Reducing Significances of Mesh Sensors Technologies through Dimensionality Reduction Algorithm

Engineering International

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Title Reducing Significances of Mesh Sensors Technologies through Dimensionality Reduction Algorithm
Creator Amin, Ruhul
Subject Dimensionality Reduction Algorithm
Cuckoo search
Classification accuracy
Description In today's world, the breadth of real-time applications and networks is not limited to business and social activities. They are expanding as a field to provide improved and competitive settings for a variety of activities such as home, health, and commercial procedures. Data analytic method is used to maintain network accessibility as well as the robustness of expert services. It is necessary to clean up the data in order to reduce the computational complexity of extracting and pre-processing models. Because present approaches are sophisticated, they necessitate large computations. To this effect, the objective is to deploy a machine learning algorithm – “cuckoo search algorithm” for dimensionality reduction problems in data extraction for IoTs application. The cuckoo search-based feature extraction algorithm is a mutant algorithm that organizes itself depending on the unpredictable amount of input and generates a new and improved feature space. After the cuckoo search-based feature extraction is implemented, a few test benchmarks are provided to assess the performance of mutant cuckoo search algorithms. As a result of the low-dimensional data, classification accuracy is improved while complexity and expense are lowered.
Publisher Asian Business Consortium
Date 2020-12-21
Type info:eu-repo/semantics/article
Format application/pdf
Source Engineering International; Vol. 8 No. 2 (2020): July - December Issue; 111-126
Language eng
Rights Copyright (c) 2020 Ruhul Amin

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