Artificial Intelligence in Life Quality Technologies

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 (31 December 2023) | Viewed by 17426

Special Issue Editors

Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
Interests: fuzzy sets and systems; ordered fuzzy numbers; applications of Artificial Intelligence; linguistic modelling of data
Special Issues, Collections and Topics in MDPI journals
Efrei Research Lab, Efrei Paris Panthéon-Assas Université, 30-32 av. de la République, 94800 Villejuif, France
Interests: machine tearning, text mining, social network analysis (content analysis and group analysis of connections), web intelligence: decision support systems: recommendation systems, emotion analysis, data mining, e-health application, sensors based medical decisional system
Special Issues, Collections and Topics in MDPI journals
Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
Interests: computer networking; network security; cloud computing; network communication

Special Issue Information

Dear Colleagues,

In today's world it is difficult to talk about significant development and progress in any area without reference to artificial intelligence (AI) methods. They are particularly useful in optimization, data processing, image analysis, and data analysis (including imprecise data, or pattern search and classification). It is worth mentioning that a number of AI methods are also effectively used in the area of designing processes and procedures.

Lately, the spread of the use of advanced technological tools in the field of healthcare (e.g., within the eHealth paradigm) and industrial production (e.g., in light of the Industry 4.0 paradigm) has led to the generation of a large amount of related data.

This became especially important during the pandemic period. AI tools significantly accelerated the development of vaccines and medicines and enabled the more efficient management of patients and medical professionals. In the case of industrial manufacturing, AI significantly supports remote-work tools, digital transformation, automation and robotization wherever possible, supporting work in a labor shortage situation.

The main purpose of this Special Issue is to gather original publications on the latest applied research on intelligent data analysis, predictions, optimization and classification, as well as the design of algorithms and procedures. We are particularly interested in topics related to applications concerning improving the quality of life and solutions for improving health. These include a wide range of applications of AI methods in the fields of medicine, agriculture, additive manufacturing, sustainability, and ergonomics. This list does not exclude other areas.

The main AI methods covered by this Special Issue are as follows:

  • Methods based on the fuzzy sets concept in its various forms;
  • Solutions based on ANNs, especially deep neural network variants;
  • AI methods inspired by Nature (e.g., evolution-based algorithms or swarm intelligence).

The above list is not exhaustive. Papers on other AI methods applied in life-quality-related technologies are also welcome.

Prof. Dr. Piotr Prokopowicz
Prof. Dr. Katarzyna Węgrzyn-Wolska
Dr. Maciej Piechowiak
Guest Editors

Manuscript Submission Information

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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

  • applied artificial intelligence
  • AI in health
  • AI in engineering
  • intelligent analysis
  • intelligent systems
  • applied computational intelligence

Published Papers (11 papers)

