Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning

Syed Aziz Shah; Qammer Hussain Abbasi; Jawad Ahmad; Muhammad Ali Imran

doi:10.3390/electronics12193990

Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning

Syed Aziz Shah, Qammer Hussain Abbasi, Jawad Ahmad, Muhammad Ali Imran

Centre for Intelligent Healthcare

Research output: Contribution to journal › Editorial › peer-review

2 Citations (Scopus)

21 Downloads (Pure)

Abstract

State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]

Original language	English
Article number	3990
Number of pages	2
Journal	Electronics
Volume	12
Issue number	19
Early online date	22 Sept 2023
DOIs	https://doi.org/10.3390/electronics12193990
Publication status	Published - Oct 2023

Bibliographical note

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords

Electrical and Electronic Engineering
Computer Networks and Communications
Hardware and Architecture
Signal Processing
Control and Systems Engineering

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10.3390/electronics12193990Licence: CC BY

PublishedFinal published version, 569 KBLicence: CC BY

Cite this

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title = "Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning",

abstract = "State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]",

keywords = "Electrical and Electronic Engineering, Computer Networks and Communications, Hardware and Architecture, Signal Processing, Control and Systems Engineering",

author = "Shah, \{Syed Aziz\} and Abbasi, \{Qammer Hussain\} and Jawad Ahmad and Imran, \{Muhammad Ali\}",

note = "{\textcopyright} 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).",

year = "2023",

month = oct,

doi = "10.3390/electronics12193990",

language = "English",

volume = "12",

journal = "Electronics",

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publisher = "Multidisciplinary Digital Publishing Institute",

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T1 - Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning

AU - Shah, Syed Aziz

AU - Abbasi, Qammer Hussain

AU - Ahmad, Jawad

AU - Imran, Muhammad Ali

N1 - © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

PY - 2023/10

Y1 - 2023/10

N2 - State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]

AB - State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]

KW - Electrical and Electronic Engineering

KW - Computer Networks and Communications

KW - Hardware and Architecture

KW - Signal Processing

KW - Control and Systems Engineering

UR - https://www.scopus.com/pages/publications/85173846366

U2 - 10.3390/electronics12193990

DO - 10.3390/electronics12193990

M3 - Editorial

SN - 2079-9292

VL - 12

JO - Electronics

JF - Electronics

IS - 19

M1 - 3990

ER -

Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning

Abstract

Bibliographical note

Keywords

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