Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
Design of Deep Learning Algorithm for IoT Application by Image based Recognition
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Analysis of Serverless Computing Techniques in Cloud Software Framework
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Health Record Management System – A Web-based Application
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A Novel Signal Processing Based Driver Drowsiness Detection System
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IoT Based Monitoring and Control System using Sensors
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Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
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Review of Internet of Wearable Things and Healthcare based Computational Devices
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Stock Index Prediction with Financial News Sentiments and Technical Indicators
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Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
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Suspicious Human Activity Detection System
Volume-2 | Issue-4
ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3
EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2
Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4
Volume - 3 | Issue - 2 | june 2021
Published
21 June, 2021
With the advent of technology, several domains have b on Internet of Things (IoT). The hyper spectral sensors present in earth observation system sends hyper spectral images (HSIs) to the cloud for further processing. Artificial intelligence (AI) models are used to analyse data in edge servers, resulting in a faster response time and reduced cost. Hyperspectral images and other high-dimensional image data may be analysed by using a core AI model called subspace clustering. The existing subspace clustering algorithms are easily affected by noise since they are constructed based on a single model. The representation coefficient matrix connectivity and sparsity is hardly balanced. In this paper, connectivity and sparsity factors are considered while proposing the subspace clustering algorithm with post-process strategy. A non-dominated sorting algorithm is used for that selection of close neighbours that are defined as neighbours with high coefficient and common neighbours. Further, pruning of useless, incorrect or reserved connections based on the coefficients between the close and sample neighbours are performed. Lastly, inter and intra subspace connections are reserved by the post-process strategy. In the field of IoT and image recognition, the conventional techniques are compared with the proposed post-processing strategies to verify its effectiveness and universality. The clustering accuracy may be improved in the IoT environment while processing the noise data using the proposed strategy as observed in the experimental results.
KeywordsHyperspectral Images Internet of Things Subspace Clustering Artificial Intelligence Sorting algorithm
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