Search Results - "QoS prediction"

A two-phase learning approach integrated with multi-source features for cloud service QoS prediction

Authors: Chen, Fuzan Yang, Jing Feng, Haiyang et al.

Source: Frontiers of Engineering Management. 12:117-127

Subject Terms: Artificial Intelligence and Robotics, Theory and Algorithms, Similarity measure, Data Science, Matrix factorization, QoS prediction, Cloud service, Deep neural network, Recommendation, Technology and Innovation, Quality

File Description: application/pdf

Collaborative Filtering Techniques for Predicting Web Service QoS Values in Static and Dynamic Environments: A Systematic and Thorough Analysis

Authors: Ghizlane Khababa Sadik Bessou Fateh Seghir et al.

Source: IEEE Access, Vol 13, Pp 45350-45376 (2025)

Subject Terms: dynamic environment, quality of service (QoS), Collaborative filtering, QoS prediction, fuzzy environment, Electrical engineering. Electronics. Nuclear engineering, static environment, TK1-9971

Access URL: https://doaj.org/article/e7fbdd101aec49a9a806542389fa3934

Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications

Authors: Song, Tailai Garza, Paolo Meo, Michela et al.

Source: 2024 International Symposium on Multimedia (ISM). :63-70

Subject Terms: Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Computer Science - Machine Learning, Real-time communications, RTP, QoS prediction, machine learning, deep learning, Computer Science - Multimedia, Machine Learning (cs.LG), Multimedia (cs.MM)

File Description: application/pdf

Access URL: http://arxiv.org/abs/2410.15846
https://hdl.handle.net/11583/2998662
https://doi.org/10.1109/ism63611.2024.00015
https://ieeexplore.ieee.org/document/10935973

QCI-WSC: Estimation and prediction of QoS confidence interval for web service composition based on Bootstrap

Authors: Li Qinying Lu Wei Li Fangli et al.

Source: Open Computer Science, Vol 15, Iss 1, Pp pp. 3716-3732 (2025)

Subject Terms: web service composition, confidence interval, qos estimation, qos prediction, bootstrap, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2299-1093

Access URL: https://doaj.org/article/d3dd51deefc94dfc9d234e912a3f715e

Trust-aware privacy-preserving QoS prediction with graph neural collaborative filtering for internet of things services

Authors: Weiwei Wang Wenping Ma Kun Yan

Source: Complex & Intelligent Systems, Vol 11, Iss 4, Pp 1-18 (2025)

Subject Terms: QoS prediction, Gaussian distribution, GNN, Privacy protection, IoT services, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Access URL: https://doaj.org/article/df4a9fa935804fe691df2fcb54fa8523

An SVR-based and Location-aware Method for Mobile QoS Prediction

Authors: Lifang Ren Jing Li Wenjian Wang

Source: Journal of Universal Computer Science, Vol 30, Iss 3, Pp 383-401 (2024)
JUCS-Journal of Universal Computer Science 30(3): 383-401

Subject Terms: Support vector regression, Electronic computers. Computer science, Locatio-naware, QoS prediction, QA75.5-76.95, 0102 computer and information sciences, Mobile service, 01 natural sciences, Su

File Description: text/html

Access URL: https://doaj.org/article/8d23256daee54fe5a4579ec58e52918f
https://lib.jucs.org/article/106314/download/pdf/
https://doi.org/10.3897/jucs.106314
https://lib.jucs.org/article/106314/

pFedLN: Personalized Federated Learning Framework With Layer-Wised and Neighbor-Based Aggregation for QoS Prediction

Authors: Weiwei She Jianlong Xu Mengqing Jin et al.

Source: IEEE Access, Vol 12, Pp 10135-10145 (2024)

Subject Terms: personalized federated learning, 0103 physical sciences, QoS prediction, 0202 electrical engineering, electronic engineering, information engineering, Internet of service, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 01 natural sciences, TK1-9971

Access URL: https://doaj.org/article/1c70dea2b7664041b31c775f418d572b

Towards AI-Native Vehicular Communications

Authors: Rizzo Gianluca Liotou Eirini Maret Yann et al.

Source: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). :1-7

Subject Terms: distributed learning, ITS, ML/AI, QoS-prediction, V2X communications

File Description: application/pdf

QoS prediction using EMD-BiLSTM for II-IoT-secure communication systems

Authors: Zeng Yun Li Xiang

Source: Journal of Intelligent Systems, Vol 33, Iss 1, Pp 648-57 (2024)

Subject Terms: industrial intelligent internet of things, secure communication, emd, bilstm, qos prediction, Science, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2191-026X

Access URL: https://doaj.org/article/70221a02af6d4a2cbf0c773dbb010f99

Toward Highly-Efficient and Accurate Services QoS Prediction via Machine Unlearning

Authors: Yuxiang Zeng Jianlong Xu Yuhui Li et al.

Source: IEEE Access, Vol 11, Pp 76242-76254 (2023)

Subject Terms: context-aware, Internet of Things, machine unlearning, QoS prediction, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/0b1f1cf9efd340a7a10d782afe909614

Toward QoS Prediction Based on Temporal Transformers for IoT Applications

Authors: Aroosa Hameed John Violos Aris Leivadeas et al.

Contributors: Aroosa Hameed John Violos Aris Leivadeas et al.

Source: IEEE Transactions on Network and Service Management. 19:4010-4027

Subject Terms: [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Internet of Things, QoS prediction, transformer, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing, Deep learning, Edge computing, 02 engineering and technology, time series

File Description: application/pdf

Access URL: https://hal.science/hal-03828639v1

End-to-end Delay Prediction Based on Traffic Matrix Sampling

Authors: Krasniqi, Filip Elias, Jocelyne Leguay, Jeremie et al.

