Enhancing email urgency reply prediction with ATAN-Transformer Fusion

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


In the contemporary landscape of burgeoning email communication, effective sorting and prioritization present formidable challenges. Precise prediction of email urgency is pivotal for efficient email management. Our study addresses this challenge by automating the critical process using advanced machine learning and deep learning algorithms. We introduce the Adaptive Temporal Attention Transformer Fusion (ATATFUSION), a novel methodology meticulously crafted to cater to the delicate intricacies of email urgency prediction. By harnessing cutting-edge techniques, our approach overcomes prevailing limitations in the existing methodologies, offering a pioneering solution for enhanced email management.
Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer Nature
Publication statusAccepted/In press - 22 Nov 2023
EventComputing Conference 2024 - Chiswick, London, United Kingdom
Duration: 11 Jul 202412 Jul 2024


ConferenceComputing Conference 2024
Country/TerritoryUnited Kingdom
Internet address

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