This week I am supposed to continue working on my annotated bibiliography and search good journal articles from CSU database and Primo Search.
The method of finding and filtering of artciles were discussed during the class and I found some good research articles for my research.
Following are the papers I found and I have provided the annotated bibiliography I have written this week.
- Ahsan, M. I., Nahian, T., Kafi, A. A., Hossain, M. I., & Shah, F. M. (2016). An Ensemble approach to detect Review Spam using hybrid Machine Learning Technique. IEEE Xplore, 388-394.The proposed approach has introduced an ensemble hybrid machine learning approach to detect fake online review. This has two different learning methods which are supervised and active. This generates hybrid dataset which can be used for both pseudo reviews and real life applications. The proposed methodology is called as Hybrid approach to Detect Review Spam (HDRS)
2. Alamlahi, Y., & Muthana, A. (2018). An Email Modelling Approach for Neural Network Spam Filtering to Improve Score-based Anti-spam Systems. Modern Education and Computer Science Press.
This study proposes a system for presenting email to Artificial Neural Network (ANN) which can classify spam and ham. This model is based on 13 fixed features associated with spam combined with text features which are pre-selected.
3. Alurkar, A. A., Ranade, S. B., Joshi, S. V., Ranade, S. S., Sonewar, P. A., Mahalle, P. N., & Deshpande, A. V. (2017). A Proposed Data Science Approach for Email Spam Classification using Machine Learning Techniques. IEEE Xplore.
The text provides a pretrained machine learning technique to identify a pattern using repetitive keywords which are pre-classified as spam. Moreover, this study has focused on classification of incoming emails based on other features in an email such as header, Cc/Bcc, domain and etc. The main objective of the project is to block senders identified through this mechanism who are likely to spam. This project is able to recognize spam emails more efficiently, rather than specifying it manually.
4.Aski, A. S., & Sourati, N. K. (2016). Proposed efficient algorithm to filter spam using machine learning techniques. Elsevier, 145-149.
The study focuses on three machine learning algorithms which can be used to filter spam from legitimate emails with a lower error percentage and a higher efficiency rate. This has used a multilayer perceptron(PM) model in the study. C4.5 decision tree classifier, Naïve Bayes classifier and C4.5 decision tree classifier techniques has been used the proposed project. These techniques used to train data to classify email into spam or ham.