K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device

2022 ◽  
pp. 161-179
Author(s):  
Micheal Olaolu Arowolo ◽  
Roseline Oluwaseun Ogundokun ◽  
Sanjay Misra ◽  
Jonathan Oluranti ◽  
Akeem Femi Kadri
Keyword(s):  
Edge Computing ◽  
Nearest Neighbour ◽  
Computing Device

Automated System Design for Classification of Chronic Lung Viruses using Non-Linear Dynamic System Features and K-Nearest Neighbour

2021 ◽  
Author(s):  
Misha Urooj Khan ◽  
Ayesha Farman ◽  
Asad Ur Rehman ◽  
Nida Israr ◽  
Muhammad Zulqarnain Haider Ali ◽  
...  
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Linear Dynamic System ◽  
Automated System ◽  
Nearest Neighbour ◽  
Linear Dynamic ◽  
Non Linear

scholarly journals An Improved Dual Clustering Method for Classification of Microarray Image Segementation

2019 ◽  
Vol 8 (4) ◽  
pp. 2514-2519
Keyword(s):  
Support Vector Machine ◽  
Ct Scanning ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Resonance Imaging ◽  
Computerised Tomography ◽  
Medical Field ◽  
Microarray Image ◽  
Image Segementation

Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.

scholarly journals A Multifault Diagnosis Method of Gear Box Running on Edge Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiao-ping Zhao ◽  
Yong-hong Zhang ◽  
Fan Shao
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Data Center ◽  
Edge Computing ◽  
Industrial Equipment ◽  
Point Model ◽  
Computing Device ◽  
External Data ◽  
Diagnosis Method ◽  
Analyze Data

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

scholarly journals Application of Wireless Sensor Network Based on Hierarchical Edge Computing Structure in Rapid Response System

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1176
Author(s):  
Yuechun Wang ◽  
Ka Lok Man ◽  
Kevin Lee ◽  
Danny Hughes ◽  
Sheng-Uei Guan ◽  
...  
Keyword(s):  
Rapid Response ◽  
Edge Computing ◽  
Response Accuracy ◽  
Rapid Response System ◽  
Distributed Management ◽  
Response System ◽  
Node Architecture ◽  
Application Fields ◽  
Computing Structure

This paper presents a rapid response system architecture for the distributed management of warehouses in logistics by applying the concept of tiered edge computing. A tiered edge node architecture is proposed for the system to process computing tasks of different complexity, and a corresponding rapid response algorithm is introduced. The paper emphasizes the classification of abstracted outlier sensing data which could better match different sensing types and transplant to various application fields. A software-defined simulation is used to evaluate the system performance on response time and response accuracy, from which it can be concluded that common predefined emergency cases can be detected and responded to, rapidly.

Setting Attribute Weights for Nearest Neighbor Learning Algorithms Using C4.5

1997 ◽  
Vol 11 (03) ◽  
pp. 405-415 ◽  
Author(s):  
Charles X. Ling ◽  
John J. Parry ◽  
Handong Wang
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Simple Approach ◽  
Nearest Neighbour ◽  
Attribute Weights ◽  
New Methods

Nearest Neighbour (NN) learning algorithms utilize a distance function to determine the classification of testing examples. The attribute weights in the distance function should be set appropriately. We study situations where a simple approach of setting attribute weights using decision trees does not work well, and design three improvements. We test these new methods thoroughly using artificially generated datasets and datasets from the machine learning repository.

scholarly journals Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
Author(s):  
Zeba Idrees ◽  
Zhuo Zou ◽  
Lirong Zheng
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Real Time ◽  
Low Cost ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Quality Data ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Computing Device

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

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