Journal of Computational Science 

About: Journal of Computational Science is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Solver. It has an ISSN identifier of 1877-7503. Over the lifetime, 1571 publications have been published receiving 31677 citations.

Twitter mood predicts the stock market.

Abstract: Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%. Index Terms—stock market prediction — twitter — mood analysis.

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

Abstract: Efficiently detecting network intrusions requires the gathering of sensitive information. This means that one has to collect large amounts of network transactions including high details of recent network transactions. Assessments based on meta-heuristic anomaly are important in the intrusion related network transaction data’s exploratory analysis. These assessments are needed to make and deliver predictions related to the intrusion possibility based on the available attribute details that are involved in the network transaction. We were able to utilize the NSL-KDD data set, the binary and multiclass problem with a 20% testing dataset. This paper develops a new hybrid model that can be used to estimate the intrusion scope threshold degree based on the network transaction data’s optimal features that were made available for training. The experimental results revealed that the hybrid approach had a significant effect on the minimisation of the computational and time complexity involved when determining the feature association impact scale. The accuracy of the proposed model was measured as 99.81% and 98.56% for the binary class and multiclass NSL-KDD data sets, respectively. However, there are issues with obtaining high false and low false negative rates. A hybrid approach with two main parts is proposed to address these issues. First, data needs to be filtered using the Vote algorithm with Information Gain that combines the probability distributions of these base learners in order to select the important features that positively affect the accuracy of the proposed model. Next, the hybrid algorithm consists of following classifiers: J48, Meta Pagging, RandomTree, REPTree, AdaBoostM1, DecisionStump and NaiveBayes. Based on the results obtained using the proposed model, we observe improved accuracy, high false negative rate, and low false positive rule.

Multi-grade brain tumor classification using deep CNN with extensive data augmentation

Abstract: Numerous computer-aided diagnosis (CAD) systems have been recently presented in the history of medical imaging to assist radiologists about their patients. For full assistance of radiologists and better analysis of magnetic resonance imaging (MRI), multi-grade classification of brain tumor is an essential procedure. In this paper, we propose a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. Firstly, tumor regions from an MR image are segmented using a deep learning technique. Secondly, extensive data augmentation is employed to effectively train the proposed system, avoiding the lack of data problem when dealing with MRI for multi-grade brain tumor classification. Finally, a pre-trained CNN model is fine-tuned using augmented data for brain tumor grade classification. The proposed system is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.

Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model

Abstract: Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading, the understanding of historical epidemics, the assessment of the role of human mobility in shaping global epidemics, and the analysis of mitigation and containment scenarios.

Chaotic bat algorithm

Abstract: Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.