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Applied and Innovative Computational Intelligence Systems: 3rd Edition

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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 December 2024 | Viewed by 8757

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Special Issue Editors

Prof. Dr. João M. F. Rodrigues

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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. Pedro J. S. Cardoso

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NOVA LINCS and Instituto Superior de Engenharia (ISE), Universidade do Algarve , 8005-139 Faro, Portugal
Interests: operational research; machine learning; data science
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Prof. Dr. Cristina Portales

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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 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

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Published Papers (10 papers)

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Research

14 pages, 4693 KiB

Open AccessArticle

Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification

by Marcos Gabriel Mendes Lauande, Geraldo Braz Junior, João Dallyson Sousa de Almeida, Aristófanes Corrêa Silva, Rui Miguel Gil da Costa, Amanda Mara Teles, Leandro Lima da Silva, Haissa Oliveira Brito, Flávia Castello Branco Vidal, João Guilherme Araújo do Vale, José Ribamar Durand Rodrigues Junior and António Cunha

Appl. Sci. 2024, 14(22), 10536; https://doi.org/10.3390/app142210536 - 15 Nov 2024

Viewed by 363

Abstract

Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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12 pages, 2630 KiB

Open AccessArticle

Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language

by Lanlan Jiang, Xingguo Qin, Jingwei Zhang and Jun Li

Appl. Sci. 2024, 14(20), 9533; https://doi.org/10.3390/app14209533 - 18 Oct 2024

Viewed by 532

Abstract

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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19 pages, 2290 KiB

Open AccessArticle

How Gastronomic Image Shapes Tourism Competitiveness: An Approach with Sentiment Analysis

by Célia M. Q. Ramos and Karina Pinto

Appl. Sci. 2024, 14(20), 9524; https://doi.org/10.3390/app14209524 - 18 Oct 2024

Viewed by 831

Abstract

The competitiveness of tourist destinations is influenced by their relative attractiveness, which will play an essential role in their global success; gastronomy is one of the main motivations that lead tourists to visit a given destination. This research aims to investigate gastronomy’s role in the destination’s competitiveness and image through the analysis of online reputation, both in terms of ratings and sentiments provided by the experience, through the creation of an index of the online reputation of gastronomic image. Online restaurant reviews retrieved from TripAdvisor, from restaurants belonging to eight tourism destination regions, considered the competitive set to the Algarve, are analysed by applying sentiment analysis algorithms. With regard to the Algarve region, it was concluded that the Costa del Sol and the Tropical coast were the most competitive regions in terms of gastronomic image, where the inclusion of seafood products in meals was one of the strategic aspects used to increase the competitiveness of this region. These results can help restaurant managers and destination management organisations to better understand the different customer needs and how to increase their competitiveness. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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33 pages, 14086 KiB

Open AccessArticle

Energy-Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing

by Rongfei Li and Francis F. Assadian

Appl. Sci. 2024, 14(19), 9140; https://doi.org/10.3390/app14199140 - 9 Oct 2024

Viewed by 588

Abstract

Visual servoing technology is well developed and applied in many automated manufacturing tasks, especially in tools’ pose alignment. To access a full global view of tools, most applications adopt an eye-to-hand configuration or an eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation architectures in various scenarios, but few have discussed the importance of the camera’s location in the eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot in different locations. In this paper, we propose an algorithm for the camera’s moving policy so that it explores the camera workspace and searches for the optimal location where the image’s noise level is minimized. Also, this algorithm ensures the camera ends up at a suboptimal (if the optimal one is unreachable) location among the locations already searched with the limited energy available for moving the camera. Unlike a simple brute-force approach, the algorithm enables the camera to explore space more efficiently by adapting the search policy by learning the environment. With the aid of an image-averaging technique, this algorithm, in the use of a solo camera, achieves observation accuracy in eye-to-hand configurations to a desirable extent without filtering out high-frequency information in the original image. An automated manufacturing application was simulated, and the results show the success of this algorithm’s improvement in observation precision with limited energy. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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18 pages, 2215 KiB

