An Extreme Learning Machine Model for Venue Presence Detection

Wiqar Khan; Asif Raza; Heidi Kuusniemi; Mohammed Elmusrati; Leonardo Espinosa-Leal

doi:10.1007/978-3-031-21678-7_14

An Extreme Learning Machine Model for Venue Presence Detection

Wiqar Khan^*, Asif Raza, Heidi Kuusniemi, Mohammed Elmusrati, Leonardo Espinosa-Leal

^*Corresponding author for this work

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

Value-added services allocation or denial in a particular venue for a given user is of high significance. It will get more prominent as we move to 5G and 6G networks’ roll out, as we will get other means to have better aids. In this paper, Extreme Learning Machines (ELM) model performance is compared with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF) models for venue presence detection. The input data is collected from the number of UEs (User Equipment) simultaneously placed inside and outside a venue and kept for longer duration. UEs logs essential data such as received signal reference power for serving cells and neighbor candidate cells, along with other network information. Our findings show that ELM model performs above 95% accuracy for a count of zero, one, and two neighbors. The results get better as we consider the collected data from more neighbors’ cells in our ELM computation.

Original language	English
Title of host publication	Proceedings of ELM 2021
Subtitle of host publication	Theory, Algorithms and Applications
Editors	Kaj-Mikael Björk
Place of Publication	Cham
Publisher	Springer
Pages	144-151
ISBN (Electronic)	978-3-031-21678-7
ISBN (Print)	978-3-031-21677-0, 978-3-031-21680-0
DOIs	https://doi.org/10.1007/978-3-031-21678-7_14
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	11th International Conference on Extreme Learning Machines (ELM2021) - On-line, Helsinki, Finland Duration: 15 Dec 2021 → 16 Dec 2021 Conference number: 11 https://risklab.fi/events/

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	16
ISSN	2363-6084

Conference

Conference	11th International Conference on Extreme Learning Machines (ELM2021)
Abbreviated title	ELM2021
Country/Territory	Finland
City	Helsinki
Period	15/12/21 → 16/12/21
Internet address	https://risklab.fi/events/

Access to Document

10.1007/978-3-031-21678-7_14

Cite this

@inproceedings{35b9014929e44ad2b14c023b23915d6e,

title = "An Extreme Learning Machine Model for Venue Presence Detection",

abstract = "Value-added services allocation or denial in a particular venue for a given user is of high significance. It will get more prominent as we move to 5G and 6G networks{\textquoteright} roll out, as we will get other means to have better aids. In this paper, Extreme Learning Machines (ELM) model performance is compared with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF) models for venue presence detection. The input data is collected from the number of UEs (User Equipment) simultaneously placed inside and outside a venue and kept for longer duration. UEs logs essential data such as received signal reference power for serving cells and neighbor candidate cells, along with other network information. Our findings show that ELM model performs above 95% accuracy for a count of zero, one, and two neighbors. The results get better as we consider the collected data from more neighbors{\textquoteright} cells in our ELM computation.",

author = "Wiqar Khan and Asif Raza and Heidi Kuusniemi and Mohammed Elmusrati and Leonardo Espinosa-Leal",

year = "2023",

doi = "10.1007/978-3-031-21678-7_14",

language = "English",

isbn = "978-3-031-21677-0",

series = "Proceedings in Adaptation, Learning and Optimization",

publisher = "Springer",

pages = "144--151",

editor = "Kaj-Mikael Bj{\"o}rk",

booktitle = "Proceedings of ELM 2021",

address = "Germany",

note = "11th International Conference on Extreme Learning Machines (ELM2021), ELM2021 ; Conference date: 15-12-2021 Through 16-12-2021",

url = "https://risklab.fi/events/",

}

Khan, W, Raza, A, Kuusniemi, H, Elmusrati, M & Espinosa-Leal, L 2023, An Extreme Learning Machine Model for Venue Presence Detection. in K-M Björk (ed.), Proceedings of ELM 2021: Theory, Algorithms and Applications. Springer, Cham, Proceedings in Adaptation, Learning and Optimization, vol. 16, pp. 144-151, 11th International Conference on Extreme Learning Machines (ELM2021), Helsinki, Finland, 15/12/21. https://doi.org/10.1007/978-3-031-21678-7_14

An Extreme Learning Machine Model for Venue Presence Detection. / Khan, Wiqar; Raza, Asif; Kuusniemi, Heidi et al.
Proceedings of ELM 2021: Theory, Algorithms and Applications. ed. / Kaj-Mikael Björk. Cham: Springer, 2023. p. 144-151 (Proceedings in Adaptation, Learning and Optimization, Vol. 16).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - An Extreme Learning Machine Model for Venue Presence Detection

AU - Khan, Wiqar

AU - Raza, Asif

AU - Kuusniemi, Heidi

AU - Elmusrati, Mohammed

AU - Espinosa-Leal, Leonardo

N1 - Conference code: 11

PY - 2023

Y1 - 2023

N2 - Value-added services allocation or denial in a particular venue for a given user is of high significance. It will get more prominent as we move to 5G and 6G networks’ roll out, as we will get other means to have better aids. In this paper, Extreme Learning Machines (ELM) model performance is compared with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF) models for venue presence detection. The input data is collected from the number of UEs (User Equipment) simultaneously placed inside and outside a venue and kept for longer duration. UEs logs essential data such as received signal reference power for serving cells and neighbor candidate cells, along with other network information. Our findings show that ELM model performs above 95% accuracy for a count of zero, one, and two neighbors. The results get better as we consider the collected data from more neighbors’ cells in our ELM computation.

AB - Value-added services allocation or denial in a particular venue for a given user is of high significance. It will get more prominent as we move to 5G and 6G networks’ roll out, as we will get other means to have better aids. In this paper, Extreme Learning Machines (ELM) model performance is compared with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF) models for venue presence detection. The input data is collected from the number of UEs (User Equipment) simultaneously placed inside and outside a venue and kept for longer duration. UEs logs essential data such as received signal reference power for serving cells and neighbor candidate cells, along with other network information. Our findings show that ELM model performs above 95% accuracy for a count of zero, one, and two neighbors. The results get better as we consider the collected data from more neighbors’ cells in our ELM computation.

U2 - 10.1007/978-3-031-21678-7_14

DO - 10.1007/978-3-031-21678-7_14

M3 - Conference article in proceedings

SN - 978-3-031-21677-0

SN - 978-3-031-21680-0

T3 - Proceedings in Adaptation, Learning and Optimization

SP - 144

EP - 151

BT - Proceedings of ELM 2021

A2 - Björk, Kaj-Mikael

PB - Springer

CY - Cham

T2 - 11th International Conference on Extreme Learning Machines (ELM2021)

Y2 - 15 December 2021 through 16 December 2021