Annals of Mathematics and Artificial Intelligence

Syriopoulos, Panos K.; Kalampalikis, Nektarios G.; Kotsiantis, Sotiris B.; Vrahatis, Michael N.

2023 Annals of Mathematics and Artificial Intelligence

doi: 10.1007/s10472-023-09882-x

The k-nearest neighbors (k/NN) algorithm is a simple yet powerful non-parametric classifier that is robust to noisy data and easy to implement. However, with the growing literature on k/NN methods, it is increasingly challenging for new researchers and practitioners to navigate the field. This review paper aims to provide a comprehensive overview of the latest developments in the k/NN algorithm, including its strengths and weaknesses, applications, benchmarks, and available software with corresponding publications and citation analysis. The review also discusses the potential of k/NN in various data science tasks, such as anomaly detection, dimensionality reduction and missing value imputation. By offering an in-depth analysis of k/NN, this paper serves as a valuable resource for researchers and practitioners to make informed decisions and identify the best k/NN implementation for a given application.

Rbihou, Safae; Joudar, Nour-Eddine; Haddouch, Khalid

2023 Annals of Mathematics and Artificial Intelligence

doi: 10.1007/s10472-023-09895-6

The continuous Hopfield network (CHN) has provided a powerful approach to optimization problems and has shown good performance in different domains. However, two primary challenges still remain for this network: defining appropriate parameters and hyperparameters. In this study, our objective is to address these challenges and achieve optimal solutions for combinatorial optimization problems, thereby improving the overall performance of the continuous Hopfield network. To accomplish this, we propose a new technique for tuning the parameters of the CHN by considering its stability. To evaluate our approach, three well-known combinatorial optimization problems, namely, weighted constraint satisfaction problems, task assignment problems, and the traveling salesman problem, were employed. The experiments demonstrate that the proposed approach offers several advantages for CHN parameter tuning and the selection of optimal hyperparameter combinations.

Bulut, Vahide

2023 Annals of Mathematics and Artificial Intelligence

doi: 10.1007/s10472-023-09893-8

Finding a collision-free feasible path for mobile robots is very important because they are essential in many fields such as healthcare, military, and industry. In this paper, a novel Clustering Obstacles (CO)-based path planning algorithm for mobile robots is presented using a quintic trigonometric Bézier curve and its two shape parameters. The CO-based algorithm forms clusters of geometrically shaped obstacles and finds the cluster centers. Moreover, the proposed waypoint algorithm (WP) finds the waypoints of the predefined skeleton path in addition to the start and destination points in an environment. Based on all these points, the predefined quintic trigonometric Bézier path candidates, taking the skeleton path as their convex hull, are then generated using the shape parameters of this curve. Moreover, the performance of the proposed algorithm is compared with K-Means and agglomerative hierarchical algorithms to obtain the quintic trigonometric Bézier paths desired by the user. The experimental results show that the CO-based path planning algorithm achieves better cluster centers and consequently better collision-free predefined paths.