FAKE NEWS DETECTION ON THE WEB: A DEEP LEARNING BASED APPROACH

The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. This work complements such research by proposing an approach that utilizes the amalgamation of Natural Language Processing (NLP), and Deep Learning techniques such as Long Short-Term Memory (LSTM). Moreover, in Information System’s paradigm, design science research methodology (DSRM) has become the major stream that focuses on building and evaluating an artifact to solve emerging problems. Hence, DSRM can accommodate deep learning-based models with the availability of adequate datasets. Two publicly available datasets that contain labeled news articles and tweets have been used to validate the proposed model’s effectiveness. This work presents two distinct experiments, and the results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets. Finally, the findings suggest that the sentiments, tagging, linguistics, syntactic, and text embeddings are the features that have the potential to foster fake news detection through training the proposed model on various dimensionality to learn the contextual meaning of the news content

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

Abstract Background Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases. Results Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present. Conclusion This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.</p

Daylight design exploration using parametric processes and Artificial Neural Networks

The integration of Artificial Neural Networks (ANNs) as surrogates for daylight simulation models within parametric design environments promises greater computational efficiency in the exploration and optimisation of design solutions. This thesis demonstrates how ANNs can be integrated in design exploration processes, specifically focusing on the investigation of design solutions for the central atrium of a school building. ANNs are validated as surrogates for climate-based-performance metrices including Daylight Autonomy (DA) and spatial Daylight Autonomy (sDA) for thresholds of 100 lux (DA100) and 300 lux (DA300). The presented work discusses the prediction accuracies and sensitivities of the developed ANN models, the efficacy of the method, and atrium design strategies aimed at improving daylight conditions in atrium adjacent spaces. The research also critically evaluates daylight performance metrices and their implications on the design outcome of optimisation. Contributions are made in terms of validating ANN prediction accuracies for annual climate-based-daylight metrices, presenting a workflow for the selection and optimisation of input features from parametric models, and identifying limitations of ANN predictions related to model complexity and number of design variables. The work also contributes to the field of atrium design research by analysing the impact of atrium design changes on daylight performance, and by employing and comparing multiple daylight performance metrices. Thesis results showed that robust predictions could be achieved by optimising the network architecture of ANN ensembles, optimising input features, and employing cross-validation and early stopping. Overall, high accuracies were achieved for performance metrices predicting both % of occupied hours in a year and the % of space. For %time metrices, mean absolute errors were around 0.6% DA MAE (for DA ranging from 0 to 100%) for the 100 lux and 300 lux thresholds. For %space metrices, mean absolute errors were around 0.3% sDA MAE for both the 100 lux and 300 lux thresholds (for sDA ranging between 0 and 100%). Daylight simulation time was reduced by up to 71% by integrating ANNs within the design process. The design results showed that optimum atrium design solutions varied between the sDA300/50% and sDA100/50% metric. Additionally, the favorable design solutions also varied depending on whether design solutions were explored via the %space results of the sDA metric or the %time visualisations of the DA metric. Hence, this work discusses both the target thresholds employed in daylight performance metrices and bias that can be introduced by careless implementation of them. In terms of design strategy, southward orientations of the atrium well and reducing WWR towards the top floors increased daylight in atrium adjacent spaces on lower floors, but was met by a tradeoff, as this also reduced daylight on upper floors. The interdependencies of atrium design changes and the value and interpretability of the applied daylight performance metrices are further elaborated on in this thesis