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14 pages, 1697 KiB  
Article
LoRaWAN Metering Infrastructure Planning in Smart Cities
by Maciej Piechowiak, Piotr Zwierzykowski and Bartosz Musznicki
Appl. Sci. 2023, 13(14), 8431; https://doi.org/10.3390/app13148431 - 21 Jul 2023
Cited by 5 | Viewed by 1074
Abstract
The planning of metering network infrastructure based on the concept of the Internet of Things primarily involves the choice of available radio technology. Then, regardless of the type and availability of power sources, energy conservation should be one of the main optimization criteria. [...] Read more.
The planning of metering network infrastructure based on the concept of the Internet of Things primarily involves the choice of available radio technology. Then, regardless of the type and availability of power sources, energy conservation should be one of the main optimization criteria. For this reason, LPWANs operating in unlicensed ISM bands appear to be a suitable solution in urban environments due to their sub 1 GHz propagation properties. High signal penetration and coverage make them applicable in urban areas with buildings and various obstacles. Therefore, this article presents solutions developed to support the planning process of implementing a LoRaWAN network infrastructure aimed at monitoring and collecting electricity meter data in smart cities. To this end, an algorithm has been proposed to support the selection of the number of LoRaWAN gateways and their deployment, as well as the selection of transmission parameters at the measurement nodes with a particular focus on geographic data from real maps. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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13 pages, 1576 KiB  
Article
Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection
by Katarzyna Zemla, Grzegorz M. Wojcik, Filip Postepski, Krzysztof Wróbel, Andrzej Kawiak and Grzegorz Sedek
Appl. Sci. 2023, 13(7), 4472; https://doi.org/10.3390/app13074472 - 31 Mar 2023
Cited by 1 | Viewed by 1513
Abstract
Coronavirus disease 2019 (COVID-19) has caused everything from daily hassles, relationship issues, and work pressures to health concerns and debilitating phobias. Relaxation techniques are one example of the many methods used to address stress, and they have been investigated for decades. In this [...] Read more.
Coronavirus disease 2019 (COVID-19) has caused everything from daily hassles, relationship issues, and work pressures to health concerns and debilitating phobias. Relaxation techniques are one example of the many methods used to address stress, and they have been investigated for decades. In this study, we aimed to check whether there are differences in the brain cortical activity of participants during relaxation or mental workload tasks, as observed using dense array electroencephalography, and whether these differences can be modeled and then classified using a machine learning classifier. In this study, guided imagery as a relaxation technique was used in a randomized trial design. Two groups of thirty randomly selected participants underwent a guided imagery session; other randomly selected participants performed a mental task. Participants were recruited among male computer science students. During the guided imagery session, the electroencephalographic activity of each student’s brain was recorded using a dense array amplifier. This activity was compared with that of a group of another 30 computer science students who performed a mental task. Power activity maps were generated for each participant, and examples are presented and discussed to some extent. These types of maps cannot be easily interpreted by therapists due to their complexity and the fact that they vary over time. However, the recorded signal can be classified using general linear models. The classification results as well as a discussion of prospective applications are presented. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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15 pages, 891 KiB  
Article
Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies
by Łukasz Apiecionek, Rafał Moś and Dawid Ewald
Appl. Sci. 2023, 13(6), 3487; https://doi.org/10.3390/app13063487 - 09 Mar 2023
Cited by 1 | Viewed by 1483
Abstract
The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the [...] Read more.
The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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23 pages, 2486 KiB  
Article
Multi-Layer QoE Learning System Implemented by Fiberhost
by Paweł Andruloniw, Karol Kowalik, Bartosz Partyka and Piotr Zwierzykowski
Appl. Sci. 2023, 13(4), 2300; https://doi.org/10.3390/app13042300 - 10 Feb 2023
Cited by 2 | Viewed by 1024
Abstract
Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most [...] Read more.
Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most network mechanisms, protocols and applications feature adaptations that allow them to change the parameters of the transmission channel and try to minimize the negative impact of the network on the perceived quality, for example by temporarily changing the modulation scheme, or coding scheme, or by re-transmitting lost packets, or buffering to compensate for the interruptions in transmission. To respond appropriately, network operators are interested in knowing how well these adaptations are performing in order to assess the ultimate quality of their networks from the user perspective, i.e., Quality of Experience (QoE). Due to the huge amount of data associated with the collection of various parameters of the telecommunications network, machine learning methods are often needed to discover the relationships between various parameters and to identify the root cause of the observed network quality. In this paper, we present a Multi-layer QoE learning system implemented by Fiberhost for QoE analysis with a multi-layer approach based on machine learning tools. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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19 pages, 914 KiB  
Article
The Use of Artificial Intelligence for Assessing the Pro-Environmental Practices of Companies
by Ewa Dostatni, Dariusz Mikołajewski and Izabela Rojek
Appl. Sci. 2023, 13(1), 310; https://doi.org/10.3390/app13010310 - 27 Dec 2022
Cited by 1 | Viewed by 1654
Abstract
In the present study, the authors analyze, supported by the use of artificial intelligence, the environmental solutions implemented in selected manufacturing companies using the example of the Great Poland Voivodship. The companies analyzed were selected from different industry sectors and were of different [...] Read more.
