scholarly journals Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Digital ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 162-172
Author(s):  
Nafissa Yusupova ◽  
Diana Bogdanova ◽  
Nadejda Komendantova ◽  
Hossein Hassani
Keyword(s):  
Artificial Intelligence ◽  
Affective Computing ◽  
Scientific Publication ◽  
Subject Area ◽  
Research Trends ◽  
Digital Platforms ◽  
Area Of Interest ◽  
Structured Information ◽  
The Subject ◽  
Citation Databases

The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”.

scholarly journals Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5015
Author(s):  
Muhammad Anas Hasnul ◽  
Nor Azlina Ab. Ab.Aziz ◽  
Salem Alelyani ◽  
Mohamed Mohana ◽  
Azlan Abd. Abd. Aziz
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Review Paper ◽  
Recognition System ◽  
Healthcare Systems ◽  
Multimodal Approach ◽  
Health And Wellbeing ◽  
The Subject ◽  
Recognition Systems

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

scholarly journals Method for resolving the lexical polysemous search query based on ontology

2020 ◽  
Vol 15 (89) ◽  
pp. 103-110
Author(s):  
Iuliia I. Butenko ◽  
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Subject Area ◽  
Search Query ◽  
Lexical Unit ◽  
Area Of Interest ◽  
Factors Influencing ◽  
Semantic Component ◽  
Subject Areas ◽  
The Subject

One of the factors influencing the relevance of search output is the multivalued search query, expressed by lexical means of a natural language. The multivalued lexical unit manifests itself at the stage of a search query. The method of removing the multivalence of lexical units in a search query based on ontologies is proposed. It is grounded that ontologies allow transferring semantic component of data related to a subject area accurately enough. The proposed method for lexical multivariance resolution can be described as follows. A user search query is received at the search engine input. The search engine contacts the ontology library to find the query. If the lexical unit from the search query is multivalued, the search engine will offer the user a list of subject areas in which the lexical unit from the search query was found. Oft the user searches in advance for the result from a particular subject area. When the subject area is defined, the search engine determines the nearest elements in the structure of ontology, and when ranking the search output will be guided by their presence or absence. The use of ontologies also allows adding synonyms and acronyms meaning the same to the search query. The proposed approach will allow solving lexical multiplicity and significantly relieving the search output, leaving only the subject area of interest to the user.

scholarly journals literature Review: Artificial Intelligence Impact on the Recruitment ProcessA LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE IMPACT ON THE RECRUITMENT PROCESS

2021 ◽  
Vol 6 (1) ◽  
pp. 108-119
Author(s):  
JihaD FraiJ ◽  
Várallyai László
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Human Resources Management ◽  
Subject Area ◽  
Recruitment Process ◽  
Core Subject ◽  
The Subject ◽  
The Impact ◽  
Magazine Articles ◽  
Good Number

This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring automation and cognitive insights as well as cognitive engagement in the recruitment process would make it possible for systems to work similarly to the human brain in terms of data analysis and the ability to build an effective systematic engagement to process the data in an unbiased, efficient and fast way.

scholarly journals Weaknesses of artificial intelligence in medicine

2021 ◽  
Vol 7 (2) ◽  
pp. 50-52
Author(s):  
I.A. Shaderkin ◽  
◽  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Medical Knowledge ◽  
Professional Community ◽  
Subject Area ◽  
Medical Decision ◽  
Successful Operation ◽  
Medical Decisions ◽  
The Subject ◽  
Sensitivity Specificity

Introduction. Recently, a large number of intelligent systems have begun to appear that are used to support medical decision- making – «artificial intelligence in medicine». Material and methods. The author of the publication also works on the issues of making medical decisions, and, being a doctor himself, in the course of his work and existing practice, discovered a number of important issues that he considered necessary to share with the professional community. Results. In some cases, the demonstration of the successful operation of the software in the declared characteristics (sensitivity, specificity) occurs only in the «reliable hands» of the developers and on the data that underlie the software. When attempting to demonstrate performance in clinical situations, the claimed characteristics are often not achieved, so the clinical community that must use this AI-based solution does not always form a favorable opinion. The author considers various types of errors that can be fatal in making medical clinical decisions – distortion of primary medical knowledge, lack of knowledge or inaccurate knowledge about the subject area, social distortions. Conclusions. When developing solutions based on AI, it seems important to keep the above points in mind for both developers and users.

