Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

2021 ◽  
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Kernel Function ◽  
Dynamic Time Warping ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Time Warping ◽  
Dynamic Time

Real-Time Fire Detection System Based on Dynamic Time Warping of Multichannel Sensor Networks

2021 ◽  
pp. 103364
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Dynamic Time Warping ◽  
Detection System ◽  
Fire Detection ◽  
Time Warping ◽  
Fire Detection System ◽  
Dynamic Time

scholarly journals Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

2020 ◽  
Vol 12 (6) ◽  
pp. 2403 ◽  
Author(s):  
Ahmed Ismail ◽  
Samir Abdlerazek ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Support Vector Machine ◽  
Speech Recognition ◽  
Elderly People ◽  
Dynamic Time Warping ◽  
Smart Devices ◽  
Support Vector ◽  
Time Warping ◽  
Home Appliances ◽  
Iot Devices ◽  
Dynamic Time

This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients.

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