In the present study, the authors analyze, supported by the use of artificial intelligence, the environmental solutions implemented in selected manufacturing companies using the example of the Great Poland Voivodship. The companies analyzed were selected from different industry sectors and were of different sizes, divided into two groups: small- and medium-sized enterprises (SMEs) and large enterprises (LEs). The authors observed the environmental activities of these two groups of companies, paying particular attention to the differences that were evident. The study is based on a questionnaire survey. All survey questions referred to the life cycle of a product, ranging from design, production, and use to recycling processes. We discuss the environmental solutions proposed by enterprises of different sizes and at different stages of the product’s life cycle. The goal of this study is three-fold: (1) To investigate the differences in the introduction of environmental issues in SMEs and LEs in the Greater Poland Voivodship, Poland; (2) to examine whether companies in this Voivodship are equally aware of the impact of their business activities and their products on the environment; and (3) to discover novel, more rapid, and simpler methods to analyze the environmental sustainability of companies, including efficient models based on artificial intelligence. An analysis based on ANNs (artificial neural networks) was performed. The novelty of the proposed approach lies in the use of a combination of research data and methods using artificial intelligent tools to develop and scalable conclusions. This approach is unique and has no equivalent in the literature. An analysis was conducted via two perspectives: (1) The level of environmental solutions implemented at successive stages of the product’s life cycle and (2) the size of the company. The results show significant differences between the environmental practices of small, medium, and large Polish enterprises, and reveal the emerging trends in enterprise operations, which will be subject to an AI-based analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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12 pages, 313 KiB  
Article
Post-Stroke Gait Classification Based on Feature Space Transformation and Data Labeling
by Robert Burduk, Izabela Rojek, Emilia Mikołajewska and Dariusz Mikołajewski
Appl. Sci. 2022, 12(22), 11346; https://doi.org/10.3390/app122211346 - 08 Nov 2022
Cited by 2 | Viewed by 936
Abstract
Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this [...] Read more.
Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this paper, we propose a transformation of the feature space and definition of class labels in the post-stroke gait problem to more efficiently study related phenomena and assess gait faster. Clustering is used to define two class labels (improvement and recurrence) in the data labeling process. The proposed approach was tested on a real-world dataset consisting of 50 patients (male and female, aged 49–82 years) after ischemic stroke who participated in a gait rehabilitation program. Gait in the study was described using speed, cadence, and stride length and their normalized values. Ten treatment sessions (10 therapy days) were conducted over two weeks (10 working days). The same specialist took measurements, and hence inter-rater reliability can be neglected. Machine learning methods, support vector machine and quadratic discriminant analysis were used to classify post-stroke gait for three cases with different class labels. The proposed novel approach, characterized by its speed of execution and accuracy of classification, may be helpful for screening, better targeting, and rehabilitation monitoring. The proposed approach minimizes clinical testing and supports the work of physicians, physiotherapists, and diagnosticians. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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17 pages, 2698 KiB  
Article
AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine
by Izabela Rojek, Mirosław Kozielski, Janusz Dorożyński and Dariusz Mikołajewski
Appl. Sci. 2022, 12(19), 9596; https://doi.org/10.3390/app12199596 - 24 Sep 2022
Cited by 4 | Viewed by 2184
Abstract
The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The [...] Read more.
The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The aim of the project was to develop a relatively simple artificial-intelligence tool to assess the likelihood of a heart infarction for preventive medicine purposes. We used binary classification to determine from a wide variety of patient characteristics the likelihood of heart disease and, from a computational point of view, determine what the minimum set of characteristics permits. Factors with the highest positive influence were: cp, restecg and slope, whilst factors with the highest negative influence were sex, exang, oldpeak, ca, and thal. The novelty of the described system lies in the development of the AI for predictive analysis of cardiovascular function, and its future use in a specific patient is the beginning of a new phase in this field of research with a great opportunity to improve pre-clinical care and diagnosis, and accuracy of prediction in preventive medicine. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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25 pages, 1449 KiB  
Article
Ecological Design with the Use of Selected Inventive Methods including AI-Based
by Ewa Dostatni, Dariusz Mikołajewski, Janusz Dorożyński and Izabela Rojek
Appl. Sci. 2022, 12(19), 9577; https://doi.org/10.3390/app12199577 - 23 Sep 2022
Cited by 4 | Viewed by 1639
Abstract
Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of [...] Read more.
Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of research. The article deals with issues relating to eco-design, eco-innovation, and inventory. The process of selecting inventive methods was presented. Selected inventive methods used to develop the product concept were briefly characterized. Creativity sessions were conducted using the methods of brainstorming, stimulating, reverse brainstorming, word games, and superpositions. The effect of these activities is the concept for an eco-innovative product. A product design was developed that is highly recyclable and environmentally friendly. An ecological analysis of the designed product, including AI-based (artificial neural networks), was carried out, which showed the legitimacy of the actions taken to develop an environmentally friendly product. The novelty of the proposed approach consists of combining the use of research data, with new methods for their analysis using both traditional and artificial intelligent tools, to create a transparent and scalable product design. To date, this approach is unique and has no equivalent in the literature. Despite higher manufacturing costs, the more environmentally friendly refrigerator is cheaper in operation (consumes less energy) due to the ecological solutions incorporated into its design. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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23 pages, 36719 KiB  
Article
Unsupervised Learning Data-Driven Continuous QoE Assessment in Adaptive Streaming-Based Television System
by Paweł Andruloniw, Karol Kowalik and Piotr Zwierzykowski
