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Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 17971

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Dr. Grigoreta-Sofia Cojocar

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Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: formal methods; computational intelligence; software engineering; programming paradigms
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Dr. Adriana-Mihaela Guran

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Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: human computer interaction; graphical user interfaces; software engineering; web technologies
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Dr. Laura-Silvia Dioşan

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Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: machine learning; evolutionary computation; image processing; optimization algorithms

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) is an evolving field that involves various nature-inspired computational paradigms and methodologies developed to solve complex real-world problems, including neural networks, fuzzy systems, evolutionary computation, learning theory, probabilistic methods, etc. CI plays a very important role in both theoretical research and practical applications.

As our lives become increasingly intelligent and digitalized, the use of different devices across a wide age spectrum is ever more prevalent. Human–computer interaction (HCI) is an interdisciplinary field at the intersection of mathematical logic, computer science, engineering, design, and human factors, among others. Human–computer interaction plays an important role in many fields, including mobile and ubiquitous computing, social media and collaborative technologies, etc. It focuses on subjects such as:

Adapting human–computer interaction such that it is intuitive and friendly for different categories of people, considering user preferences and empathic interaction.
Integrating the progress from computational intelligence into the HCI domain and tailoring user experiences.
Assessing the subjective satisfaction of users to obtain relevant information on user experience.
Identifying users’ emotions during the interaction and establishing relations with the interaction context to provide insights into design flaws and possible improvements.
Improving young children’s interaction with technology by supporting the authentication process, emotion identification, and interaction adaptation based on the identified emotions.
Providing recommendations on designing smart learning environments that would support remote learning (e.g., the COVID-19 pandemic revealed multiple drawbacks in the existing solutions) .

In this context, this Special Issue focuses on current advances in computational intelligence supporting the interaction between humans and computers as well as the design and assessment of adapted HCI. We welcome submissions considering new algorithms, paradigms, technologies, methodologies, and approaches applied to analyzing human characteristics and modeling interactions between human and computers, including voice and speech processing, face and expression recognition, medical image reconstruction, gesture and motion recognition, affective computing, computer-aided detection and diagnosis, computer-supported learning, brain–computer interface, adaptive systems, etc.

This Special Issue provides a platform for researchers from both academia and industry to present their novel and unpublished work in the domains of computational intelligence and human–computer interaction. This will help to foster future research in the emerging field of human–computer interaction and related fields.

Dr. Grigoreta-Sofia Cojocar
Dr. Adriana-Mihaela Guran
Dr. Laura-Silvia Dioşan
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. Mathematics 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 2600 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

Computational intelligence
Machine translation
Text processing
Speech processing
Video processing
Visual sense
Face recognition
Fingerprint recognition
Posture recognition
Adaptive systems
Computer vision
Neural networks
Machine learning
Deep learning
Semantic analysis
Natural language processing
Approximated reasoning
Interactive reasoning
User–system interfaces
Affective computing
User experience evaluation
Agent-based systems
Adaptive systems
Intelligent UIs and agents
Multicontext usability
HCI tools, techniques, and methodologies
Computer-supported learning and assessment
Computer-aided detection and diagnosis

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Published Papers (10 papers)

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Research

21 pages, 3768 KiB

Open AccessArticle

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions

by Laila Alshehri and Muhammad Hussain

Mathematics 2024, 12(20), 3286; https://doi.org/10.3390/math12203286 - 20 Oct 2024

Viewed by 656

Abstract

A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. Additionally, their ability to generalize across a large number of subjects remains unclear. Moreover, they have been validated on datasets collected in controlled environments, which do not accurately reflect real-world scenarios involving diverse brain conditions. To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. First, a raw EEG signal is decomposed into frequency bands and then passed to GCT–EEGNet for feature extraction, which utilizes a gated channel transformation (GCT) layer to selectively emphasize informative features from the relevant frequency bands. The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. The experimental results demonstrated that the method achieved a correct recognition rate (CRR) of 99.23% and an equal error rate (EER) of 0.0014, corroborating its robustness against different brain conditions. The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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21 pages, 15462 KiB

