通过降维算法降低网格传感器技术的重要性

IF 0.6 4区 工程技术 Q4 Engineering Nuclear Engineering International Pub Date : 2020-12-21 DOI:10.18034/EI.V8I2.556
Ruhul Amin
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引用次数: 0

摘要

在当今世界,实时应用和网络的广度不仅限于商业和社会活动。它们正在扩大作为一个领域,为各种活动,如家庭、保健和商业程序,提供更好的和有竞争力的环境。采用数据分析的方法来保证网络的可访问性和专家服务的鲁棒性。为了降低模型提取和预处理的计算复杂度,有必要对数据进行清理。由于目前的方法是复杂的,它们需要大量的计算。为此,目标是部署一种机器学习算法-“布谷鸟搜索算法”,用于物联网应用中数据提取中的降维问题。基于布谷鸟搜索的特征提取算法是一种突变算法,它根据不可预测的输入量进行自我组织,生成新的改进的特征空间。在实现基于杜鹃搜索的特征提取后,提供了一些测试基准来评估突变杜鹃搜索算法的性能。由于采用了低维数据,在提高分类精度的同时降低了分类复杂度和分类费用。
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Reducing Significances of Mesh Sensors Technologies through Dimensionality Reduction Algorithm
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.
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来源期刊
Nuclear Engineering International
Nuclear Engineering International 工程技术-核科学技术
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审稿时长
6-12 weeks
期刊介绍: Information not localized
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