Taiwo Ayodele

Email Reply Prediction: A Machine Learning Approach

Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

Evolving email clustering method for email grouping: A machine learning approach

This paper presents the design and implementation of a new system to manage email messages using email evolving clustering method with unsupervised learning approach to group emails base on activities found in the email messages, namely email grouping. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organise and prioritize email better. The goal is to provide highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time.

Integrating business policy into system engineering processes using ontology

Business policy reflects way too many things not only restricting what the company should do but also predicting the future direction of the company. It is desirable that the description of business policy can be interchangeable between business descriptions and engineering specifications so that the rules can be integrated into engineering process without further misinterpreting and conflicting. This paper presents our initiatives of a work to investigate and identify suitable ontologies, as well as suitable ontology alignment framework. A set or sets of formal vocabulary will be developed across various ontologies to specify business policy in a way that different levels of business managers, business people and technical people especially engineers can exchange and share business rules without misinterpreting and inconsistency.

Email Grouping and Summarization: An Unsupervised Learning Technique

This paper presents the design and implementation of a system to group and summarize email messages. The system exploits the subject and content of email messages to classify emails based on users’ activities and auto generate summaries of each incoming messages. Our framework solves the problem of email overload, congestion, difficulties in prioritizing and successfully processing of contents of new incoming messages and difficulties in finding previously archived messages in the mail box by providing a system that groups emails based on users’ activities, and providing summaries of emails.

Email reply prediction: Unsupervised leaning approach

With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organised and prioritized email better; email reply prediction. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.