Open AccessArticle

An Empirical Investigation on the Visual Imagery of Augmented Reality User Interfaces for Smart Electric Vehicles Based on Kansei Engineering and FAHP-GRA

by Jin-Long Lin and Meng-Cong Zheng

Mathematics 2024, 12(17), 2712; https://doi.org/10.3390/math12172712 - 30 Aug 2024

Viewed by 752

Abstract

Smart electric vehicles (SEVs) hold significant potential in alleviating the energy crisis and environmental pollution. The augmented reality (AR) dashboard, a key feature of SEVs, is attracting considerable attention due to its ability to enhance driving safety and user experience through real-time, intuitive driving information. This study innovatively integrates Kansei engineering, factor analysis, fuzzy systems theory, analytic hierarchy process, grey relational analysis, and factorial experimentation to evaluate AR dashboards’ visual imagery and subjective preferences. The findings reveal that designs featuring blue planar and unconventional-shaped dials exhibit the best performance in terms of visual imagery. Subsequent factorial experiments confirmed these results, showing that drivers most favor blue-dominant designs. Furthermore, in unconventional-shaped dial designs, the visual effect of vertical 3D is more popular with drivers than horizontal 3D, while the opposite is true in round dials. This study provides a scientific evaluation method for assessing the emotional experience of AR dashboard interfaces. Additionally, these findings will help reduce the subjectivity in interface design and enhance the overall competitiveness of SEV vehicles. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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37 pages, 656 KiB

Open AccessArticle

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

by Anamaria Briciu, Alina-Delia Călin, Diana-Lucia Miholca, Cristiana Moroz-Dubenco, Vladiela Petrașcu and George Dascălu

Mathematics 2024, 12(3), 456; https://doi.org/10.3390/math12030456 - 31 Jan 2024

Cited by 2 | Viewed by 2144

Abstract

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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22 pages, 3558 KiB

Open AccessArticle

Spectral Salt-and-Pepper Patch Masking for Self-Supervised Speech Representation Learning

by June-Woo Kim, Hoon Chung and Ho-Young Jung

Mathematics 2023, 11(15), 3418; https://doi.org/10.3390/math11153418 - 5 Aug 2023

Viewed by 1734

Abstract

Recent advanced systems in the speech recognition domain use large Transformer neural networks that have been pretrained on massive speech data. General methods in the deep learning area have been frequently shared across various domains, and the Transformer model can also be used effectively across speech and image. In this paper, we introduce a novel masking method for self-supervised speech representation learning with salt-and-pepper (S&P) mask which is commonly used in computer vision. The proposed scheme includes consecutive quadrilateral-shaped S&P patches randomly contaminating the input speech spectrum. Furthermore, we modify the standard S&P mask to make it appropriate for the speech domain. In order to validate the effect of the proposed spectral S&P patch masking for the self-supervised representation learning approach, we conduct the pretraining and downstream experiments with two languages, English and Korean. To this end, we pretrain the speech representation model using each dataset and evaluate the pretrained models for feature extraction and fine-tuning performance on varying downstream tasks, respectively. The experimental outcomes clearly illustrate that the proposed spectral S&P patch masking is effective for various downstream tasks when combined with the conventional masking methods. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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9 pages, 2658 KiB

Open AccessArticle

Deep Neural Network-Based Simulation of Sel’kov Model in Glycolysis: A Comprehensive Analysis

by Jamshaid Ul Rahman, Sana Danish and Dianchen Lu

Mathematics 2023, 11(14), 3216; https://doi.org/10.3390/math11143216 - 21 Jul 2023