scholarly journals Аlgorithmization of the knowledge base in service of passenger cars

2022 ◽  
Vol 14 (2) ◽  
pp. 80-88
Author(s):  
Viacheslav Pavlenko ◽  
◽  
Volodymyr Manuylov ◽  
Volodymyr Kuzhel ◽  
Vladislav Listgarten ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Intelligent Systems ◽  
Driving Forces ◽  
Conceptual Modeling ◽  
Subject Area ◽  
Detailed Knowledge ◽  
Passenger Cars ◽  
Subject Areas ◽  
The Subject

The article considers the architecture of conceptual modeling of the knowledge base, which creates a model of the subject area in the form of many concepts and relationships between them. This approach is based on the concept of a mobile software agent, which is implemented and functions as an independent specialized computer program or an element of artificial intelligence. Ensuring the use of subject area knowledge has become one of the driving forces of the recent surge in the study of artificial intelligence. For example, for models of many different subject areas it is necessary to formulate the concept of time. This representation includes the concepts of time intervals, time points, relative measures of time, etc. If one group of scientists develops a detailed knowledge base, others can simply reuse it in their subject areas using their own database. Creating explicit assumptions in the subject area, which underlie the implementation, makes it easy to change the assumptions when changing our knowledge of the subject area. The process of conceptualization of TO and P, first of all, involves the development of databases in research areas for the formalization and systematization of knowledge about the characteristics of this area of entities and phenomena. That is, the use of concepts in the field of maintenance in a consistent manner in relation to theories of knowledge. Ultimately, the paper updated mathematical modeling, algorithmization and implementation of intelligent systems in the field of maintenance, which will help automate the process of diagnosis and inspection of all car systems, facilitate fault prevention and improve the maintenance process and modernize the maintenance system itself. The approach of algorithmization of the base of knowledge of a condition of the car in each moment of time considered in work gives the chance to reduce time of stay of the car in the service center and to reduce considerably expenses for passing of MOT at service of cars.

Biometric Authentication Schemes and Methods on Mobile Devices

2020 ◽  
pp. 172-192
Author(s):  
Akon Obu Ekpezu ◽  
Enoima Essien Umoh ◽  
Felix Nti Koranteng ◽  
Joseph Ahor Abandoh-Sam
Keyword(s):  
Mobile Devices ◽  
Subject Area ◽  
Research Trends ◽  
Dental Occlusion ◽  
Biometric Authentication ◽  
Unauthorized Access ◽  
Touch Gestures ◽  
The Subject ◽  
Authentication Schemes ◽  
Authentication Technique

Due to the sensitivity and amount of information stored on mobile devices, the need to protect these devices from unauthorized access has become imperative. Among the various mechanisms to manage access on mobile devices, this chapter focused on identifying research trends on biometric authentication schemes. The systematic literature review approach was adopted to guide future researches in the subject area. Consequently, seventeen selected articles from journals in three databases (IEEE, ACM digital library, and SpringerLink) were reviewed. Findings from the reviewed articles indicated that touch gestures are the predominant authentication technique used in mobile devices, particularly in android devices. Furthermore, mimic attacks were identified as the commonest attacks on biometric authentic schemes. While, robust authentication techniques such as dental occlusion, ECG (electrocardiogram), palmprints and knuckles were identified as newly implemented authentication techniques in mobile devices.

scholarly journals Ontologization of mental representations for management purposes

2020 ◽  
pp. 5-9
Author(s):  
A. R. Akopyan ◽  
A. M. Arakelyan ◽  
V. V. Krysov
Keyword(s):  
Artificial Intelligence ◽  
Mental Representations ◽  
Subject Area ◽  
The Subject ◽  
Management Functions ◽  
First Time

The article considers the problems of ontologization of mental representations of a certain field of activity and the corresponding subject area for the purposes of management automation. For the first time, the authors raise the question about the need to include mental representations of the sphere of activity that the subject area reflects in the ontology of the subject area. Ontologization of mental representations is necessary to identify the participants' understanding of their actions, which will change approaches to management, and for deep training of artificial intelligence in order to transfer part of its management functions to it. On the basis of connectionist approaches to understanding thinking, the paper considers the processes of forming mental representations and changing their attributes. The article gives a new definition and typology of mental representations.

