Keywords : data analysis

Review of Deployment of Machine Learning in Blockchain Methodology

Sona Solanki; Asha D Solanki

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 9, Pages 14-20
DOI: 10.47392/irjash.2020.141

The evolution of blockchain methodology has been a remarkable, highly transformative and trend-setting platform in current years. BT's accessible platform reinforces data protection and confidentiality. In addition, the consensus framework in it ensures system is protected and accurate. Nevertheless, it introduces additional security challenges such as invasion by the majority and double consumption. Data analysis on encrypted data centered on blockchain is crucial to manage the existing challenges. Insights on these results elevates the value of emerging of Machine Learning technique. It covers the fair quantity of data needed to make specific choices. Consistency of data and its distribution are very critical in ML to increase findings reliability. The fusion of these two techniques will produce extremely accurate outcomes. In this article, we describe a thorough analysis on ML implementation to make smart applications based on BT further robust to threats. There are numerous standard ML approaches such as Support Vector Machines (SVM), Clustering, Bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long-Term Memory (LSTM) that can be employed to evaluate the threats on a block chain network. Finally, we discuss how two different techniques can be implemented in a number of smart applications like Unmanned Aerial Vehicle (UAV), Smart Grid (SG), medical care and Smart cities.

Latent Approach in Entertainment Industry Using Machine Learning

Salini Suresh; Suneetha V; Niharika Sinha; Sabyasachi Prusty

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICARD 2020, Pages 304-307
DOI: 10.47392/irjash.2020.106

Nowadays, a huge amount of data is available everywhere. Therefore, we need to prioritize analysing this dataset which would help us in gaining some meaningful information for the development of an algorithm based on the analysis. These feet can be obtained by using Machine Learning, Data Mining, and Data Analysis. Machine Learning which is a part of Artificial Intelligence is used for designing algorithms based on trends of data, patterns and the relation found between them. ML has been used in various fields such as Marketing, gameplay, intrusion detection, bioinformatics, information retrieval, healthcare, entertainment and also on COVID -19 applications and so on. This paper presents an overview of the contribution of ML in Entertainment industry