scholarly journals Cry Recognition for Infant Incubator Monitoring System Based on Internet of Things using Machine Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 444-452
Author(s):  
Erwin Sutanto ◽  
◽  
Fahmi Fahmi ◽  
Wervyan Shalannanda ◽  
Arga Aridarma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Smart Device ◽  
The Real ◽  
Current Technology ◽  
2D Pattern ◽  
Infant Incubator ◽  
Technology Trend

With the current technology trend of IoT and Smart Device, there is a possibility for the improvement of our infant incubator in responding to the real baby’s condition. This work is trying to see that possibility. First is by analyzing of open baby voice database. From there, a procedure to find out baby cry classification will be explained. The approach was starting with an analysis of sound’s power from that WAV files before going further into the 2D pattern, which will have features for the machine learning. From this work, around 85% accuracy could be achieved. Then together with sensors, it would be useful for infant incubator’s innovation by utilizing this proposed configuration.

scholarly journals Smart Device Monitoring System Based on Multi-type Inertial Sensor Machine Learning

2021 ◽  
Vol 33 (2) ◽  
pp. 693
Author(s):  
Yingqi Zeng ◽  
Chen Wang ◽  
Chih-Cheng Chen ◽  
Wang-Ping Xiong ◽  
Zhen Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Inertial Sensor ◽  
Smart Device ◽  
Device Monitoring

scholarly journals On-farm welfare monitoring system for goats based on Internet of Things and machine learning

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772094403
Author(s):  
Yuan Rao ◽  
Min Jiang ◽  
Wen Wang ◽  
Wu Zhang ◽  
Ruchuan Wang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Animal Husbandry ◽  
Environmental Data ◽  
Software Systems ◽  
Data Detection ◽  
Livestock Farming ◽  
On Farm

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

Design of industrial internet of things system based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-11
Author(s):  
Xu Kun ◽  
Zhiliang Wang ◽  
Ziang Zhou ◽  
Wang Qi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Industrial Equipment ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Test Platform ◽  
Artificial Intelligence Technology

For industrial production, the traditional manual on-site monitoring method is far from meeting production needs, so it is imperative to establish a remote monitoring system for equipment. Based on machine learning algorithms, this paper combines artificial intelligence technology and Internet of Things technology to build an efficient, fast, and accurate industrial equipment monitoring system. Moreover, in view of the characteristics of the diverse types of equipment, scattered layout, and many parameters in the manufacturing equipment as well as the complexity of the high temperature, high pressure, and chemical environment in which the equipment is located, this study designs and implements a remote monitoring and data analysis system for industrial equipment based on the Internet of Things. In addition, based on the application scenarios of the actual aeronautical weather floating platform test platform, this study combines the platform prototype system to design and implement a set of strong real-time communication test platform based on the Windows operating system. The test results show that the industrial Internet of Things system based on machine learning and artificial intelligence technology constructed in this paper has certain practicality.

scholarly journals Smart Crop Monitor using Internet of Things, Cloud, Machine Learning and Android App

2020 ◽  
Vol 9 (3) ◽  
pp. 3213-3217
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Environmental Conditions ◽  
Crop Management ◽  
Android Application ◽  
Crop Monitoring ◽  
Android App

This paper describes a Smart Crop Monitoring system implemented using Internet of Things (IoT) for sensing environmental conditions and forwarding the data, Machine Learning to generate decisions for crop management based on the data, Cloud for storage and an Android application interface for operation of the system.

scholarly journals The prototype of infant incubator monitoring system based on the internet of things using NodeMCU ESP8266

2019 ◽  
Vol 1171 ◽  
pp. 012015
Author(s):  
R Firmansyah ◽  
A Widodo ◽  
A D Romadhon ◽  
M S Hudha ◽  
P P S Saputra ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
The Internet ◽  
Infant Incubator ◽  
The Internet Of Things

scholarly journals An Experimental Evaluation of Hybrid Learning Methodology based Internet of Things Assisted Health Care Monitoring System

2021 ◽  
Author(s):  
Kamal Upreti ◽  
MAHAVEERAKANNAN R ◽  
Raut Ranjana Dinkar ◽  
Sudhanshu Maurya ◽  
Venkatramanan Reddy ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Learning Strategy ◽  
Smart Device ◽  
Modern World ◽  
Support Vector ◽  
Patient Health ◽  
Health Care Monitoring

