We propose a new approach for outlier detection, based on a new ranking measure that focuses on the question of whether a point is “important” for its nearest neighbors; using our notations low cumulative rank implies the point is central. For instance, a point centrally located in a cluster has relatively low cumulative sum of ranks because it is among the nearest neighbors of its own nearest ...

This paper presents a Memory Neuron Network (MNN) controller for the lateral position control of a vehicle on highway in the aim to increase the traffic safety. We first present an enhanced MNN with its fast learning algorithm based on double backpropagation. Then we describe experiments achieved in collaboration with the French car manufacturer group PSA Peugeot Citroën on a XM vehicle. The pr...

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation demand Turkey’s mainland with use mixed MNN, WAO, SVM. Imports, exports, gross domestic product (GDP), population data based on input from 1980 2019 Turkey, demand...

We present a simple method for meshing a 3D point cloud to a manifold genus-0 mesh. Our approach is based on recent methods for spherical embedding of planar graphs, where we use instead a k-nearest neighborhood graph of the point cloud. Our approach proceeds in two steps: We first embed the neighborhood graph on a sphere using an iterative procedure, minimizing the tangential Laplacian. Then w...

Local Tangent Space Alignment (LTSA) algorithm is a classic local nonlinear manifold learning algorithm based on the information about local neighborhood space, i.e., local tangent space with respect to each point in dataset, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. In this paper, we present a nov...

Diffusion filters are widely used to reduce noise on images. In [1] a new diffusion method for image filtering based on incorporating an integral of image intensity over a point neighborhood into the diffusion coefficient was suggested. Here we start by introducing two modified diffusion filters. Next we illustrate on number of images the advantages of modified filters comparing with not modifi...

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor,...

A method for the classification of tabular grain silver halide microcrystals according to their shape is presented. Various approaches of shape analysis and recognition and their applicability for the given problem are discussed. Shape descriptors obtained from Fourier power spectra are used to describe the shape of microcrystals. The classification of the shapes is based on nearest neighborhoo...