The purpose of the present article is to highlight the outcomes of Indian premier league cricket match utilizing a managed taking in come nearer from a team-based point of view. The methodology consists of prescriptive and descriptive models. Descriptive model focuses mainly on two aspects they are, it describes data and statistics of the previous information. i.e., batting, balling or allrounder and It predicts past matches of IPL. Predictive model predicts ranking and winning percentage of the team. The two models show the measurements of winning level of the group Winner that the user has selected. This paper predicts the result through which technique match has highest result. The dataset consists of two groups that is the toss outcome, venue date, which tells about of the counterpart for all matches. Since the nature impact can't be expected in the game, 109 matches which were either finished by downpour or draw/tie, have been taken out from the dataset. The dataset is partitioned into two sections to be specific the test information and the train information.The readiness dataset contains the 70% of the information from our dataset and the test dataset contains 30% of the information from our dataset. There were all out of 3500 coordinates in getting ready dataset and 1500 matches. This paper has been researched earlier by different scholars like Pathak and Wadwa, Munir etl ,and many other scholars. This viewpoint discusses the application of INDIAN PREMIER LEAGUE Matches held in different states. Gives the score of batsman and bowler with the help of machine learning techniques. Focuses on predicted analysis which is predicted by applying with various AI strategies to the real outcome actual result and gives the percentage of predicted result.

No abstract

Real-time detection of domain names that are generated using the domain generating algorithms (DGA) is a challenging cyber security challenge. Traditional malware control methods, such as blacklisting, are insufficient to handle DGA threats. In this paper, a machine learning framework for identifying and detecting DGA domains is proposed to alleviate the threat. The proposed machine learning framework consists of a two-level model. In the two-level model, the DGA domains are classified apart from normal domains and then the clustering method is used to identify the algorithms that generate those DGA domains. K E Y W O R D S density-based spatial clustering of applications with noise, DGA, gradient boosting tree, J48, Jaccard-index, logistic regression, machine learning, malware, n-grams entropy ordering points to identify the clustering

No abstract

No abstract