scholarly journals Emerging Complexity in Distributed Intelligent Systems

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1437
Author(s):  
Valentina Guleva ◽  
Egor Shikov ◽  
Klavdiya Bochenina ◽  
Sergey Kovalchuk ◽  
Alexander Alodjants ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Educational Process ◽  
Distributed Intelligence ◽  
New Class ◽  
Quantum Domain ◽  
Educational Assistants ◽  
Distributed Intelligent Systems

Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

Empirical Study on Intelligent Systems Coordination – A Case Study of Intelligent Tourism

2015 ◽  
Vol 740 ◽  
pp. 966-971
Author(s):  
Rong Rong Cai ◽  
Shu Tang
Keyword(s):  
Intelligent Systems ◽  
Perceived Quality ◽  
Travel Agency ◽  
Traditional Theory ◽  
Satisfaction Index ◽  
Index Model ◽  
Tourist Satisfaction ◽  
Intelligent City

Based on the traditional theory of the intelligent systems, as well as the present study on intelligent tourism, Tourist Satisfaction Index model of Intelligent Tourism is put out in this paper. With the data collected in Nanjing, statistic materials suggest that two variables, Perceived Quality of the Intelligent Tourism and Intelligent City, play the most important roles in tourist satisfaction of intelligent tourism. The further analysis reveals that the factor under Perceived Quality of Intelligent Tourism, including Intelligent Transportation, Public Service Platform, Intelligent Hotel and Intelligent Travel Agency, as well as the factors under Intelligent City including Intelligent Governance and Intelligent Environment are the most important factors in the model.

scholarly journals Business and Management Higher Education Quality: A Case Study of a Czech Private University

2019 ◽  
Vol 27 (1) ◽  
pp. 87-97
Author(s):  
Lucie Vnoučková ◽  
Hana Urbancová ◽  
Helena Smolová
Keyword(s):  
Higher Education ◽  
University Students ◽  
Private University ◽  
Educational Process ◽  
Education Quality ◽  
Business Economics ◽  
Higher Education Quality ◽  
Multidimensional Concept

Assessment of the business higher education quality is a multifaceted and multidimensional concept. Quality as a factor of performance of universities is currently an often-discussed topic. The aim of this article is to identify and evaluate factors of quality of business economics education by university students at a private Czech university. The results are based on a quantitative survey by questionnaire data collection from university students. The factor analysis was conducted to find significant groups of students regarding their perception of the educational process divided into three main areas. The quality perception was analyzed in this paper specifically by using focus on areas of subjects, lessons, and teachers. The analysis found groups of variables with significant appearance within the groups of students to reveal their main orientation and preferences. It is quality orientation (specified learning outcomes and its applicability), business orientation (tailoring to business needs) and expert orientation (skills and knowledge of teacher, his/her orientation on study group and tailoring lessons to their needs). Furthermore, identification of homogenous groups of students and their expectations helps with a design of subjects and lessons in the way of focusing on practice, addressing the needs and preferred teaching techniques. This is especially true when the students are already experienced in the taught subject. A limitation of the study is a narrow focus on one private university. It may be taken as a case study.

scholarly journals Use of artificial intelligence technologies for building individual educational trajectories of students

2021 ◽  
Vol 18 (1) ◽  
pp. 27-35
Author(s):  
Roman B. Kupriyanov ◽  
Dmitry L. Agranat ◽  
Ruslan S. Suleymanov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Educational Process ◽  
Content Filtering ◽  
Machine Learning Methods ◽  
Educational Trajectories ◽  
Textual Data ◽  
Second Year

Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

scholarly journals 3D Animation and Virtual Reality Integrated Cognitive Computing for Teaching and Learning in Higher Education

2021 ◽  
Author(s):  
Abhishek Kumar ◽  
Rini Dey ◽  
G. Madhukar Rao ◽  
Saravanan Pitchai ◽  
K. Vengatesan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Virtual Reality ◽  
Teaching And Learning ◽  
3D Animation ◽  
Multimodal Approach ◽  
Cognitive Computing ◽  
Teaching Procedure

