1,051 research outputs found
Online Programming Judge System (UOJ)
This research conducts a study to build an Online Programming Judge system with a mechanism
to generate test cases automatically using Particle Swarm Optimization (PSO) algorithm. The
system has the function to judge programming code by evaluating the output that the program
produced. Based on the problem that it is time consuming for lecturers to manually compile, run
and verify every student programs for judging. Moreover, they also need to define test cases for
different programming exercises in order to judge student‘s code. The system is built on the
purpose to assist lecturers in Universiti Teknologi PETRONAS in judging code submitted from
students and generate test cases for every programming exercise automatically. It also helps UTP
students practice and enhancing their programming skills. In this research, details of judging
process are explored. Moreover, the mechanism of test cases generation using PSO algorithm is
deeply analyzed. The study would focus on the primary structure of PSO and the proposed
fitness function to calculate fitness value for each generated test case. There are comparisons
between manual and automatic PSO test case generation results that would be conducted to
evaluate the efficiency of the proposed method. Finally, conclusion of current results and
recommendation for future development are also stated
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Influence maximization is a crucial issue for mining the deep information of
social networks, which aims to select a seed set from the network to maximize
the number of influenced nodes. To evaluate the influence spread of a seed set
efficiently, existing studies have proposed transformations with lower
computational costs to replace the expensive Monte Carlo simulation process.
These alternate transformations, based on network prior knowledge, induce
different search behaviors with similar characteristics to various
perspectives. Specifically, it is difficult for users to determine a suitable
transformation a priori. This article proposes a multi-transformation
evolutionary framework for influence maximization (MTEFIM) with convergence
guarantees to exploit the potential similarities and unique advantages of
alternate transformations and to avoid users manually determining the most
suitable one. In MTEFIM, multiple transformations are optimized simultaneously
as multiple tasks. Each transformation is assigned an evolutionary solver.
Three major components of MTEFIM are conducted via: 1) estimating the potential
relationship across transformations based on the degree of overlap across
individuals of different populations, 2) transferring individuals across
populations adaptively according to the inter-transformation relationship, and
3) selecting the final output seed set containing all the transformation's
knowledge. The effectiveness of MTEFIM is validated on both benchmarks and
real-world social networks. The experimental results show that MTEFIM can
efficiently utilize the potentially transferable knowledge across multiple
transformations to achieve highly competitive performance compared to several
popular IM-specific methods. The implementation of MTEFIM can be accessed at
https://github.com/xiaofangxd/MTEFIM.Comment: This work has been submitted to the IEEE Computational Intelligence
Magazine for publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information
As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation
and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused
people. To be able to detect such COVID-19 misinformation, an effective detection method should be
applied to obtain more accurate information. This will help people and researchers easily differentiate
between true and fake news. The objective of this research was to introduce an enhanced evolutionary
detection approach to obtain better results compared with the previous approaches. The proposed
approach aimed to reduce the number of symmetrical features and obtain a high accuracy after
implementing three wrapper feature selections for evolutionary classifications using particle swarm
optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments
were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained
prediction results, the proposed model revealed an optimistic and superior predictability performance
with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison
with other state-of-the-art classifiers, our results showed that the proposed detection method with
the genetic algorithm model outperformed other classifiers in the accurac
An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection
The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models
Stock market prediction using machine learning classifiers and social media, news
Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble
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