Evolutionary deep learning

The primary objective of this thesis is to investigate whether evolutionary concepts can improve the performance, speed and convenience of algorithms in various active areas of machine learning research. Deep neural networks are exhibiting an explosion in the number of parameters that need to be trained, as well as the number of permutations of possible network architectures and hyper-parameters. There is little guidance on how to choose these and brute-force experimentation is prohibitively time consuming. We show that evolutionary algorithms can help tame this explosion of freedom, by developing an algorithm that robustly evolves near optimal deep neural network architectures and hyper-parameters across a wide range of image and sentiment classification problems. We further develop an algorithm that automatically determines whether a given data science problem is of classification or regression type, successfully choosing the correct problem type with more than 95% accuracy. Together these algorithms show that a great deal of the current "art" in the design of deep learning networks - and in the job of the data scientist - can be automated. Having discussed the general problem of optimising deep learning networks the thesis moves on to a specific application: the automated extraction of human sentiment from text and images of human faces. Our results reveal that our approach is able to outperform several public and/or commercial text sentiment analysis algorithms using an evolutionary algorithm that learned to encode and extend sentiment lexicons. A second analysis looked at using evolutionary algorithms to estimate text sentiment while simultaneously compressing text data. An extensive analysis of twelve sentiment datasets reveal that accurate compression is possible with 3.3% loss in classification accuracy even with 75% compression of text size, which is useful in environments where data volumes are a problem. Finally, the thesis presents improvements to automated sentiment analysis of human faces to identify emotion, an area where there has been a tremendous amount of progress using convolutional neural networks. We provide a comprehensive critique of past work, highlight recommendations and list some open, unanswered questions in facial expression recognition using convolutional neural networks. One serious challenge when implementing such networks for facial expression recognition is the large number of trainable parameters which results in long training times. We propose a novel method based on evolutionary algorithms, to reduce the number of trainable parameters whilst simultaneously retaining classification performance, and in some cases achieving superior performance. We are robustly able to reduce the number of parameters on average by 95% with no loss in classification accuracy. Overall our analyses show that evolutionary algorithms are a valuable addition to machine learning in the deep learning era: automating, compressing and/or improving results significantly, depending on the desired goal

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Investigating the Efficacy of Online Text Reconstruction Exercises for Facilitating the Use of Metadiscourse Markers in First-Year Japanese University Students’ Argumentative Writing

This thesis examines pedagogical merits of online text reconstruction exercises (OTREs) and examines their efficacy in teaching argumentative writing (AW) and students’ experience of learning through them. It looks at a specific type of OTRE called WebSequiturs and that application’s unique pedagogical affordances. Key research questions ask to what extent OTREs can influence EFL university students to use select metadiscourse markers (MDMs) in written argument; and to what extent those exercises can guide students to use those MDMs appropriately. A third research question asks about students’ perceptions of their experiences completing OTREs. This study was conducted at Tokyo University of Science and involved nine students in a first-year undergraduate compulsory English course. This study strives for an interpretive understanding of participants’ experiences. Hence, I pursue this topic through action research. I also take a mixed-methods approach, using both quantitative and qualitative data. Quantitative data shows that following OTRE sessions, frequency of the use of selected MDMs in participants’ writing increased. The variety of MDMs having similar meanings likewise increased. There was also an increase in the range of communicative functions across which MDMs were used. Moreover, the unique uses of select MDMs also increased. Finally, there was an increase in appropriacy of use. Qualitative data showed that during the OTREs participants sometimes reached metalinguistic levels of awareness, as evidenced by their utterances. These utterances were frequently followed by increased levels of appropriacy of MDM use in participants’ writing. During semi-structured interviews, participants responded, evincing the view that OTREs had played an important role in their developing use of MDMs in their AW. The findings of this study suggest that OTREs are helpful in influencing English learners to become autonomous users of selected MDMs and that they can also guide learners to use MDMs appropriately. The findings also show that participants understood the purpose of the OTREs and valued the exercises for their linguistic instruction and for their collaborative qualities. In my conclusion, I recommend that universities implement programs using similar OTREs and train their teachers in how to maximise their efficacy in teaching AW