In this paper, the prediction of damage results for complex network is considered under grey information attack. Firstly, in order to construct more realistic networks, a new algorithm is proposed to ...generate 3 types of fully connected networks (normal scale-free network, scale-free network with cutoff, random network). Secondly, robustness of the 3 networks is analyzed under grey information attack. And then, a new method is proposed to predict the damage results by training the BP neural network. Thirdly, the effects of different topological parameters on the damage results are analyzed and a new method is proposed to find central nodes of the network. Finally, the damage results of a real bus network under grey information attack are predicted by the proposed method and several suggestions are given to help protect the real bus network.
In this paper, we presented the problem of predicting response to recombinant human growth hormone (GH) treatment in GH-deficient children. Such a prediction can be done by techniques of mathematical ...modelling and is important because the therapy consists of daily injections and is expensive; thus, it should be administered only to those patients who will, with high probability, benefit from it. Until now, the leading methodological approach to this problem was multiple regression analysis. Several authors demonstrated that it is possible to derive useful models by this method; however, it has some obvious limitations that can be avoided with the use of the proposed neural network approach.
This research aims to examine the most effective methodology for predicting football match outcomes within a league format, specifically focusing on the English Premier League. The predictions are ...solely based on analyzing previous match data, without any information about the current game, such as player lineups or whether the match is played at the home or away stadium. We compared the prediction accuracy of two models, the Multilayer Perceptron and Convolutional Neural Networks, using a training dataset of 1,160 matches. The Convolutional Neural Networks, incorporating our player performance metrics, achieved the highest accuracy of 55.31% when tested with 320 matches, particularly in predicting home team wins with an accuracy of 71.52%. Notably, this accuracy surpasses that of football experts and bookmakers.
The article that follows demonstrates how to build new characteristics from pre-existing ones and forecast election outcomes using a popular machine learning method. However, this paper has observed ...a method of leveraging social media to anticipate results that is not more precise in the real world, due to the manipulation and usage of bots to quickly increase in popularity, so that the accuracy has gone down. This notepaper shows that a machine learning supervised learning technique which uses the most recent Indian electoral datum for forecasting the overall outcomes of national elections as well as many local results. The findings imply that the forecasting outcomes are close to the actual outcome with more accurate results when compared to the other methodologies. Additionally, because of it is simple, reproducibility, so resistance in opposite to volume manipulation, it outperforms several state-of-the-art approaches. As far as this research aware, this is the initial attempt to verify ML prototype for the forecasting of the 2019 Indian elections.
The paper analyzes the problem of predicting the outcome of elections (how many votes each candidate is going to get), given an imperfect information on the preferences of the voters. We assume that ...we have a fixed prior on the preferences of each voter for each candidate. We have used two naive algorithms which predict the votes obtained by each candidate in an election. The algorithms are fast and have a linear time and space complexity. We have implemented the algorithms and experimented with simulated data. The results are compared with the method proposed by N. Hazon et al. and it is found to give good results in a very short time. We then experimentally show that these naive algorithms give the same approximation, with linear time complexity.
Esports have evolved into a major form of entertainment, drawing hundreds of millions of viewers to its online competitive broadcasts. Using Esports telemetry data to predict the outcome of a match ...is a well-researched topic, but micropredictions of specific in-game events are explored only sparingly. How accurately can we predict specific in-game events within a limited time window, and how can these predictions be used in a live broadcast? This research aims at predicting in-game deaths using telemetry data in Counter-Strike: Global offensive (CS:GO). We establish a data processing pipeline to acquire and re-structure raw in-game data and propose a set 36 features which will ultimately be used to predict in-game deaths within a three second window. Three neural network models are compared, namely convolutional (CNN), recurrent (RNN) and long short-term memory (LSTM). Our results show that the LSTM network has the best predictive accuracy (F1 0.38) when prompted, for all 10 players of a competitive game of CS:GO. The predictions are most influenced by features related to a player’s average in-game death count, health points, enemies in range and equipment value. Our model enables real-time micropredictions of deaths in CS:GO, and may be leveraged by Esports commentators and game observers to direct their focus on critical in-game events during a live competitive broadcast.
Class result prediction using machine learning Pushpa, S K; Manjunath, T N; Mrunal, T V ...
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon),
2017-Aug.
Conference Proceeding
More than 2.5 quintillion bytes of data is being generated across the globe. In fact, this data is as much as 90% of the data in the world today, and has been created in the last two years alone. Big ...data describes the large volume of data that inundates a business on a day to day basis. Huge amount of data is being generated by everything around us at all times and is produced by every digital process and social media exchange through systems, sensors, mobile devices, etc. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. To extract meaningful value from big data, one needs optimal processing power, analytics capabilities and skills. Using the concept of machine learning, a number of algorithms are explored in order to predict the result of class students. Based on the performance of the students in previous semester, and the scores of internal examinations of the current semester, the final result, whether the student passes or fails the current semester is computed before the final examination actually takes place.
Predicting election results is a hot area in political science. In the last decade, social media has been widely used in political elections. Most approaches can predict the result of a national ...election. However, it is still challenging to predict the overall results of many local elections. This paper presents a machine learning based strategy to analyze Twitter data for predicting the overall results of many local elections. To verify the effectiveness of this strategy, we apply it for analyzing the Twitter data based on the 2018 midterm election in United States. The results suggest the predicted results are close to the actual election outcome.
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