Advances in Intelligent Systems, Computer Science and Digital Economics II

2021 ◽  
Keyword(s):  
Computer Science ◽  
Intelligent Systems ◽  
Digital Economics

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

scholarly journals Prospects for the use of artificial intelligence systems in gastroenterology

2020 ◽  
pp. 109-117
Author(s):  
B. A. Kobrinskii ◽  
A. I. Khavkin ◽  
G. V. Volynets
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Intelligent Systems ◽  
Clinical Medicine ◽  
Correct Diagnosis ◽  
Huge Number ◽  
Powerful System ◽  
Difficult Cases ◽  
Artificial Intelligence Systems

The lecture is devoted to a new direction in clinical medicine — the possibility of using artificial intelligence — the field of computer science, which is engaged in modeling the method of acquiring and using knowledge specific to humans. The basis for a correct diagnosis is a combination of experience, the ability to think and act non-standard in difficult cases. A powerful system of generalization and classification, implemented in intelligent systems, allows you to reduce a huge number of possible situations to a small number of typical situations by which decisions or hypotheses are formed.

scholarly journals Big data technology in education

2020 ◽  
Vol 100 (4) ◽  
pp. 8-15
Author(s):  
M Serik ◽  
◽  
G Nurbekova ◽  
J Kultan ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Intelligent Systems ◽  
Professional Training ◽  
Educational Process ◽  
Educational Institutions ◽  
National University ◽  
The Internet Of Things ◽  
Theoretical Foundations ◽  
Big Data Technology

The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.

On the Intersection of Artificial Intelligence and Distance Education

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Computer Science ◽  
Real World ◽  
Intelligent Systems ◽  
Current Trends ◽  
Learning Technique ◽  
Technological Developments ◽  
E Learning ◽  
Oriented Research

In today's world, intelligent systems play an important role in improving humankind's life standards and providing effective solutions for real-world-based problems. In this sense, such intelligent systems are the research outputs of the Artificial Intelligence field in Computer Science. Today, in many fields intelligent systems are widely used to obtain effective and accurate results for the problems encountered. At this point, education is one of the most remarkable fields in which lots of Artificial Intelligence-oriented research works are performed. When we consider the education field in terms of the latest technological developments, we can also see that the e-learning technique and more generally distance education approach are highly associated with the applications of Artificial Intelligence. Therefore, in this chapter the author explores the trends within the interaction between Artificial Intelligence and Distance Education. The chapter is a brief report on current trends of applications of “intelligent distance education” solutions. It also provides a short focus on the future possibilities of the relation of Artificial Intelligence and Distance Education.

scholarly journals 'Informees' as Informers? An Approach to Effective Informing

10.28945/2608 ◽  
2003 ◽  
Author(s):  
Iwona Miliszewska ◽  
Anne Venables
Keyword(s):  
Artificial Intelligence ◽  
Active Learning ◽  
Computer Science ◽  
Educational Research ◽  
Subject Matter ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Science Degree ◽  
The Subject ◽  
Selection Of

An Intelligent Systems subject is offered in the final year of the Computer Science degree. The subject includes a diverse selection of topics in artificial intelligence and intelligent agents. The paper reflects on an innovative approach to the implementation of this subject. The development of the approach drew on educational research and the Informing Science paradigm. The aims of the approach included enga g-ing students in active learning, integrating theory with practice, and presenting the subject matter in an effective way. An innovative aspect of the approach was participatory teaching, i.e. students acting as guest lecturers and workshop presenters. The paper presents evaluation results indicating that the aims of the approach were achieved to a large extent.

scholarly journals Sistem Pendukung Keputusan Prediksi Kecepatan Studi Mahasiswa Menggunakan Metode ID3

2011 ◽  
Vol 2 (2) ◽  
Author(s):  
Ronny Ardi Giovani ◽  
Paulus Mudjihartono ◽  
Pranowo Pranowo
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Computer Science ◽  
Support System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Speed Prediction ◽  
Past Data ◽  
Tree Development