This paper proposes the and justify how we can enhance the quality of medical education through immersive learning and AI (Artificial Intelligence) use in education. A Multimodal Approach for Immersive Teaching and learning through Animation, AR (Augmented Reality) & VR (Virtual Reality) is aimed at providing specifically medical students with knowledge, skills, and understanding. It is important to understand the current challenge involved in medical education. This paper reports the findings of a novel study on the technology enable teaching with Animation, AR and VR by and MR impact. A case study was conducted involving 521 participants from different states of India. The data was analyzed by their feedback after using this Virtual reality-based teaching procedure in classroom. Recommendations from this paper that are expected to effectively improving the quality of medical education in faster way.

scholarly journals EFFECTIVENESS OF GUIDED DISCOVERY-BASED MODULE: A CASE STUDY IN PADANG CITY, INDONESIA

2019 ◽  
Vol 10 (2) ◽  
pp. 239-250
Author(s):  
Delsi Kariman ◽  
Yulyanti Harisman ◽  
Anny Sovia ◽  
Rully Charitas Indra Prahmana
Keyword(s):  
Complex Analysis ◽  
Educational Process ◽  
Learning Models ◽  
Teaching Materials ◽  
Guided Discovery ◽  
Research Designs ◽  
Materials Used ◽  
Education Process

One of the research objectives was formulated to improve the quality of education, namely by understanding the problems of students and teachers. There were three main groups of quality characteristics of education highlighted in scientific and academic literature in Russia, namely the quality of educational objectives, the quality of the educational process, and the quality of educational outcomes. The problem of education in Indonesia is the quality of the education process. The development of teaching materials is an alternative to improve the quality of the education process. Research so far has only focused on the implementation of learning models by teachers in the classroom, but the model is not integrated into the teaching materials used. This paper examines how the effectiveness of a product that has been developed for two years on 27 Indonesian students. The product produced is a guided discovery-based module in the Complex Analysis subject. This product was developed after the preliminary analysis (defining) process. Students are given 16 modules during lectures; then students are given a final test containing all the competencies that must be achieved. Test results are scored, and statistical analyses are conducted to compare them with student score before using the module. The test used is t-test. The design used is one of the experimental research designs, One Shot Case Study. The results of the study showed that students who were taught with modules developed effectively to improve student learning outcomes. Further research can be done by implementing different learning models in teaching materials.

scholarly journals Big Data and the Threat to Moral Responsibility in Healthcare

2021 ◽  
pp. 11-25
Author(s):  
Daniel W. Tigard
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Moral Responsibility ◽  
Intelligent Systems ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Trade Offs ◽  
The Social ◽  
Healthcare Technologies

AbstractTechnological innovations in healthcare, perhaps now more than ever, are posing decisive opportunities for improvements in diagnostics, treatment, and overall quality of life. The use of artificial intelligence and big data processing, in particular, stands to revolutionize healthcare systems as we once knew them. But what effect do these technologies have on human agency and moral responsibility in healthcare? How can patients, practitioners, and the general public best respond to potential obscurities in responsibility? In this paper, I investigate the social and ethical challenges arising with newfound medical technologies, specifically the ways in which artificially intelligent systems may be threatening moral responsibility in the delivery of healthcare. I argue that if our ability to locate responsibility becomes threatened, we are left with a difficult choice of trade-offs. In short, it might seem that we should exercise extreme caution or even restraint in our use of state-of-the-art systems, but thereby lose out on such benefits as improved quality of care. Alternatively, we could embrace novel healthcare technologies but in doing so we might need to loosen our commitment to locating moral responsibility when patients come to harm; for even if harms are fewer – say, as a result of data-driven diagnostics – it may be unclear who or what is responsible when things go wrong. What is clear, at least, is that the shift toward artificial intelligence and big data calls for significant revisions in expectations on how, if at all, we might locate notions of responsibility in emerging models of healthcare.

