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An effective global path planning algorithm with teaching-learning-based optimization

Emad Hazrati Nejad;  Sevgi Yigit-Sert;  Sahin Emrah Amrahov — 2024

Kybernetika

Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed...

A new curve fitting based rating prediction algorithm for recommender systems

Yilmaz Ar;  Şahin Emrah Amrahov;  Nizami A. Gasilov;  Sevgi Yigit-Sert — 2022

Kybernetika

The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase...

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