Abstract. Decision Support System of Students’ Study Speed Prediction Using ID3 Method. Speed can be a decisive period of study a student taking a degree in sajana. In this study would be built applications that serve to speed decision making predictions Students study Computer Science University of Atma Jaya Yogyakarta. Students will be expected sooner or later than the period of study by taking one course or thesis that will be undertaken after a certain semesters. There are many methods of classification of one method of ID3 (Induction Decision 3 'Tree'). Development system in this study made use of intelligent systems-based applications. The results achieved after the system is formed, among others, sophisticated and intelligent system capable of storing past data is used as a reference for decision making, where students with certain criteria can know the travel time of their studies, and can refer to the database so the system can be more detailed and rigorous in determining the choice. Keywords: study period speed, Decision Support Systems, ID3, Intelligent Systems Kecepatan masa studi dapat menjadi penentu seorang mahasiswa dalam menempuh gelar sajana. Dalam penelitian ini akan dibangun aplikasi yang berfungsi untuk pengambilan keputusan prediksi kecepatan studi Mahasiswa Teknik Informatika Universitas Atma Jaya Yogyakarta. Mahasiswa akan diprediksi  cepat atau lambatnya masa studi dalam menempuh mata kuliah maupun skripsi yang akan dijalani setelah semester tertentu. Ada banyak metode klasifikasi salah satunya metode ID3 (Induction Decision 3 ‘Tree’). Pembangunan sistem dalam penelitian ini dibuat menggunakan aplikasi berbasis sistem  cerdas. Hasil yang dicapai setelah sistem ini terbentuk antara lain sistem canggih dan cerdas yang mampu menyimpan data masa lalu yang digunakan sebagai acuan pengambilan keputusan, dimana mahasiswa dengan kriteria tertentu dapat diketahui masa tempuh studi mereka, serta dapat mengacu pada database sehingga sistem dapat lebih detail serta teliti dalam menentukan pilihan. Kata Kunci: kecepatan masa studi, Sistem Pendukung Keputusan, ID3, Sistem Cerdas

On the Intersection of Artificial Intelligence and Distance Education

2018 ◽  
pp. 1348-1360
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Computer Science ◽  
Real World ◽  
Intelligent Systems ◽  
Current Trends ◽  
Learning Technique ◽  
Technological Developments ◽  
E Learning ◽  
Oriented Research

In today's world, intelligent systems play an important role in improving humankind's life standards and providing effective solutions for real-world-based problems. In this sense, such intelligent systems are the research outputs of the Artificial Intelligence field in Computer Science. Today, in many fields intelligent systems are widely used to obtain effective and accurate results for the problems encountered. At this point, education is one of the most remarkable fields in which lots of Artificial Intelligence-oriented research works are performed. When we consider the education field in terms of the latest technological developments, we can also see that the e-learning technique and more generally distance education approach are highly associated with the applications of Artificial Intelligence. Therefore, in this chapter the author explores the trends within the interaction between Artificial Intelligence and Distance Education. The chapter is a brief report on current trends of applications of “intelligent distance education” solutions. It also provides a short focus on the future possibilities of the relation of Artificial Intelligence and Distance Education.

scholarly journals Thinking and Behaving Scientifically in Computer Science: When Failure is an Option!

10.28945/3048 ◽  
2006 ◽  
Author(s):  
Anne Venables ◽  
Grace Tan
Keyword(s):  
Computer Science ◽  
Undergraduate Students ◽  
Intelligent Systems ◽  
Academic Disciplines ◽  
Student Beliefs ◽  
Problem Solving Skills ◽  
Assessment Task ◽  
Science Degree ◽  
Experimental Learning ◽  
The Given

In a Finnish study of four different academic disciplines, Ylijoki (2000) found that in Computer Science there was a disparity between the conceptions held by undergraduate students and staff about their discipline; students viewed it as being far more pragmatic and results focused than did their instructors. Not surprisingly, here at our Australian university where the undergraduate Computer Science program emphasizes programming and problem solving skills, the authors had noticed a similar inconsistency between staff and student beliefs. This paper reports on an effort to realign these conceptions and broaden student experience using an assessment task. Centered on solutions to the popular ‘Sudoku’ puzzle (Sudoku, 2005), the task was designed and introduced into an Intelligent Systems course, a final year elective of a Computer Science degree. The goal was to expose students to some of the ‘pure’ rather than applied aspects of the Computer Science discipline (Becher & Trowler, 2001), by using assessment to encourage experimental learning (Kolb & Fry, 1975). The assessment specification instructed students to design and conduct several ‘in silica’ Computer Science experiments to solve and/or create Sudoku puzzles. Importantly, students were asked to keep a Research Diary documenting their thoughts, attempts, backtracking and progresses as they attempted the assignment. Most unique from a student’s perspective was that ‘failure’ to solve the given problem by experimentation was a viable option; their efforts would be rewarded given they conducted themselves ‘scientifically’ in their attempt.

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