A Computer Assisted Decision Support System for Education Planning

Author(s):  
Yigit Alisan ◽  
Faruk Serin

The advances in technology are eliminating the demand for certain occupations and creating new opportunities. Thus, the universities, teachers, and students have to collaboratively work together to restructure their departments, course offerings, and course contents. Failure to realize the aforementioned initiatives may lead to a loss of quality and competitiveness. This study proposes a decision support system capable of maintaining the quality and competitiveness of the departments and the course offerings. The proposed system consists of three stages. The first stage is the data collection stage. At this stage, data are collected from the internet using web scraping methods. In the second stage, the collected data are turned into meaningful and processable information by natural language processing methods. In the third stage, the alternatives are ranked using multi-criteria decision-making methods. The proposed decision support system provides useful information to several educational stakeholders. First, universities are informed on which departments to create or close as well as the relevant course offerings. Second, information are provided to the teachers to create new courses or shape the course contents. Finally, students are better informed on how to go about choosing the universities, departments, courses, or career paths to pursue. The applicability and reliability of the proposed decision support system were experimentally proven through the use of computer engineering-related job postings and course contents of the universities in Turkey.

1986 ◽  
Vol 15 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Balasubramanian Ram ◽  
Muhammed Al-Rumaih ◽  
Sanjiv Sarin

This article describes a decision support system (DSS) for planning course offerings in an academic department. The system involves selection of courses, assignment of instructors to courses, and scheduling of courses. The DSS has been implemented and is currently in use.


Author(s):  
Maharukh Syed ◽  
◽  
Meera Narvekar ◽  

Depression is one of the leading causes of suicides in society. The youth of the 21st century are inclined towards social media for all their needs and expressions. Close friends can easily predict if someone is happy, sad, or depressed from a user’s daily social media activity like status uploads/shares/reposts/check-ins, etc. This activity can be analyzed in order to understand the pattern of mental health. Such data is easily available and if suspected, it can be reported to a Psychiatrist and Psychologist to prevent socially active depressed patients from taking any wrong decisions regarding their life thus providing a Decision Support System (DSS). Various natural language processing techniques have been used in order to detect depression but there is a need for a unified architecture that is based on contextual data and is bidirectional in nature. This can be achieved by using example be achieved by using the Google research project (BERT) Bidirectional Encoder Representations from Transformers.


Author(s):  
Daniel Ruiz-Fernandez ◽  
Antonio Soriano-Paya

The incorporation of computer engineering into medicine has meant significant improvements in the diagnosis-related tasks. This chapter presents an architecture for diagnosis support based on the collaboration among different diagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinical meetings in hospitals in which the members of a medical team share their opinions in order to analyze complicated diagnoses. A system that combines availability, cooperation and harmonization of all contributions in a diagnosis process will bring more confidence in healthcare for the physicians. They have tested the architecture proposed in two different diagnosis, melanoma, and urological dysfunctions.


2020 ◽  
Vol 11 (1) ◽  
pp. 216
Author(s):  
Mohammed Jabreel ◽  
Najlaa Maaroof ◽  
Aida Valls ◽  
Antonio Moreno

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.


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