Abstract In this modern world, every individual uses intelligent devices to lead a day-to-day activity intelligently. Using the latest technologies such as deep learning, the Internet of Things (IoT) forth provides standard prediction and communication abilities to the existing applications to properly provide rich support to the clients. Many commercial and non-commercial organizations almost adapt these technologies to modify their physical records digitally. This paper designed a novel health care monitoring scheme by adapting these technologies to provide an intelligent monitoring system to analyze patients over random instances with periodic intervals. This paper introduced a new learning-based scheme called Deviated Learning-based Health Analysis (DLHA), in which it combines the conventional algorithms such as Convolutional Neural Network (CNN) and the Support Vector Classification (SVM) logic in a transparent manner. The logical evaluations of the proposed approach called DLHA assessed by extracting the layers from the CNN, appending the classification logic of SVM into the CNN layers, and defining a new algorithm to predict patient health intelligently. The association of sensor-based smart device called Smart Health Indicator (SHI) provides significant support to the proposed approach with the association of intelligent sensors such as Heartbeat Analyzer, Body Temperature Estimation Sensor, Breath Sensor, Global Positioning System (GPS), and the useful Internet of Things enabled controller called ESP8266. Using this SHI kit, the patient details are monitoring instantly and reporting it to the remote server periodically to analyze the health summary without any interventions. The proposed deep learning strategy called DLHA acquires the data from the intelligent health care kit SHI and processes it using classification principles. The records collected from the kit were manipulated according to the process of the trained model generated from the previous testing samples of the patients. The dataset used in this system is generated dynamically from the real-time patient health record and processes the testing report of the patient accordingly. The processed record is appended into the dataset for further reference. The resulting section provides proper proof of the efficiency of the proposed approach in a transparent manner with graphical representations. For all this system is more significant to identify and monitor the health details of the patient in clear manner with proper specifications.

scholarly journals Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qiong Li ◽  
Liqiang Zhang ◽  
Rui Zhou ◽  
Yaowen Xia ◽  
Wenfeng Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Internet Of Things ◽  
Monitoring System ◽  
Temperature Monitoring ◽  
Dynamics Simulation ◽  
Energy Output ◽  
Gradient Boosting ◽  
Energy Internet ◽  
Extreme Gradient Boosting

With the development of the Energy Internet of Things (EIoT), it is of great practical significance to study the security strategy and intelligent control system for solar thermal utilization system to optimize the operation efficiency and carry out intelligent dynamic adjustment. For buildings integrated with solar water heating systems, computational fluid dynamics simulation was used in analyzing the process of solar energy output. A method based on machine learning is proposed to predict energy conversion. Besides, the simulation and analysis are carried out in combination with the possible safety problems such as the vibration of the control system. This paper proposed a novel platform of EIoT for machine learning-based cybersecurity study and implemented the platform for the temperature monitoring system. After the evaluation of the machine learning-based cybersecurity study, the EIoT system demonstrated a high performance with the Extreme Gradient Boosting (XGBoost) training algorithm.

scholarly journals Zigbee and Long-Range Architecture Based Monitoring System for Oil Pipeline Monitoring with the Internet of Things

2021 ◽  
Vol 13 (18) ◽  
pp. 10226
Author(s):  
Rajesh Singh ◽  
Mohammed Baz ◽  
Ch. Lakshmi Narayana ◽  
Mamoon Rashid ◽  
Anita Gehlot ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Critical Parameters ◽  
The Internet ◽  
Real Time Monitoring ◽  
The Real ◽  
Oil Pipeline ◽  
Pipeline Monitoring ◽  
The Internet Of Things

Oil pipeline monitoring is having a significant role in minimizing the impact on the environment and humans during pipeline accidents. The real-time monitoring of oil pipelines empowers the authorities to have continuous supervision of the oil pipeline. The Internet of Things (IoT) provides an opportunity for realizing the real-time monitoring system by deploying the IoT-enabled end devices on the oil pipeline. In this study, we propose a hybrid architecture based on 2.4 GHz-based Zigbee and LoRa communication for oil pipeline monitoring. Moreover, customized end devices and LoRa based gateway are designed and implemented for sensing the critical parameters of an oil pipeline. Here, we have performed the simulation of ZigBee communication on the OPNET simulator for evaluating the parameters such as packet delivery ratio (PDR), retransmission attempts, throughput, medium access (MAC) queue size, and queue delay. Furthermore, the distinct evaluation metrics of LoRa such as bit rate, link budget, and receiver sensitivity are also included. Finally, a real-time experiment is implemented with customized end devices and a gateway for evaluating the proposed architecture. In the real-time experiment, the devices and gateway are logging the pressure sensory data into the Cayenne cloud.

Sign in / Sign up

Export Citation Format

Share Document