Appl. Sci. 2022, 12(16), 8288; https://doi.org/10.3390/app12168288 - 19 Aug 2022
Cited by 2 | Viewed by 1237
Abstract
The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played [...] Read more.
The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played by the STB device connected to the production TV system. The evaluation has been based on the stalling effects, video quality changes, and the time related to the last decreased bitrate change occurrence. The two-phase continuous clustering approach has been studied to assess the QoE level based on the ACR scale. The number of devices with grades 1 or 2 is relatively low, but those devices generate significantly more events than adequately functioning devices. STBs try to play the highest possible bitrate, and there is no possibility of setting the intermediate bitrate level. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. Hence the bitrate fluctuations that can annoy customers appear for the lowest grades. The boundary cases can be easily assessed. The outcome should be challenged by the customers’ opinions to find the proper QoE threshold. Continuous clustering may allow telecom operators to assess customer satisfaction with their TV service. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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22 pages, 1322 KiB  
Article
Effect of COVID-19 on Selected Characteristics of Life Satisfaction Reflected in a Fuzzy Model
by Dariusz Mikołajewski and Piotr Prokopowicz
Appl. Sci. 2022, 12(15), 7376; https://doi.org/10.3390/app12157376 - 22 Jul 2022
Cited by 6 | Viewed by 1381
Abstract
The general goal of the research in this article is to devise an algorithm for assessing overall life satisfaction—a term often referred to as Quality of Life (QoL). It is aggregated to its own proposition, called personal life usual satisfaction (PLUS). An important [...] Read more.
The general goal of the research in this article is to devise an algorithm for assessing overall life satisfaction—a term often referred to as Quality of Life (QoL). It is aggregated to its own proposition, called personal life usual satisfaction (PLUS). An important assumption here is that the model is based on already known and commonly used solutions, such as medical (psychological and physiotherapeutic) questionnaires. Thanks to this, the developed solution allows us to obtain a synergy effect from the existing knowledge, without the need to design new, complicated procedures. Fuzzy multivariate characterization of life satisfaction presents a challenge for a complete analysis of the phenomenon. The complexity of description using multiple scales, including linguistic, requires additional computational solutions, as presented in this paper. The detailed aim of this study is twofold: (1) to develop a fuzzy model reflecting changes in life satisfaction test scores as influenced by the corona virus disease 2019 (COVID-19) pandemic, and (2) to develop guidelines for further research on more advanced models that are clinically useful. Two groups affected by professional burnout to different degrees were analyzed toward life satisfaction twice (pre- and during pandemy): a study group (physiotherapists, n = 25) and a reference group (computer scientists, n = 25). The Perceived Stress Score (PSS10), Maslach Burnout Inventory (MBI), Satisfaction with Life Scale (SWLS), and Nordic Musculoskeletal Questionnaire (NMQ) were used. The resultant model is based on a hierarchical fuzzy system. The novelty of the proposed approach lies in the combination of the use of data from validated clinimetric tests with the collection of data from characteristic time points and the way in which they are analyzed using fuzzy logic through transparent and scalable hierarchical models. To date, this approach is unique and has no equivalent in the literature. Thanks to the hierarchical structure, the evaluation process can be defined as a modular construction, which increases transparency and makes the whole procedure more flexible. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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20 pages, 1374 KiB  
Perspective
From Neuroimaging to Computational Modeling of Burnout: The Traditional versus the Fuzzy Approach—A Review
by Emilia Mikołajewska, Piotr Prokopowicz, YeeKong Chow, Jolanta Masiak, Dariusz Mikołajewski, Grzegorz Marcin Wójcik, Brian Wallace, Andy R. Eugene and Marcin Olajossy
Appl. Sci. 2022, 12(22), 11524; https://doi.org/10.3390/app122211524 - 13 Nov 2022
Cited by 1 | Viewed by 1937
Abstract
Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that [...] Read more.
Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that have used medical neuroimaging techniques to provide evidence or links regarding changes in brain function in occupational burnout syndrome from a neuroscientific perspective, and then use these to propose a fuzzy-based computational model of burnout.A comprehensive literature search was conducted in two major databases (PubMed and Medline Complete). The search period was 2006–2021, and searches were limited to the English language. Each article was carefully reviewed and appropriately selected on the basis of raw data, validity of methods used, clarity of results, and scales for measuring burnout. The results showed that the brain structures of patients with job burnout that are associated with emotion, motivation, and empathy weresignificantly different from healthy controls. These altered brain regions included the thalamus, hippocampus, amygdala, caudate, striatum, dorso-lateral prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, anterior insula, inferior frontal cingulate cortex, middle frontal cingulate cortex, temporoparietal junction, and grey matter. Deepening our understanding of how these brain structures are related to burnout will pave the way for better approaches fordiagnosis and intervention. As an alternative to the neuroimaging approach, the paper presents a late proposal of the PLUS (personal living usual satisfaction) parameter. It is based on a fuzzy model, wherein the data source is psychological factors—the same or similar to the neuroimaging approach. As the novel approach to searching for neural burnout mechanisms, we have shown that computational models, including those based on fuzzy logic and artificial neural networks, can play an important role in inferring and predicting burnout. Effective computational models of burnout are possible but need further development to ensure accuracy across different populations. There is also a need to identify mechanisms and clinical indicators of chronic fatigue syndrome, stress, burnout, and natural cognitive changes associated with, for example, ageing, in order to introduce more effective differential diagnosis and screening. Full article
(This article belongs to the Special Issue Artificial Intelligence in Life Quality Technologies)
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