scholarly journals IOT Based Forest Fire Prediction and Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1835-1838
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Humidity Sensor ◽  
New Method ◽  
The Internet ◽  
Industrial Areas ◽  
Fire Alarm ◽  
Wooded Area ◽  
Cloud Application ◽  
Late Detection

In recent days, satellite-based surveillance gadget is used to notice wooded area hearth however this works when fireplace is unfold in the massive area. So these methods are no longer efficient. According to a survey, about 80% losses are accumulated in the woodland due to the late detection of fire. To overcome this two, we proposed a new method to predict and the fire at early stages . In our proposed method the hardware kit with temperature and humidity sensor is connected to the PC and it is deployed in many places in the forest area. The PC is connected with the Internet . The details collected using sensor is upload with the fixed interval time. Then this data is uploaded to the cloud application. If the forest temperature is increased abnormally this will detect send notification to the forest authorities then the fire alarm will rung .It can also predict the fire that will be occur in future by using machine learning . This is done by using KNN algorithm. This can be used in all kind of forest and considering the effectiveness of the sensors it be also used in industrial areas.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

Forest Fire Prediction Using Machine Learning Techniques

2021 ◽  
Author(s):  
T Preeti ◽  
Suvarna Kanakaraddi ◽  
Aishwarya Beelagi ◽  
Sumalata Malagi ◽  
Aishwarya Sudi
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

Ontology Construction Research for the Mobile Phones' Reviews on the Internet

2014 ◽  
Vol 602-605 ◽  
pp. 3363-3366
Author(s):  
Yi Ming Sun ◽  
Chun Lei Han
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Semantic Information ◽  
Domain Ontology ◽  
University Research ◽  
New Method ◽  
The Internet ◽  
Stanford University ◽  
Information Mining ◽  
Ontology Construction

In order to automatically identify the mobile phones' reviews that the users comment on the mobile phone on the internet and obtain valuable information from the reviews, this paper presents the process of constructing ontology for the mobile phones' reviews and preliminarily establish a domain ontology of the mobile phones' reviews. The ontology construction adopts the Protégé tool and the Seven Steps method of Stanford University research. The ontology can provide convenience for the semantic information mining on Web mobile phones' reviews, and it can provide a new method to effectively mine the use feelings of the phone from a large number of mobile phone users' reviews.

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