Two novel ensemble approaches for improving classification of neural networks

x, 77 leaves ; 29 cmThe task of pattern recognition is one of the most recurrent tasks that we encounter in our lives. Therefore, there has been a significant interest of automating this task for many decades. Many techniques have been developed to this end, such as neural networks. Neural networks are excellent pattern classifiers with very robust means of learning and a relatively high classification power. Naturally, there has been an increasing interest in further improving neural networks’ classification for complex problems. Many methods have been proposed. In this thesis, we propose two novel ensemble approaches to further improving neural networks’ classification power, namely paralleling neural networks and chaining neural networks. The first seeks to improve a neural network’s classification by combining the outputs of a set of neural networks together via another neural network. The second improves a neural network’s accuracy by feeding the outputs of a neural network into another and continually doing so in a chaining fashion until the error is reduced sufficiently. The effectiveness of both approaches has been demonstrated through a series of experiments. i

Co-localization of EEG and functional MRI recordings of interictal discharges in human focal epilepsy

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A Team Composition Approach For Social Crowdsourcing Communities

This research takes place in an emerging paradigm of social computation that we name social crowdsourcing communities (SCCs). These are moderated online communities where members participate in collaborative activities (i.e. queries) designed to elicit their opinions concerning some topics, products, or services. This paradigm is distinct in that it combines the powers of crowdsourcing and social networking (SN) to allow for systematic querying of crowds and synthesizing response data (i.e. contributions) into coherent reports for decision-makers. SCCs consist of a beneficiary (i.e. the operators, the moderators, the analysts, and the organization that benefits from the reports), queries, a working crowd, and a platform where all activities occur. We show that it is possible to apply methods and techniques from existing fields to alleviate many of their challenges. One of these challenges is improving teamwork outcomes (i.e. contribution quality). Currently, SCC members, who are interested in a specific task, self-assemble into teams without considering any factors that may cause them to exhibit lower levels of productivity, participation, and contribution quality. The growth and query frequency restrictions imposed on these platforms by their operators to control operation costs further exacerbate this challenge. This thesis demonstrates how member behaviour can guide team formations and identifies specific behavioural characteristics related to improved team performance through an exploratory case study. It accomplishes this goal by capturing member behaviour in a model and using it to explore the compositions of existing teams. In doing so, this thesis identifies the specific compositions associated with increased team performance. The outcomes indicate the validity of this approach and provide a strong foundation for further investigation

A Report on Propagative Influence: An Influence measure for directed and undirected networks

In this report, we present a new influence measure, namely Propagative Influence - PI. PI measure defines influence of a node in terms of its relationships. It suggests that a node's influence is a product of the interactions of nodes and is a state given to the node by the recognition and endorsements of others. We show that using PI will identify all influential nodes in a network in an effective and timely manner. The theory of this measure is very relevant and easily understood. We propose two variations of this measure, Static Propagative Influence - SPI and Dynamic Propagative Influence - DPI. Both variations use the same underlying algorithm but differ in influence propagation values. In this work, the abilities and effectiveness of our proposed measure is demonstrated through a series of examples on synthetic datasets which reflect real-world situations

A study of solid lubricants used to prevent wear and friction in powder metallurgy production

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Towards Quantifying Behaviour in Social Crowdsourcing Communities

We analyze crowdsourcing communities by creating a detailed process for quantifying individual behaviour in online environments. The key feature of our communities is their social interactions so we call these social crowdsourcing communities (SCC). First, we derive factors based on actions captured about textual contributions. We interpret and name these factors. Then we demonstrate their utility in predicting the quality of team contributions. We capture the actions of members using measurable variables and perform factor analysis on these to produce factors of behaviour in SCCs (i.e. dimensions of behaviour). We derive factor scores for each member. An abstract notion of teams is used that is based on the social interactions. Team scores are then determined by the aggregation of the individual factor scores. The relationship between the team-level factor scores and the quality of contributions made by each team are then used as a proxy for team effectiveness. We found that member behaviour has three dimensions/factors: Impact, Activity, Policing/Rowdiness and there is a linear relationship between a team's contribution quality and their Impact scores. We also found a moderate negative linear relationship between the smallest Activity scores in each team with the quality of their individual contributions. This shows that teams that produce higher quality contributions tend to have higher total and maximum Impact score with lower levels of Activity. Thus, we demonstrate that properly aggregated behavioural factors can predict the quality of team-level contributions