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Applied and Innovative Computational Intelligence Systems ‖
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Applied and Innovative Computational Intelligence Systems ‖

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

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 12946

Special Issue Editors

Prof. Dr. Pedro J. S. Cardoso

E-Mail Website
Guest Editor
NOVA LINCS and Instituto Superior de Engenharia (ISE), Universidade do Algarve , 8005-139 Faro, Portugal
Interests: operational research; machine learning; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João M. F. Rodrigues

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
Prof. Dr. Cristina Portales

E-Mail Website
Guest Editor
Institute of Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Av. de Blasco Ibáñez, 13, 46010 València, Spain
Interests: computer vision; HCI; augmented reality; multispectral imaging; 3D reconstruction; multimodal data acquisition and rendering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on ‘Applied and Innovative Computational Intelligence Systems’ provides a place where Computational Intelligence (CI) researchers and practitioners can publish their theoretical and experimental outcomes in a journal with an Impact Factor of 2.679 and CiteScore of 3.4 in 2021 (updated on November 4th). Supported in huge pillars (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 complementary application 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, Sentiment Analysis, Natural Language Processing, Privacy and Ethics, Operational Research, Evolutionary Computation, Fuzzy Logic, Robotics, etc.

Accepted papers will build a comprehensive collection of research and development trends on 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, which increases the authors’ and works’ visibility.

Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. João M. F. Rodrigues
Prof. Dr. Cristina Portalés Ricart
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
  • sentiment analysis
  • natural language processing
  • privacy and ethics
  • operational research
  • evolutionary computation
  • robotics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

11 pages, 1282 KiB  
Article
Automated Think-Aloud Protocol for Identifying Students with Reading Comprehension Impairment Using Sentence Embedding
by Yongseok Yoo
Appl. Sci. 2024, 14(2), 858; https://doi.org/10.3390/app14020858 - 19 Jan 2024
Viewed by 1192
Abstract
The think-aloud protocol is a valuable tool for investigating readers’ cognitive processes during reading. However, its reliance on experienced human evaluators poses challenges in terms of efficiency and scalability. To address this limitation, this study proposes a novel application of natural language processing [...] Read more.
The think-aloud protocol is a valuable tool for investigating readers’ cognitive processes during reading. However, its reliance on experienced human evaluators poses challenges in terms of efficiency and scalability. To address this limitation, this study proposes a novel application of natural language processing to automate the think-aloud protocol. Specifically, we use a sentence embedding technique to encode the stimulus text and corresponding readers’ responses into high-dimensional vectors, and the similarity between these embeddings serves as a feature. The properties of the feature are investigated for word-frequency-based and contextualized embedding models. Differences in the sentence embedding-based feature between poor comprehenders and normal readers are investigated. Using these features, seven machine learning models were trained to classify readers into normal and abnormal groups. The highest F1 score of 0.74 was achieved with the contextualized embedding and random forest classifier. This highlights the effectiveness of the embedding technique in extracting useful features for automating the think-aloud protocol for assessing reading comprehension abilities. The potential benefits of this automated approach include increased efficiency and scalability, ultimately facilitating the early diagnosis of reading comprehension impairment and individualized interventions. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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22 pages, 14810 KiB  
Article
A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning
by Dawei Tong, Haifeng Wu, Changxin Liu, Zhangchao Guo and Pei Li
Appl. Sci. 2023, 13(16), 9135; https://doi.org/10.3390/app13169135 - 10 Aug 2023
Cited by 1 | Viewed by 1202
Abstract
Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing [...] Read more.
Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing studies use numerical simulations and model experiments to analyze the local resistance characteristics of specific structures and calculate the local resistance coefficient under specific ventilation conditions. Therefore, there are shortcomings of low efficiency and high cost in the ventilation scheme optimization when considering the influence of the local resistance. This paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning. The hybrid prediction model introduces the hybrid kernel into a relevance vector machine to build the hybrid kernel relevance vector machine model (HKRVM). The improved artificial jellyfish search algorithm (IAJS), which utilizes Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies to improve the algorithm performance, is applied to the kernel parameter optimization of the HKRVM model. The results of a case study show that the method proposed in this paper can achieve the efficient and accurate prediction of the local resistance coefficient of maintenance ventilation and improve the prediction accuracy and prediction efficiency to a certain extent. The method proposed in this paper provides a new concept for the prediction of the ventilation local resistance coefficient and can further provide an efficient prediction method for the design and optimization of maintenance ventilation schemes. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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19 pages, 3071 KiB  
Article
An Enhanced Feature Extraction Network for Medical Image Segmentation
by Yan Gao, Xiangjiu Che, Huan Xu and Mei Bie
Appl. Sci. 2023, 13(12), 6977; https://doi.org/10.3390/app13126977 - 9 Jun 2023
Cited by 5 | Viewed by 3532
Abstract
The major challenges for medical image segmentation tasks are complex backgrounds and fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation tasks, we propose an enhanced feature extraction network (EFEN), which is based on U-Net. Our network is designed [...] Read more.
The major challenges for medical image segmentation tasks are complex backgrounds and fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation tasks, we propose an enhanced feature extraction network (EFEN), which is based on U-Net. Our network is designed with the structure of feature re-extraction to strengthen the feature extraction ability. In the process of decoding, we use improved skip-connection, which includes positional encoding and a cross-attention mechanism. By embedding positional information, absolute information and relative information between organs can be captured. Meanwhile, useful information will be strengthened and useless information will be weakened by using the cross-attention mechanism. Our network can finely identify the features of each skip-connection and cause the features in the process of decoding to have less noise in order to reduce the effect of fuzzy object boundaries in medical images. Experiments on the CVC-ClinicDB, the task1 from ISIC-2018, and the 2018 Data Science Bowl challenge dataset demonstrate that EFEN outperforms U-Net and some recent networks. For example, our method obtains 5.23% and 2.46% DSC improvements compared to U-Net on CVC-ClinicDB and ISIC-2018, respectively. Compared with recent works, such as DoubleU-Net, we obtain 0.65% and 0.3% DSC improvements on CVC-ClinicDB and ISIC-2018, respectively. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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17 pages, 4937 KiB  
Article
DA-FER: Domain Adaptive Facial Expression Recognition
by Mei Bie, Huan Xu, Quanle Liu, Yan Gao, Kai Song and Xiangjiu Che
Appl. Sci. 2023, 13(10), 6314; https://doi.org/10.3390/app13106314 - 22 May 2023
Viewed by 1458
Abstract
Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address [...] Read more.
Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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16 pages, 1576 KiB  
Article
A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks
by Stanisław Kozdrowski, Piotr Paziewski, Paweł Cichosz and Sławomir Sujecki
Appl. Sci. 2023, 13(8), 4657; https://doi.org/10.3390/app13084657 - 7 Apr 2023
Viewed by 1840
Abstract
This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of [...] Read more.
This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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22 pages, 531 KiB  
Article
Evolutionary Features for Dynamic Link Prediction in Social Networks
by Nazim Choudhury and Shahadat Uddin
Appl. Sci. 2023, 13(5), 2913; https://doi.org/10.3390/app13052913 - 24 Feb 2023
Cited by 1 | Viewed by 1708
Abstract
One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal [...] Read more.
One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems ‖)
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