A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks

A climate of increasing deregulation in traditional highway transportation, where the private sector has an expanded role in the provision of traditional transportation services, provides a background for practical policy issues to be investigated. One of the key issues of interest, and the focus of this chapter, would be the equilibrium decision variables offered by participants in this market. By assuming that the private sector participants play a Nash game, the above problem can be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from the classical Cournot-Nash game because each and every player’s actions is constrained by another variational inequality describing the equilibrium route choice of users on the network. In this chapter, we discuss this BLVI and suggest a heuristic coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm is subsequently tested on example problems drawn from the literature. The numerical experiments suggest that the proposed algorithm is a viable solution method for this problem

Evolution of cooperation and trust in an N-player social dilemma game with tags for migration decisions

S.D. would like to acknowledge the support of an Australian Government Research Training Program scholarship to study a PhD degree in Computer Science at the University of Newcastle, Australia, supervised by R.C.We present an evolutionary game model that integrates the concept of tags, trust and migration to study how trust in social and physical groups influence cooperation and migration decisions. All agents have a tag, and they gain or lose trust in other tags as they interact with other agents. This trust in different tags determines their trust in other players and groups. In contrast to other models in the literature, our model does not use tags to determine the cooperation/defection decisions of the agents, but rather their migration decisions. Agents decide whether to cooperate or defect based purely on social learning (i.e. imitation from others). Agents use information about tags and their trust in tags to determine how much they trust a particular group of agents and whether they want to migrate to that group. Comprehensive experiments show that the model can promote high levels of cooperation and trust under different game scenarios, and that curbing the migration decisions of agents can negatively impact both cooperation and trust in the system.We also observed that trust becomes scarce in the system as the diversity of tags increases. This work is one of the first to study the impact of tags on trust in the system and migration behaviour of the agents using evolutionary game theory.Australian GovernmentDepartment of Industry, Innovation and Scienc

Multi-level particle swarm optimisation and its parallel version for parameter optimisation of ensemble models: a case of sentiment polarity prediction

Ensemble learning is increasingly used in sentiment analysis. Determining the parameter settings of ensemble models, however, is not easy. Besides its own parameters, an ensemble model has base-predictors that have their individual parameters. Some ensemble models use a specific base-predictor and could be optimised using standard metaheuristics such as the Particle Swarm Optimisation (PSO) approach. Optimising ensemble models with multiple base-predictor candidates is more complicated and challenging, as there are multiple options to choose from. We therefore propose Multi-Level PSO (ML-PSO) and Parallel ML-PSO (PML-PSO) to optimise the parameters of ensemble models, especially those with multiple base-predictors, for sentiment analysis. The idea is to utilise multiple PSOs as particles of the main PSO. The main PSO optimises ensemble-model parameters and determines the best base-predictor, whereas PSOs within it optimise the corresponding base-predictor�s parameters. Experimental results using Bagging Predictors as the underlying ensemble model show that ML-PSO can improve prediction accuracy, while PML-PSO is able to speed up the processing time and further improve the accuracy

Combining Sentiment Lexicons and Content-Based Features for Depression Detection

Numerous studies on mental depression have found that tweets posted by users with major depressive disorder could be utilized for depression detection. The potential of sentiment analysis for detecting depression through an analysis of social media messages has brought increasing attention to this field. In this article, we propose 90 unique features as input to a machine learning classifier framework for detecting depression using social media texts. Derived from a combination of feature extraction approaches using sentiment lexicons and textual contents, these features are able to provide impressive results in terms of depression detection. While the performance of different feature groups varied, the combination of all features resulted in accuracies greater than 96% for all standard single classifiers, and the best accuracy of over 98% with Gradient Boosting, an ensemble classifier

Evolution of cooperation and trust in an N-player social dilemma game with tags for migration decisions

