Research on Node Localization Algorithm in WSN Basing Machine Learning

2013 ◽  
Vol 756-759 ◽  
pp. 3568-3573
Author(s):  
Qing Zhang Chen ◽  
Yu Zheng Chen ◽  
Cong Ling Fan ◽  
Fan Yang ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Node Ratio ◽  
Location Algorithm ◽  
Beacon Node ◽  
Coverage Holes

Machine learning uses experience to improve its performance. Using Machine Learing, to locate the nodes in wireless sensor network. The basic idea is that: the network area is divided into several equal portions of small grids, each gird represents a certain class of Machine Learning algorithm. After Machine Learning algorithm has learnt the parameters using the known beacon nodes, it can classify the unknown nodes location classes, and further determine their coordinates. For the SVM OneAgainstOne Location Algorithm, the results of simulation show that it has a high localization accuracy and a better tolerance for the ranging error, while it doesnt require a high beacon node ratio. For the SVM Decision Tree Location Algorithm, the results show that this algorithm is not affected seriously by coverage holes, it is suitable for the network environment of nonuniformity distribution or existing coverage holes.

The Weighted Localization Algorithm Based on RSSI Calibration

2012 ◽  
Vol 236-237 ◽  
pp. 1010-1014
Author(s):  
Ning Hui He ◽  
Hong Sheng Li ◽  
Guang Rong Bian
Keyword(s):  
Wireless Sensor Network ◽  
Signal Strength ◽  
The Other ◽  
Wireless Sensor ◽  
Network Node ◽  
Localization Algorithm ◽  
Node Localization ◽  
Positioning Accuracy ◽  
Location Algorithm ◽  
Beacon Node

This paper introduces the first wireless sensor network node localization in two ways, one is based on the RSSI ranging approach, and the other is based on the weighted centric location algorithm, as a result of environmental factors, the same RSSI value the distance ，but the corresponding values are not always the same, these two methods do not consider the environmental impact to the RSSI value. Therefore, we consider combining the distance and signal strength information as a reference to correct each beacon node weights， in order to improve positioning accuracy.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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