Advanced Encryption Standard (AES) algorithm is one of the most common and widely used symmetric block cipher algorithm. This algorithm has its own particular structure to encrypt and decrypt sensitive data and is applied in hardware and software, all over the world. It is extremely difficult for hackers to get the real data when encrypting by AES algorithm. In AES algorithm, encryption and decryption involves a number of rounds that depends on the length of the key and the number of block columns. So, to improve the strength of the AES the number of rounds is increased. Till date there is not any evidence to crack this algorithm. AES has the ability to deal with three different key sizes such as AES 128, 192 and 256 bit and each of this ciphers has 128 bit block size. This paper will provide an overview of AES algorithm and explain several crucial features of this algorithm in detail.
This document present the implementation of Machine Learning algorithms for the prediction of the house
and the real estate prices. As the house and real estate prices are subject to change with the market
conditions, so it become very difficult to predict the real estate prices with the conventional methods as it may
sometimes gives some exaggerated result that may incur losses. To predict the prices more accurately and
precisely we predict the prices based on the statics of that particular area which has all the trends and
factors on which the price is dependent. To analyse these data , several algorithms are used namely random
forest, linear regression , lasso regression etc. Use of these algorithms decreases the margin of error and
more precise result are achieved. So,we at this point recommend the real estate agents and house vendors as
well as the people to look into the model for better valuation of the house. This model can also be integrated
with the real estates websites to give better recommendation based on the prices using Machine Learning
Algorithms.
Sports Analytics is a growing industry and one of the best real-word applications of Data Science. In this paper, the interest of author and machine learning capabilities were combined to develop a result predictor for football matches. The model proposed is capable of predicting result of any English Premier League Match at the half-time with 75% accuracy. The full-time result predictor is a system based on ensemble of powerful classification algorithms which can predict the odds of winning and draw of both home team and away team on the basis of goals scored at the half time and the current standings in the league. The model learns from the past records of the league and the results of different models are compared in the last section of the paper.
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