We present an evolutionary game model that integrates the concept of tags, trust and migration to study how trust in social and physical groups influence cooperation and migration decisions. All agents have a tag, and they gain or lose trust in other tags as they interact with other agents. This trust in different tags determines their trust in other players and groups. In contrast to other models in the literature, our model does not use tags to determine the cooperation/defection decisions of the agents, but rather their migration decisions. Agents decide whether to cooperate or defect based purely on social learning (i.e. imitation from others). Agents use information about tags and their trust in tags to determine how much they trust a particular group of agents and whether they want to migrate to that group. Comprehensive experiments show that the model can promote high levels of cooperation and trust under different game scenarios, and that curbing the migration decisions of agents can negatively impact both cooperation and trust in the system. We also observed that trust becomes scarce in the system as the diversity of tags increases. This work is one of the first to study the impact of tags on trust in the system and migration behaviour of the agents using evolutionary game theory

Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews

The financial impact of positive reviews has prompted some fraudulent sellers to generate fake product reviews for either promoting their products or discrediting competing products. Many e-commerce portals have implemented measures to detect such fake reviews, and these measures require excellent detectors to be effective. In this work, we propose 133 unique features from the combination of content and behaviour-based features to detect fake reviews using machine learning classifiers. Preliminary results show that these features can provide good results for all datasets tested. Detailed analysis of the results, however, reveals the existence of class imbalance issues for two of the bigger datasets - there is a high imbalance between the accuracies of different classes (e.g., 7.73% for the fake class and 99.3% for the genuine class using a Multilayer Perceptron classifier). We therefore introduce two sampling methods that can improve the accuracy of the fake review class on balanced datasets. The accuracies can be improved to a maximum of 89% for both random under and oversampling on Convolutional Neural Networks. Additionally, we propose a parallel cross-validation method that can speed up the validation process in a parallel environment

Predicting Psychological Distress from Ecological Factors: A Machine Learning Approach

Over 300 million people worldwide were suffering from depression in 2017. Australia alone invests more than $9.1 billion each year on mental health related services. Traditional intervention methods require patients to first present with symptoms before diagnosis, leading to a reactive approach. A more proactive approach to this problem is highly desirable, and despite ongoing work using approaches such as machine learning, further work is required. This paper aims to provide a foundation by building a machine learning model across multiple techniques to predict psychological distress from ecological factors alone. Eight different classification techniques were implemented on a sample dataset, with the best results achieved through Logistic Regression, providing an accuracy of 0.811. The preliminary results suggest that, with future improvements to implementation and analysis, an accurate and reliable model is possible. This study, with the proposed base model, can potentially lead to the development of a proactive solution to the global mental health crisis

A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

Depression is one of the leading causes of suicide worldwide. However, a large percentage of cases of depression go undiagnosed and, thus, untreated. Previous studies have found that messages posted by individuals with major depressive disorder on social media platforms can be analysed to predict if they are suffering, or likely to suffer, from depression. This study aims to determine whether machine learning could be effectively used to detect signs of depression in social media users by analysing their social media posts—especially when those messages do not explicitly contain specific keywords such as ‘depression’ or ‘diagnosis’. To this end, we investigate several text preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to propose a generalised approach for depression detection using social media texts. We first use two public, labelled Twitter datasets to train and test the machine learning models, and then another three non-Twitter depression-class only datasets (sourced from Facebook, Reddit, and an electronic diary) to test the performance of our trained models in other social media sources. Experimental results indicate that the proposed approach is able to effectively detect depression via social media texts even when the training datasets do not contain specific keywords (such as ‘depression’ and ‘diagnose’), as well as when unrelated datasets are used for testing

Physiological studies on Alternaria porri and Stemphylium vesicarium causing purple blotch complex in onion

Effect of different culture media, pH levels and natural substrates on mycelial growth and sporulation of Alternaria porri and Stemphylium vesicarium, incitants of purple blotch complex of onion was investigated. Maximum colony growth of A. porri and S. vesicarium was recorded on oat meal agar and Richard’s agar while, oat meal agar and V8 juice agar recorded the maximum sporulation, respectively. Similarly, pH 5.0 and 6.0 on potato dextrose agar (PDA) recorded the maximum colony growth of A. porri and S. vesicarium, respectively. None of the pH levels on PDA supported the sporulation of A. porri while maximum sporulation of S. vesicarium was recorded on pH 5.0. Onion seed stalks and garlic leaves were found to be the most suitable natural substrates for mass multiplication of A. porri and S. vesicarium, respectively. The present findings are useful for preparation of inoculums required for resistance breeding and fungicidal evaluation against purple blotch complex