Cited by 5 | Viewed by 1294

Abstract

The Sel’kov model for glycolysis is a highly effective tool in capturing the complex feedback mechanisms that occur within a biochemical system. However, accurately predicting the behavior of this system is challenging due to its nonlinearity, stiffness, and parameter sensitivity. In this paper, we present a novel deep neural network-based method to simulate the Sel’kov glycolysis model of ADP and F6P, which overcomes the limitations of conventional numerical methods. Our comprehensive results demonstrate that the proposed approach outperforms traditional methods and offers greater reliability for nonlinear dynamics. By adopting this flexible and robust technique, researchers can gain deeper insights into the complex interactions that drive biochemical systems. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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26 pages, 2336 KiB

Open AccessArticle

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

by Sandi Ljubic and Alen Salkanovic

Mathematics 2023, 11(11), 2550; https://doi.org/10.3390/math11112550 - 1 Jun 2023

Viewed by 1529

Abstract

In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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19 pages, 626 KiB

Open AccessArticle

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

by Md. Humaun Kabir, Shabbir Mahmood, Abdullah Al Shiam, Abu Saleh Musa Miah, Jungpil Shin and Md. Khademul Islam Molla

Mathematics 2023, 11(8), 1921; https://doi.org/10.3390/math11081921 - 19 Apr 2023

Cited by 18 | Viewed by 3034

Abstract

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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15 pages, 6527 KiB

Open AccessArticle

BroadBand-Adaptive VMD with Flattest Response

by Xizhong Shen and Ran Li

Mathematics 2023, 11(8), 1858; https://doi.org/10.3390/math11081858 - 13 Apr 2023

Cited by 1 | Viewed by 1294

Abstract

A mixed signal with several unknown modes is common in the industry and is hard to decompose. Variational Mode Decomposition (VMD) was proposed to decompose a signal into several amplitude-modulated modes in 2014, which overcame the limitations of Empirical Mode Decomposition (EMD), such as sensitivity to noise and sampling. We propose an improved VMD, which is simplified as iVMD. In the new algorithm, we further study and improve the mathematical model of VMD to adapt to the decomposition of the broad-band modes. In the new model, the ideal flattest response is applied, which is derived from the mathematical integral form and obtained from different-order derivatives of the improved modes’ definitions. The harmonics can be treated via synthesis in our new model. The iVMD algorithm can decompose the complex harmonic signal and the broad-band modes. The new model is optimized with the alternate direction method of multipliers, and the modes with adaptive broad-band and their respective center frequencies can be decomposed. the experimental results show that iVMD is an effective algorithm based on the artificial and real data collected in our experiments. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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22 pages, 2107 KiB

Open AccessArticle

Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment

by Damjan Vlaj and Andrej Zgank

Mathematics 2023, 11(1), 169; https://doi.org/10.3390/math11010169 - 29 Dec 2022

Cited by 6 | Viewed by 2089

Abstract

The advanced smart home environment presents an important trend for the future of human wellbeing. One of the prerequisites for applying its rich functionality is the ability to differentiate between various user categories, such as gender, age, speakers, etc. We propose a model for an efficient acoustic gender and age classification system for human–computer interaction in a smart home. The objective was to improve acoustic classification without using high-complexity feature extraction. This was realized with pitch as an additional feature, combined with additional acoustic modeling approaches. In the first step, the classification is based on Gaussian mixture models. In the second step, two new procedures are introduced for gender and age classification. The first is based on the count of the frames with the speaker’s pitch values, and the second is based on the sum of the frames with pitch values belonging to a certain speaker. Since both procedures are based on pitch values, we have proposed a new, effective algorithm for pitch value calculation. In order to improve gender and age classification, we also incorporated speech segmentation with the proposed voice activity detection algorithm. We also propose a procedure that enables the quick adaptation of the classification algorithm to frequent smart home users. The proposed classification model with pitch values has improved the results in comparison with the baseline system. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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15 pages, 1330 KiB

Open AccessArticle

Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

by Hancheng Zhu, Yong Zhou, Zhiwen Shao, Wenliang Du, Guangcheng Wang and Qiaoyue Li

Mathematics 2022, 10(22), 4181; https://doi.org/10.3390/math10224181 - 9 Nov 2022

Cited by 4 | Viewed by 2231

Abstract

Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications, 2nd Edition)

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