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Enhancing Email Urgency Reply Prediction with ATAN-Transformer Fusion

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Intelligent Computing (SAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1016))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Solent University, East Park Terrace, Southampton, SO14 0YN, UK

    Taiwo Oladipupo Ayodele

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  1. Taiwo Oladipupo Ayodele
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Correspondence to Taiwo Oladipupo Ayodele .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Ayodele, T.O. (2024). Enhancing Email Urgency Reply Prediction with ATAN-Transformer Fusion. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-031-62281-6_10

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