Unformalized tasks and methodology of their solution

2021 ◽  
Author(s):  
S.G. Vorona ◽  
V.V. Lisickii ◽  
A.V. Stolbov
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Semantic Network ◽  
Traditional Approach ◽  
A Priori ◽  
Operational Semantics ◽  
Human Memory ◽  
Subject Area ◽  
Domain Model ◽  
The Subject

The article deals with the methodology of solving non-formalized problems. The first methodology represents the traditional approach to the description of the problem domain model (MPO) and it is characterized by a significant operational semantics. The second description methodology represents a new direction in computer science − artificial intelligence. Any problem for which the solution algorithm is unknown, a priori refers to artificial intelligence. An algorithm is understood as the entire sequence of specified actions that are well defined and can be performed on a computer. The knowledge model is a means of describing the logic and data of the problem being solved. Since the knowledge about the subject area may be inaccurate, incomplete, or unclear, and the subject areas are different, the logic used in the description may also be different. The problem to be solved is presented in the form of rules (statements) and a request, the validity of which should be established or refuted. The solution of the problem is reduced to finding out the logical sequence (derivability) of the target formula from a given set of rules. The most popular knowledge models for creating ES are production models. The frame model, or the model of knowledge representation, based on the frame theory, is a psychological model of human memory and consciousness systematized in the form of a single theory The semantic network model is a knowledge representation model based on the semantic network method. The described models are only a small part of the applied methods for solving unformalized problems.

Neurophilosophy, Philosophy of Neuroscience, and Philosophy of Artificial Intelligence: The Problem of Distinguishing

2021 ◽  
Vol 64 (1) ◽  
pp. 71-87
Author(s):  
Evgeny A. Bezlepkin ◽  
Alina S. Zaykova
Keyword(s):  
Artificial Intelligence ◽  
Philosophy Of Science ◽  
21St Century ◽  
Subject Area ◽  
Philosophy Of Neuroscience ◽  
Early 21St Century ◽  
Methodological Basis ◽  
Eliminative Materialism ◽  
The Subject ◽  
The Common

Neurophilosophy is understood as different areas of philosophy, for example, the philosophy of neuroscience, the philosophy of artificial intelligence, or eliminative materialism. This excessive interpretation of the term is due to the fact that the understanding of the subject area of this discipline is still incomplete. For example, one of the earliest definitions of neurophilosophy given by P.S. Churchland stated reduction of psychology to neurosciences. In modern views, the idea of neurophilosophy as an attempt to justify eliminative materialism is outdated and does not correspond to reality. The article analyzes the terms “philosophy of neuroscience,” “neurophilosophy,” and “philosophy of artificial intelligence” and also offers a variant of their differentiation. The authors focus on the common and different features, using the example of G.M. Edelman's theory of consciousness and the concept of connectionism for weak artificial intelligence. It is concluded that integral use of the term “neurophilosophy” should be abandoned. As a result, the term “neurophilosophy” should be understood as a direction in philosophy of the early 21st century, applying neuroscientific concepts to solve traditional philosophical problems, while the philosophy of specific neurosciences can be considered primarily as a field in the philosophy of science that formulates and solves problems of specific neurosciences as well as of the entire neuroscientific direction. The philosophy of artificial intelligence is an area in philosophy that answers the question of what non-biological intelligence is and what makes it possible; in other words, it is a philosophical and methodological basis for the study of non-biological intelligence. In the formation of neurosciences and their scientific and philosophical basis, we are still at the first methodological stage of the analysis and differentiation of hypotheses. After some time, there will emerge a philosophy of neuroscience, as the basis of all existing neuroscientific theories, and then this term will acquire greater significance.

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