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Internet of Things (IoT) in Smart Systems and Applications
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General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
,
etc
.
Acceptance Rate:
12%
APC:
1000 USD
Average Days to Accept:
87 days
Journal Metrics:
Impact Factor 2023: 0.9
4.2
2023
CiteScore
57th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2025-02-10
All the 141 papers published in JAIT in 2024 have been indexed by Scopus.
2025-01-23
JAIT Vol. 16, No. 1 has been published online!
2024-06-07
JAIT received the CiteScore 2023 with 4.2, ranked #169/394 in Category Computer Science: Information Systems, #174/395 in Category Computer Science: Computer Networks and Communications, #226/350 in Category Computer Science: Computer Science Applications
Home
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Published Issues
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2022
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Volume 13, No. 2, April 2022
>
JAIT 2022 Vol.13(2): 106-116
doi: 10.12720/jait.13.2.106-116
Real Time Audio-Based Distress Signal Detection as Vital Signs of Myocardial Infarction Using Convolutional Neural Networks
H. M. Mohan and S. Anitha
Department of ECE, ACS College of Engineering, Bangalore, India
Abstract
—In recent years, with rapid advancement in Artificial Intelligence technology, several intelligent systems have been developed for human emergency prediction under ambient intelligence. Automatic pain recognition through state-of-the-art deep learning algorithms has attracted much attention recently in smart healthcare informatics. This research presents a Convolutional Neural Network (CNN) approach for detecting audio-based emergency identification during Myocardial Infarction. For evaluation, simulated emergency distress audio signals are recorded during possible myocardial infarction as a private dataset. This work demonstrates an approach to train the deep learning CNN model for multiclass audio samples and deploy it on an edge embedded Artificial Intelligence device Jetson Nano for real-time recognition.
Index Terms
—ambient intelligence, myocardial infarction,
convolutional neural network, emergency distress signal
Cite: H. M. Mohan and S. Anitha, "Real Time Audio-Based Distress Signal Detection as Vital Signs of Myocardial Infarction Using Convolutional Neural Networks," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 106-116, April 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
1-JAIT-2687-Final-India
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