The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) $2/3$ databases.

The novel approach for automatic detection and classification of road defects is proposed based on shape and texture features analysis. The system includes three main steps: defects position detection, feature contour extraction followed by classific

The aim of this paper is to propose the convolution neural network—VGG16 structure in Keras to classify pavement defects. In this paper, we present a

ROC analysis is a visual and numerical method used to evaluate the performance of classification algorithms, such as those used to predict the structure and functions from string data. The main objective of the paper is to use the ROC analysis to evaluate the accuracy of the Random Forest algorithm to classify road surface defects on three different sets of data collected from Portugal, Irkutsk city - Russia Federation and Thai Nguyen city - Vietnam. This article summarizes the basics of ROC analysis and interprets the results analysis with other thresholds to build ROC curve. In addition, we present the steps to build a system to automatically classify road surface defects based on visual techniques and machine learning methods.

This study tested solutions aiming to improve the estimates of tropical forest stand volume (SV) for the large area using SPOT 5 data and field data. Firstly, forests were stratified into four forest strata using a hybrid classification of ISODATA (Iterative Self - Organization Data Analysis) unsupervised, and Maximum likelihood supervised (MLC) based on SPOT 5 image and field data. A set of 111 sample plots was distributed in these four forest strata, which represented disturbed forests under different levels by human-induced. Within the sample plots, diameter at breast height (DBH) and tree height (H) were measured to calculate standing volume using the SV equation from the previous study for the area. The method of k-NN (k-Nearest Neighbour) was applied to estimate the stand volume for different datasets of SPOT 5 bands, normalized difference vegetation index (NDVI), and combination of SPOT 5 bands and NDVI. The estimates were separately tested for the whole area and each forest stratum. Leave one out cross-validation method was employed to validate the quality of the predictions. The results indicated the accuracy of the estimate was significantly improved when applying for each stratum comparing to for the whole area with both SPOT 5 and NDVI data. The lower errors were found in the forest strata that are fewer disturbances than the heavily degraded stratum. Among the image data, the estimates were based on the NDVI give a lower accuracy compared to others.