Source: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :774-779

Subject Terms: Machine Learning, End-to-end delay, End-to-end delay, QoS prediction, Machine Learning, Traffic Measurement, QoS prediction, 0202 electrical engineering, electronic engineering, information engineering, Traffic Measurement, 02 engineering and technology

File Description: application/pdf

Access URL: https://hdl.handle.net/11311/1153629
https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162765
https://hdl.handle.net/11585/795014
https://ieeexplore.ieee.org/document/9162765
https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162765

QoS Prediction of Web Services Based on a Two-Level Heterogeneous Graph Attention Network

Authors: Shengkai Lv Fangzhou Yi Peng He et al.

Source: IEEE Access, Vol 10, Pp 1871-1880 (2022)

Subject Terms: QoS prediction, 0202 electrical engineering, electronic engineering, information engineering, service computing, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Heterogeneous graph neural network, attention mechanism, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09662349.pdf
https://doaj.org/article/8972c2da60f349e08a69bd3258031818

A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning

Authors: Hussain, Walayat Merigó, J. M. Raza, M. R. et al.

Source: Information Sciences. 584:280-300

Subject Terms: Clustered Induced Ordered Weighted Averaging Adaptive Neuro-Fuzzy Inference System model, 4602 Artificial intelligence, fuzzy c-means, service-oriented network, VU School of Business, Quality of Service, CI-ANFIS model, grid partitioning, 4606 Distributed computing and systems software, 02 engineering and technology, SLA management, cloud services, OWA operator, cloud QoS prediction, fuzzy time series prediction model, subtractive clustering, 0202 electrical engineering, electronic engineering, information engineering, fuzzy clustering, time series forecasting, ANFIS, complexity

Access URL: https://www.sciencedirect.com/science/article/pii/S0020025521010768
https://acuresearchbank.acu.edu.au/item/8z519/a-new-qos-prediction-model-using-hybrid-iowa-anfis-with-fuzzy-c-means-subtractive-clustering-and-grid-partitioning

Context-Aware and Adaptive QoS Prediction for Mobile Edge Computing Services

Authors: Zhizhong Liu Quan Z. Sheng Xiaofei Xu et al.

Source: IEEE Transactions on Services Computing. 15:400-413

Subject Terms: context-awareness, case-based reasoning, 0202 electrical engineering, electronic engineering, information engineering, Mobile edge computing, adaptive QoS prediction, support vector machine, artificial bee colony algorithm, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8852687
https://www.computer.org/csdl/journal/sc/5555/01/08852687/1j4FwhOv4Tm
https://researchers.mq.edu.au/en/publications/context-aware-and-adaptive-qos-prediction-for-mobile-edge-computi

QoS prediction in intelligent edge computing based on feature learning

Authors: Hongxia Zhang Dengyue Wang Wei Zhang et al.

Source: Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-16 (2023)

Subject Terms: Intelligent edge computing, Service recommendations, QoS prediction, multi-head self-attention, feature learning, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2192-113X

Access URL: https://doaj.org/article/c9c307d85f6c4812af3c4c9859d0ad4b

A Novel Deep-Learning-Based QoS Prediction Model for Service Recommendation Utilizing Multi-Stage Multi-Scale Feature Fusion with Individual Evaluations

Authors: Zhang, Peiyun Huang, Wenjun Chen, Yutong et al.

Source: Faculty Publications

Subject Terms: deep neural network, feature fusion, multi-scale, multi-stage, QoS prediction, Service recommendation

Relation: https://digitalcommons.njit.edu/fac_pubs/547

Availability: https://digitalcommons.njit.edu/fac_pubs/547
https://doi.org/10.1109/TASE.2023.3244184

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Authors: Shaoyu Zhu Jiaman Ding Jingyou Yang

Source: Applied Sciences, Vol 14, Iss 4, p 1450 (2024)

Subject Terms: service recommendation, sparse data, feature interaction, deep forest, QoS prediction, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/14/4/1450; https://doaj.org/toc/2076-3417; https://doaj.org/article/9a458ab5b71f4ce1bd61b5dc39c9fa9a

Availability: https://doi.org/10.3390/app14041450
https://doaj.org/article/9a458ab5b71f4ce1bd61b5dc39c9fa9a

NFMF: neural fusion matrix factorisation for QoS prediction in service selection

Authors: Jianlong Xu Lijun Xiao Yuhui Li et al.

Source: Connection Science, Vol 33, Iss 3, Pp 753-768 (2021)

Subject Terms: qos prediction, contextual information, neural network, matrix factorisation, 0202 electrical engineering, electronic engineering, information engineering, multi-task learning, Information technology, 02 engineering and technology, T58.5-58.64

Access URL: https://doaj.org/article/34bca9fb69e94db89042ae1074e2c191
https://www.tandfonline.com/doi/full/10.1080/09540091.2021.1889975
http://ui.adsabs.harvard.edu/abs/2021ConSc..33..753X/abstract

Deep Neural Network-based Approach for IoT Service QoS Predicti

Authors: Awanyo, Chrisson Guermouche, Nawal AWANYO, Kossi Jean Baptiste Christson

Contributors: Awanyo, Chrisson Guermouche, Nawal AWANYO, Kossi Jean Baptiste Christson

Subject Terms: IoT, Deep Learning, QoS prediction, Smart City, [INFO] Computer Science [cs], LSTM, ResNet

File Description: application/pdf

Access URL: https://laas.hal.science/hal-04285558v1/document
https://laas.hal.science/hal-04285558v1