Bioscience Biotechnology Research Communications

An Open Access International Journal

P-ISSN: 0974-6455 E-ISSN: 2321-4007

Bioscience Biotechnology Research Communications

An Open Access International Journal

Sreelatha. P1, J. Faritha  Banu2,  T. Ch. Anil Kumar3, D. Sugumar4,
Shailendra Kumar Rawat5 and Ahmad Jawad Niazi6

1Department of Biomedical Engineering, KPR Institute of Engineering and Technology, Arasur,  Tamil Nadu, India.

2Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

3Department of Mechanical Engineering, Vignan’s Foundation for Science Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India.

4Department of ECE, Signal Processing Lab, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamil Nadu, India.

5Department of Computer Science, Maharishi Institute of Information Technology, Lucknow, Uttar Pradesh, India.

6Department of Architecture, Kabul Polytechnic University, Kabul City, Afghanistan.

Corresponding author email: sreelathaselvaraj@gmail.com

Article Publishing History

Received: 12/05/2021

Accepted After Revision: 13/07/2021

ABSTRACT:

In recent decade, there has been an abundant generation of high-dimensional data in larger volumes while measuring the water resource environment. This environment consists of large spatial areas with high resolution and high temporal data, where such collection needs to be summarized. In this paper, we develop a deep learning model to recognize the meaningful relationship between the objects in data during decision-making process. Appropriate extraction of meaningful relationship using deep learning reduces the redundant information that provides inter-variable relationships and characteristic patterns. The deep learning framework recognizes the patterns via clustering the data that provides better understanding of objects. The experimental results are conducted with various training options and parameter selection on the information extracted. The results of simulation shows that the proposed deep learning model achieves improved clustering than other methods.

KEYWORDS:

Clustering, Deep Learning, Water Resource Engineering, Patterns, Data

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