This paper proposes a novel methodology for delineating urban areas based on a machine learning algorithm that groups buildings within portions of space of sufficient density. To do so, we use the precise geolocation of all 12 million buildings in Spain. We exploit building heights to create a ...
The process of building the adaptation code Spark-ml-algo-lib for the machine learning algorithm library is as follows: This section uses the build process that adapts to the Spark 2.3.2 code as an example. The process that adapts to the Spark 2.4.6 code is ...
A neural network algorithm (rxNeuralNet) with support for custom, multilayer network topologies; and One-class anomaly detection (rxOneClassSvm) based onsupport vector machines. As the function names suggest, the implementations are tuned for speed: most use multiple CPUs, and some will even use ...
More specifically, the energy demand of a building is calculated for different ECMs and this is used as a criterion to select the best performing ECM. Ceballos-Fuentealba et al. [12] combined such steady state linear energy equations with an easy to tune parameter optimization algorithm to ...
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch). - ahmedfgad/GeneticAlgorithmPython
As a result of the analysis, it was seen that the data obtained from the ICESat-2 system was successful in estimating building height and provided reliable data. 展开 关键词: ICESat-2/ATLAS Airborne LiDAR Machine Learning Algorithm Building Height Photon Laser ...
A machine learning approach to automated building of knowledge bases for image analysis expert systems incorporating GIS data is presented. The method uses an inductive learning algorithm to generate production rules from training data. With this method, building a knowledge base for a rule-based expe...
We’ll use Python’s machine learning library scikit-learn for finding nearest neighbors of the query features; that is, features that represent a query image. We train a nearest-neighbor model using the brute-force algorithm to find the nearest five neighbors based on Euclidean distance (to in...
The data set is labelled and hence forms part of the supervised machine learning category. As a general rule, the data analyst should mark the underrepresented class as “1” and the normal class with “0” in the data set. The algorithm will detect it accordingly and thereby derive the va...
• An activity-aware sensor cycling (ASC) to manage uncommon and unpredicted activities and makes a balance between sensors’ energy usage by a scheduling algorithm. • ASC sensor detects 99% of activities and warranties the network lifetime for 2000 h. • Enabling IoT for In-Home Rehabili...