Open AccessArticle

Simultaneous Instance and Attribute Selection for Noise Filtering

by Yenny Villuendas-Rey, Claudia C. Tusell-Rey and Oscar Camacho-Nieto

Appl. Sci. 2024, 14(18), 8459; https://doi.org/10.3390/app14188459 - 19 Sep 2024

Viewed by 548

Abstract

The existence of noise is inherent to most real data that are collected. Removing or reducing noise can help classification algorithms focus on relevant patterns, preventing them from being affected by irrelevant or incorrect information. This can result in more accurate and reliable models, improving their ability to generalize and make accurate predictions on new data. For example, among the main disadvantages of the nearest neighbor classifier are its noise sensitivity and its high computational cost (for classification and storage). Thus, noise filtering is essential to ensure data quality and the effectiveness of supervised classification models. The simultaneous selection of attributes and instances for supervised classifiers was introduced in the last decade. However, the proposed solutions present several drawbacks because some are either stochastic or do not handle noisy domains, and the neighborhood selection of some algorithms allows very dissimilar objects to be considered as neighbors. In addition, the design of some methods is just for specific classifiers without generalization possibilities. This article introduces an instance and attribute selection model, which seeks to detect and eliminate existing noise while reducing the feature space. In addition, the proposal is deterministic and does not predefine any supervised classifier. The experiments allow us to establish the viability of the proposal and its effectiveness in eliminating noise. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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20 pages, 3800 KiB

Open AccessArticle

Machine Learning Framework for Classifying and Predicting Depressive Behavior Based on PPG and ECG Feature Extraction

by Mateo Alzate, Robinson Torres, José De la Roca, Andres Quintero-Zea and Martha Hernandez

Appl. Sci. 2024, 14(18), 8312; https://doi.org/10.3390/app14188312 - 15 Sep 2024

Viewed by 1118

Abstract

Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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19 pages, 1160 KiB

Open AccessArticle

Enhancing Explainable Recommendations: Integrating Reason Generation and Rating Prediction through Multi-Task Learning

by Xingyu Zhu, Xiaona Xia, Yuheng Wu and Wenxu Zhao

Appl. Sci. 2024, 14(18), 8303; https://doi.org/10.3390/app14188303 - 14 Sep 2024

Viewed by 1023

Abstract

In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these “black-box” systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users’ trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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15 pages, 4564 KiB

Open AccessArticle

Embedding a Real-Time Strawberry Detection Model into a Pesticide-Spraying Mobile Robot for Greenhouse Operation

by Khalid El Amraoui, Mohamed El Ansari, Mouataz Lghoul, Mustapha El Alaoui, Abdelkrim Abanay, Bouazza Jabri, Lhoussaine Masmoudi and José Valente de Oliveira

Appl. Sci. 2024, 14(16), 7195; https://doi.org/10.3390/app14167195 - 15 Aug 2024

Viewed by 845

Abstract

The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 × 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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13 pages, 2479 KiB

Open AccessArticle

Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts

by Xingguo Qin, Ya Zhou and Jun Li

Appl. Sci. 2024, 14(12), 5146; https://doi.org/10.3390/app14125146 - 13 Jun 2024

Viewed by 843

Abstract

Online education review data have strong statistical and predictive power but lack efficient and accurate analysis methods. In this paper, we propose a multi-modal emotion analysis method to analyze the online education of college students based on educational data. Specifically, we design a multi-modal emotion analysis method that combines text and emoji data, using pre-training emotional prompt learning to enhance the sentiment polarity. We also analyze whether this fusion model reflects the true emotional polarity. The conducted experiments show that our multi-modal emotion analysis method achieves good performance on several datasets, and multi-modal emotional prompt methods can more accurately reflect emotional expressions in online education data. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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11 pages, 840 KiB

Open AccessArticle

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 719

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 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

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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