Transformer Deep Learning Architectures: Advances and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 25 August 2024 | Viewed by 2701
Special Issue Editor
Interests: big data/data science; machine/deep learning; software development; health informatics; sensor information extraction
Special Issue Information
Dear Colleagues,
This Special Issue spotlights the advancements in and applications of Transformer-based deep learning architectures. Transformers have significantly influenced artificial intelligence (AI), particularly natural language processing (NLP), with their innovative approach to handling sequential data. This Special Issue explores the core components of these architectures, including their self-attention mechanism and positional encoding, and discusses recent developments that enhance efficiency, interpretability, and scalability.
The Special Issue also delves into the broad spectrum of applications of Transformers, ranging from traditional tasks such as text summarization, machine translation, and sentiment analysis, to innovative utilizations in language generation and conversational AI, including chatbots and dialogue systems like ChatGPT. Beyond these conventional domains, the Special Issue also highlights breakthrough applications in emerging fields such as computer vision, bioinformatics, health informatics and climate modeling. It provides insight into how models such as BERT and GPT are changing paradigms across various sectors.
Moreover, this Special Issue tackles the existing challenges in utilizing Transformer models, giving readers a well-rounded view of this field. It outlines potential future directions, providing a roadmap for continued innovation. This comprehensive guide offers invaluable insights to researchers, students, and practitioners interested in the cutting edge of deep learning technology.
Dr. Ting Xiao
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI
- NLP
- transformer
- self-attention
- ChatGPT
- BERT
- GPT
- deep learning
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Predictive Analysis of X Post Privatization Using ChatGPT and Deep Learning Techniques
Author: Kumar
Highlights: Developed a unique approach using LLMs and NLC for estimating the value of "X" post privatization.
Combined traditional financial valuation with news sentiment analysis for precision.
Utilized ChatGPT for creating custom transformers tailored to financial predictions.
Showcased deep learning's role in modernizing financial forecasting.
Title: Simulation of Epidemic Dynamics Using a Multi-Agent Model: Analysis of Social Distancing Strategies and Their Impacts on Public Health and Economy
Authors: Luis Augusto Silva
Affiliation: Department of Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain
Abstract: Infectious disease epidemics play critical roles in shaping public health outcomes. Observing these effects helps us understand the demographic, social, and cultural factors influencing the spread of diseases in various contexts. This research investigates how social variables affect epidemic predictions in socioeconomically vulnerable regions during the COVID-19 pandemic. Utilizing an adapted compartmental epidemiological model and a synthetic multi-agent community, this study examines the impact of social distancing strategies on public health and the economy. By simulating epidemic dynamics in the socioeconomically disadvantaged neighbourhood of Joana Bezerra Island in Recife, we analyze the effectiveness of face mask usage and moderate social isolation. The adapted SEPAI3R3O model, which includes additional compartments for pre-symptomatic and asymptomatic states, provides a detailed view of disease dynamics. Results highlight the need for tailored public health policies considering socioeconomic factors to manage epidemics in vulnerable communities effectively.
