-based navigation, Biomimetism, Neural network, ant-inspired, Bioinspiration, Navigation, Biorobotics, Bio-inspired robotics, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Familiarity-based vision, Biomimicry, Mushroom Bodies, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

Popis súboru: application/pdf

Prístupová URL adresa: https://hal.science/hal-04820343v2/document
https://doi.org/10.1038/s41467-025-62327-3
https://hal.science/hal-04820343v2

Artificial Intelligence and the Energy Dilemma: Challenges and Opportunities for Electrical Engineers

Autori: Bernardino dos Passos, Andre Luiz

Predmety: Artificial Intelligence Energy Consumption Electrical Engineering Data Centers Smart Grids Edge Computing Green AI Sustainable Technology Power Systems Machine Learning Renewable Energy Energy Efficiency AI Hardware Energy Optimization Digital Infrastructure

Data-driven predictive models for sustainable smart buildings

Autori: Prabhu Rajaram Gnana Swathika O․V․

Zdroj: Results in Engineering, Vol 27, Iss, Pp 105916-(2025)

Predmety: Technology, Sustainability, Smart buildings, Sensors, Energy management, Green AI

Prístupová URL adresa: https://doaj.org/article/77c4aac896294fc989f76336cd95e62b

Online Knowledge Distillation and Deep Supervision in HRNet: Green AI for Preterm Infants’ Pose Estimation

Autori: Alessandro Cacciatore Daniele Berardini Vito Scaraggi a ďalší

Zdroj: ACM Transactions on Computing for Healthcare.

Predmety: Green AI, Computational Efficiency, Knowledge Distillation, Deep Supervision, Preterm Infants

Popis súboru: application/pdf

GAISSALabel: A Tool for Energy Labeling of ML Models

Autori: Pau Duran Joel Castaño Cristina Gómez a ďalší

Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Predmety: Computer Science - Software Engineering, Software engineering, Àrees temàtiques de la UPC::Informàtica::Enginyeria del software, Green software, Sustainability, Green AI

Popis súboru: application/pdf

Prístupová URL adresa: http://arxiv.org/abs/2401.17150

Energy-Efficient AI for Medical Diagnostics: Performance and Sustainability Analysis of ResNet and MobileNet

Autori: Zia Ur Rehman Uzair Hassan Saif Ul Islam a ďalší

Zdroj: Studies in Health Technology and Informatics ISBN: 9781643685960
Rehman, Z U, Hassan, U, Islam, S U, Gallos, P & Boudjadar, J 2025, Energy-Efficient AI for Medical Diagnostics : Performance and Sustainability Analysis of ResNet and MobileNet. in Intelligent Health Systems – From Technology to Data and Knowledge. IOS Press, Studies in Health Technology and Informatics, vol. 327, pp. 1225-1229. https://doi.org/10.3233/SHTI250585

Predmety: Energy-efficient AI, Green AI, Artificial Intelligence (AI), MobileNet, Machine learning (ML), Medical diagnostics, ResNet, Energy consumption, Deep Learning, Sustainability, Artificial Intelligence, CheXpert dataset, Deep learning (DL), Power efficiency, Humans, Diagnosis, Computer-Assisted

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40380690
http://www.scopus.com/inward/record.url?scp=105005816986&partnerID=8YFLogxK
https://pure.au.dk/portal/en/publications/8e6621b1-8d07-479e-9f50-9a2a2bc7d0a8
https://doi.org/10.3233/SHTI250585

Journal of Soft Computing and Computer Applications

Predmety: ai and green ai, computer applications, software engineering, computer security and cyber security, computer networks and cloud computing, multimedia, Computer software, QA76.75-76.765

Popis súboru: electronic resource

Relation: https://jscca.uotechnology.edu.iq/jscca/aimsandscope.html; https://jscca.uotechnology.edu.iq/jscca/; https://jscca.uotechnology.edu.iq/jscca/information_for_authors.html

Prístupová URL adresa: https://doaj.org/toc/3008-1084

Explainable Process Deviance Discovery with Data-Efficient Deep Learning

Autori: Folino F. Folino G. Guarascio M. a ďalší

Zdroj: Lecture Notes in Computer Science ISBN: 9783031812408
Numerical Computations: Theory And Algorithms (Numta 2023), Pizzo (VV), 14-20/06/2023
info:cnr-pdr/source/autori:Francesco Folino, Gianluigi Folino, Massimo Guarascio, Luigi Pontieri/congresso_nome:Numerical Computations: Theory And Algorithms (Numta 2023)/congresso_luogo:Pizzo (VV)/congresso_data:14-20%2F06%2F2023/anno:2023/pagina_da:/pagina_a:/intervallo_pagine

Predmety: Deep Learning, Process Deviance Mining, Deep Learning, Multi-task Learning, Green AI, Container Log Analysis, Container Log Analysis, Green AI, Multi-task Learning, Process Deviance Mining

Popis súboru: application/pdf

Prístupová URL adresa: https://publications.cnr.it/doc/490143
http://www.cnr.it/prodotto/i/490143
https://hdl.handle.net/20.500.14243/452013
https://hdl.handle.net/20.500.14243/530985
https://doi.org/10.1007/978-3-031-81241-5_6

How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection

Autori: Omar, Rafiullah Bogner, Justus Leest, Joran a ďalší

Zdroj: 2024 10th International Conference on ICT for Sustainability (ICT4S). :172-182

Predmety: Machine Learning, Software Engineering (cs.SE), FOS: Computer and information sciences, machine learning, controlled experiment, 11. Sustainability, Green AI, Software Engineering, concept drift, 7. Clean energy, energy efficiency, 12. Responsible consumption, Machine Learning (cs.LG)

Popis súboru: application/pdf

Prístupová URL adresa: http://arxiv.org/abs/2404.19452
https://hdl.handle.net/1871.1/bf2c715a-4f64-42f7-9f93-29020c458374
https://doi.org/10.1109/ICT4S64576.2024.00026
https://research.vu.nl/en/publications/bf2c715a-4f64-42f7-9f93-29020c458374
https://hdl.handle.net/11697/257821
https://doi.org/10.1109/ict4s64576.2024.00026

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Autori: Philipp Wiesner Ramin Khalili Dennis Grinwald a ďalší

Zdroj: E-Energy

Predmety: FOS: Computer and information sciences, Computer Science - Machine Learning, federated learning, green AI, sustainable computing, 7. Clean energy, Machine Learning (cs.LG), Computer Science - Distributed, Parallel, and Cluster Computing, 13. Climate action, carbon efficiency, electricity curtailment, client selection, Distributed, Parallel, and Cluster Computing (cs.DC), 000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung, Informatik

Prístupová URL adresa: http://arxiv.org/abs/2305.15092

Identifying Architectural Design Decisions for Achieving Green ML Serving

Autori: Francisco Durán Silverio Martinez-Fernandez Matias Martinez a ďalší

Zdroj: Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI. :18-23

Predmety: Computer Science - Machine Learning, Computer Science - Software Engineering, machine learning, AI-based systems, green AI, architectural design decisions

Prístupová URL adresa: http://arxiv.org/abs/2402.07585
https://hdl.handle.net/1871.1/5cad0139-6df8-4ef6-85c9-ed83012cdf3c
https://doi.org/10.1145/3644815.3644962
https://research.vu.nl/en/publications/5cad0139-6df8-4ef6-85c9-ed83012cdf3c