维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

2篇 您的检索式：作者名="T.Kalavathidevi"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Miniaturized antenna design for communication establishment of peer-to-peer communication in the oil pipelines显示文摘In the oil pipeline world,wireless automation plays an important role through integrated electronic technology in antenna design for robust performance.The technology gets evaluated by its power consumption and compactness to afford a better solution to real-world problems with better sophistication to adopt a modernized wireless world.Among that,the IoT(Internet of Things)technology plays a predominant role by combining with Artificial Intelligence to optimize and revolute the characterization of data communication.When the data communication is initiated,the main contributor to the transmission is the miniaturized antenna holding different types in its structure and processing.It acquires the required radio spectrum channel with the advanced multi coding modulating technique by utilizing token ring mesh topology for the data transfer with acquired data channels.This chapter describes the design and fabrication of a miniaturized antenna using MEMS technology(Micro-Electro-Mechanical System)and its different types using INTELLISUITE software to get suited for better communication in oil pipelines.Based on the application,it also enumerates the choices of micro or nanoantennas with its specification upholding data adopting techniques with modulation schemes to support IoT applications.	E.B.Priyanka S.Thangavel T.Kalavathidevi	2021	Petroleum Research2021，6，3：	0
	2	Deep LearningModel for Big Data Classification in Apache Spark Environment显示文摘Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classiﬁcation performance.This study proposes a new sigmoid butterﬂy optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classiﬁcation in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classiﬁcation model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.	T.M.Nithya R.Umanesan T.Kalavathidevi C.Selvarathi A.Kavitha	2023	Intelligent Automation & Soft Computing2023，37，9：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费