Applied and Innovative Computational Intelligence Systems: 3rd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 411

Special Issue Editors


E-Mail Website
Guest Editor
NOVA LINCS and Instituto Superior de Engenharia (ISE) , University of the Algarve, 8005-139 Faro, Portugal
Interests: computer vision; human–computer interaction; human–machine cooperation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

This Special Issue on ‘Applied and Innovative Computational Intelligence Systems’ provides an opportunity for computational intelligence (CI) researchers and practitioners to publish their theoretical and experimental outcomes in a journal with an Impact Factor of 2.7 and a CiteScore of 4.5 for 2022.  (updated on November 4th). Supported in a number of ways, (such as neural networks, fuzzy systems or evolutionary computation), CI practitioners seek an intelligent system that is characterized by computational adaptability, fault tolerance and high performance in the form of adaptive platforms that enable or facilitate intelligent behavior in complex and dynamic environments, developing technology that enables machines to think, behave or act more humanely.

In this context, this Special Issue intends to explore CI and its complementary applications and theory fields including, but not restricted to, artificial intelligence in general, machine learning, deep learning, computer vision, augmented reality, human–computer interaction, smart spaces, smart cities, ubiquitous intelligence, data analysis and science, time-series, internet of things/everything, fault detection, affective computing, natural language processing, privacy and ethics, operational research, evolutionary computation, fuzzy logic, robotics, etc.

Accepted papers will be those that include a comprehensive collection of research and development trends in contemporary applied and innovative computational intelligence systems that will serve as a convenient reference for other CI experts as well as newly arrived practitioners, introducing them to the field’s trends. Following the journal’s policy, there is no limit on the documents’ length, and full experimental details should be provided, allowing other researchers to reproduce results. Furthermore, electronic files and software can be deposited as supplementary electronic material, allowing full reproducibility and future analysis, and thus increasing the authors’ and works’ visibility.

We look forward to working with you,

Prof. Dr. João M. F. Rodrigues
Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. Cristina Portales
Guest Editors

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

  • artificial intelligence
  • machine learning
  • deep learning
  • computer vision
  • augmented reality
  • human–computer interaction
  • smart spaces
  • smart cities
  • ubiquitous intelligence
  • data analysis and science
  • time-series
  • Internet of Things/everything
  • fault detection
  • affective computing
  • natural language processing
  • privacy and ethics
  • operational research
  • evolutionary computation
  • robotics

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

11 pages, 840 KiB  
Article
Automated Assessment of Inferences Using Pre-Trained Language Models
by Yongseok Yoo
Appl. Sci. 2024, 14(9), 3657; https://doi.org/10.3390/app14093657 - 25 Apr 2024
Viewed by 175
Abstract
Inference plays a key role in reading comprehension. However, assessing inference in reading is a complex process that relies on the judgment of trained experts. In this study, we explore objective and automated methods for assessing inference in readers’ responses using natural language [...] Read more.
Inference plays a key role in reading comprehension. However, assessing inference in reading is a complex process that relies on the judgment of trained experts. In this study, we explore objective and automated methods for assessing inference in readers’ responses using natural language processing. Specifically, classifiers were trained to detect inference from a pair of input texts and reader responses by fine-tuning three widely used pre-trained language models. The effects of the model size and pre-training strategy on the accuracy of inference classification were investigated. The highest F1 score of 0.92 was achieved via fine-tuning the robustly optimized 12-layer BERT model (RoBERTa-base). Fine-tuning the larger 24-layer model (RoBERTa-large) did not improve the classification accuracy. Error analysis provides insight into the relative difficulty of classifying inference subtypes. The proposed method demonstrates the feasibility of the automated quantification of inference during reading, and offers potential to facilitate individualized reading instructions. Full article
Show Figures

Figure 1

Back to TopTop