PLANNING THE UNIVERSITY TIMETABLE USING NEURAL NETWORKS
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Date
2019
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Publisher
Faculty of Engineering and Natural Science
Abstract
Planning the timetable issues with constraints for the most part have a place with the enormous class of NP complete issues. A significant gathering of such issues concerns Timetable Scheduling (TS) for individuals and devices. In this process, we will optimize the algorithm of the lesson schedule at KUU (renamed the university in accordance with administration requirements). However, the planning and settlement of this issue made it difficult for many of these constraints. This work proposes an effective way to deal with TS utilizingq Lateral Inhibitory Networks (LIN) for imperatives the board. TS can be considered as a booking issue with disjunctive limitations, and the Neural Network model with LIN is all around adjusted to it. This kind of neural models and the arrangement picked to take care of the TS issue is depicted. An application, written by Python language utilizing a PC, has been actualized to take care of the TS issue. This way of solving the problem gives a new impetus to the graphics.
Description
Keywords
Timetable Scheduling, Python, Neural Network
Citation
Adilet D / PLANNING THE UNIVERSITY TIMETABLE USING NEURAL NETWORKS / Faculty of Engineering and Natural Science / 6M060100 - 2019