Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://cronfa.swan.ac.uk/Record/cronfa54400/Download/54400__17763__d6602f13cc44449b8145bc15173fc903.pdf

Setting the future of digital and social media marketing research: Perspectives and research propositions

Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse

Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model

The purpose of this paper is to present a detailed and critical look at the evolution of online survey research since Evans and Mathur’s (2005) article on the value of online surveys. At that time, online survey research was in its early stages. Also covered are the present and future states of online research. Many conclusions and recommendations are presented.,The look back focuses on online surveys, strengths and weaknesses of online surveys, the literature on several aspects of online surveys and online survey best practices. The look ahead focuses on emerging survey technologies and methodologies, and new non-survey technologies and methodologies. Conclusions and recommendations are provided.,Online survey research is used more frequently and better accepted by researchers than in 2005. Yet, survey techniques are still regularly transformed by new technologies. Non-survey digital research is also more prominent than in 2005 and can better track actual behavior than surveys can. Hybrid surveys will be widespread in the future.,The paper aims to provide insights for researchers with different levels of online survey experience. And both academics and practitioners should gain insights.,Adhering to a strong ethics code is vital to gain respondents’ trust and to produce valid results.,Conclusions and recommendations are offered in these specific areas: defining concepts, understanding the future role of surveys, developing and implementing surveys and a survey code of ethics. The literature review cites more than 200 sources.

The value of online surveys: a look back and a look ahead

https://cronfa.swan.ac.uk/Record/cronfa29584/Download/0029584-15082016211834.pdf

Predicting the “helpfulness” of online consumer reviews

Deep learning to filter SMS spam

A Machine Learning approach for automation of Resume Recommendation system

The rapid growth of Internet users led to unwanted cyber issues, including cyberbullying, hate speech, and many more. This article deals with the problems of hate speech on Twitter. Hate speech appears to be an inflammatory kind of interaction process that uses misconceptions to express a hate ideology. The hate speech focuses on various protected aspects, including gender, religion, race, and disability. Owing to hate speech, sometimes unwanted crimes are going to happen as someone or a group of people get disheartened. Hence, it is essential to monitor user’s posts and filter the hate speech related post before it is spread. However, Twitter receives more than six hundred tweets per second and about 500 million tweets per day. Manually filtering any information from such a huge incoming traffic is almost impossible. Concerning to this aspect, an automated system is developed using the Deep Convolutional Neural Network (DCNN). The proposed DCNN model utilises the tweet text with GloVe embedding vector to capture the tweets’ semantics with the help of convolution operation and achieved the precision, recall and F1-score value as 0.97, 0.88, 0.92 respectively for the best case and outperformed the existing models.

/pdf/a-framework-for-hate-speech-detection-using-deep-3irdjzxtc4.pdf

A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

Community Question Answering (CQA) sites have become a very popular place to ask questions and give answers to a large community of users on the Internet. Stack Exchange is one of the popular CQA sites where a large amount of contents are posted every day in the form of questions, answers and comments. The answers on Stack Exchange are listed by their recent occurrences, time of posting or votes obtained by peer users under three tabs called active, oldest and votes, respectively. Votes tab is the default setting on the site and is also preferred tab of users because answers under this tab are voted as good answers by other users. The problem of voting-based sorting is that new answers which are yet to receive any vote are placed at the bottom in vote tab. The new answer may be of sufficiently high-quality to be placed at the top but no or fewer votes (later posting) have made them stay at the bottom. We introduce a new tab called promising answers tab where answers are listed based on their usefulness, which is calculated by our proposed system using the classification and regression models. Several textual features of answers and users reputation are used as features to predict the usefulness of the answers. The results are validated with good values of precision, recall, F1-score, area under the receiver operating characteristic curve (AUC) and root mean squared error. We also compare the top ten answers predicted by our system to the actual top ten answers based on votes and found that they are in high agreement.

Pradeep Kumar Roy

Papers

Predicting the “helpfulness” of online consumer reviews

Deep learning to filter SMS spam

A Machine Learning approach for automation of Resume Recommendation system

A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

Finding and Ranking High-Quality Answers in Community Question Answering Sites