Machine Learning Techniques and Cloud Computing to Estimate River Water Quality—Survey

2020 ◽  
pp. 387-396
Author(s):  
M. Ranjithkumar ◽  
L. Robert
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Cloud Computing ◽  
River Water ◽  
River Water Quality ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Water Quality Survey

scholarly journals Machine learning techniques in river water quality modelling: a research travelogue

2020 ◽  
Author(s):  
Sakshi Khullar ◽  
Nanhey Singh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Resources ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Aquatic Life ◽  
Learning Techniques

Abstract Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers has been adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a matter of great concern as it is deteriorating the water quality, making it unfit for any type of use. Contaminated water resources can cause serious effects on humans as well as aquatic life. Hence, water quality monitoring of reservoirs is essential. Recently, water quality modeling using AI techniques has generated a lot of interest and it can be very beneficial in ecological and water resources management. This paper presents the state-of-the-art application of machine learning techniques in forecasting river water quality. It highlights the different key techniques, advantages, disadvantages, and applications with respect to monitoring the river water quality. The review also intends to find the existing challenges and opportunities for future research.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

A New Framework for Classification of Distributed Denial of Service (DDOS) Attack in Cloud Computing by Machine Learning Techniques

2014 ◽  
Vol 20 (1) ◽  
pp. 175-178
Author(s):  
Mostafa Behzadi ◽  
Ramlan Mahmod ◽  
Mehdi Barati ◽  
Azizol Bin Hj Abdullah ◽  
Mahda Noura
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Framework
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