THE RELATIONSHIP BETWEEN EDUCATION INPUTS AND THE QUALITY OF THE GRADUATES: A CASE STUDY IN VIETNAM

2010 ◽  
Vol 13 (4) ◽  
pp. 79-91
Author(s):  
Duoc Quang Truong
Keyword(s):  
Graduate Students ◽  
Structural Equation ◽  
Analytical Study ◽  
Educational Process ◽  
Ho Chi Minh City ◽  
Equation Modeling ◽  
Tertiary Institutions ◽  
The Relationship

The purpose of this exploratory and analytical study is to answer the question about the relationships between education inputs in the educational process and the quality of graduates. For this question, exploratory factor analysis (EFA) and Structural equation modeling (SEM) are the two major statistical approaches used for scale purification and data analysis. The population for this study is composed of 460 graduate students and 195 faculties from 7 main tertiary institutions across Vietnam and 153 employers in Ho Chi Minh City. The findings from the study indicate that there is a relationship between faculty, curriculum and entrant students to the graduate quality. The research has implications for universities, faculty, and employers in Vietnam. Several recommendations are suggested for further research in Vietnam or other developing countries.

scholarly journals Towards understanding of artificial intelligence in accounting profession

2021 ◽  
pp. 1-5
Author(s):  
Aditya Prasad Sahoo Sahoo ◽  
Dr Yajnya Dutta Nayak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Systems ◽  
Feature Detection ◽  
Expert Knowledge ◽  
Object Identification ◽  
Extensive Experience ◽  
Efficiency And Effectiveness ◽  
Repetitive Activities

Accountants have embraced the emission of automation over many years to get better the efficiency and effectiveness of their work. But technology has not been able to replace the need for expert knowledge and decision-making. Earlier generations of ‘intelligent systems have usually demonstrated the progressing power of human expertise and the restrictions of machines. In the upcoming decades, intelligent systems must take over more and better decision-making tasks from humans. While accountant has been using technology for a lot of years to improve what they do and deliver more value to businesses, this is an opportunity to reimagine and radically improve the quality of business and investment decisions which is the ultimate purpose of the profession. Accountants, as expert decision-makers, use both ways of thinking they apply their knowledge to specific situations to make reasoned decisions, although also make quick intuitive decisions based on extensive experience in their field. Today, AI is being used for image recognition, object identification, detection, classification, and automated geophysical feature detection. These are underlying tasks that once required the input of a human. Focusing on how artificial intelligence will impact accountants, AI will very soon help the organization to automate much of the routine and repetitive activities that are undertaken on a daily, weekly or annual basis. It will also help the organization to empower quick decision-making to create smart insights examine huge quantities of data with ease.

scholarly journals THE ROLE OF INTELLIGENT INFORMATION SYSTEMS IN THE MODERN WORLD

2021 ◽  
Author(s):  
И.Р. Усамов ◽  
А.А. Албакова ◽  
А.А. Мустиев
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Intelligent Systems ◽  
Quality Of Services ◽  
Modern World ◽  
Intelligent Information Systems ◽  
Intelligent Information ◽  
The Moment

Статья посвящена рассмотрению роли интеллектуальных информационных систем в современном мире. Проведен анализ и рассмотрена сущность интеллектуальных систем, отрасли использования интеллектуальных систем, выделены проблемы внедрения интеллектуальных информационных систем и предложены механизмы решения проблем внедрения интеллектуальных информационных систем. Рассмотрены основные отрасли, где используются интеллектуальные информационные системы для повышения скорости производства и улучшения качества оказываемых услуг. Рассмотрены основные три проблемы искусственного интеллекта, которые не решены на данный момент, и которые в будущем могут вызвать мировой хаос. Предложены механизмы решения данных трех проблем. The article is devoted to the role of intelligent information systems in the modern world. The article analyzes and considers the essence of intelligent systems, the branches of using intelligent systems, identifies the problems of implementing intelligent information systems, and suggests mechanisms for solving the problems of implementing intelligent information systems. The main industries where intelligent information systems are used to increase the speed of production and improve the quality of services provided are considered. The main three problems of artificial intelligence, which are not solved at the moment, and which in the future can cause global chaos, are considered. Mechanisms for solving the set here problems areproposed.

scholarly journals An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1492
Author(s):  
Mogana Darshini Ganggayah ◽  
Sarinder Kaur Dhillon ◽  
Tania Islam ◽  
Foad Kalhor ◽  
Teh Chean Chiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Automated Scoring

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

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