scholarly journals Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data

2014 ◽  
Vol II-2 ◽  
pp. 15-22 ◽  
Author(s):  
M.J. Schulze ◽  
F. Thiemann ◽  
M. Sester
Keyword(s):  
Semantic Distance ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Major Influence ◽  
Combine Data ◽  
Learning Techniques ◽  
Least Squares Adjustment ◽  
Data Infrastructures ◽  
Different Sources ◽  
Matching Probability

In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If both datasets are maintained separately, different capturing methods and intervals leads to inconsistencies in geometry and semantic, even if the same reality has been modelled. Our project aims to automatically harmonize such datasets and to allow an efficient actualisation of the semantics. The application domain in our project is cadastral and topographic datasets. To resolve geometric conflicts between topographic and cadastral data a local nearest neighbour method was used to identify perpendicular distances between a node in the topographic and an edge in the cadastral dataset. The perpendicular distances are reduced iteratively in a constraint least squares adjustment (LSA) process moving the coordinates from node and edge towards each other. The adjustment result has to be checked for conflicts caused by the movement of the coordinates in the LSA. <br><br> The correct choice of matching partners has a major influence on the result of the LSA. If wrong matching partners are linked a wrong adaptation is derived. Therefore we present an improved matching method, where we take distance, orientation and semantic similarity of the neighbouring objects into account. Using Machine Learning techniques we obtain corresponding land-use classes. From these a measurement for the semantic distance is derived. It is combined with the orientation difference to generate a matching probability for the two matching candidates. Examples show the benefit of the proposed similarity measure.

scholarly journals Standing out in a networked communication context: Toward a network contingency model of public attention

2020 ◽  
pp. 146144482093944
Author(s):  
Aimei Yang ◽  
Adam J Saffer
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Public Attention ◽  
Combine Data ◽  
Contingency Model ◽  
Learning Techniques ◽  
Community Strategy ◽  
Communication Context

Social media can offer strategic communicators cost-effective opportunities to reach millions of individuals. However, in practice it can be difficult to be heard in these crowded digital spaces. This study takes a strategic network perspective and draws from recent research in network science to propose the network contingency model of public attention. This model argues that in the networked social-mediated environment, an organization’s ability to attract public attention on social media is contingent on its ability to fit its network position with the network structure of the communication context. To test the model, we combine data mining, social network analysis, and machine-learning techniques to analyze a large-scale Twitter discussion network. The results of our analysis of Twitter discussion around the refugee crisis in 2016 suggest that in high core-periphery network contexts, “star” positions were most influential whereas in low core-periphery network contexts, a “community” strategy is crucial to attracting public attention.

A Multidimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence-Based Meta-Models

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Lorenzo Tieghi
Keyword(s):  
Axial Flow ◽  
Principal Component ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Operational Parameters ◽  
Kinematic Parameters ◽  
Fan Performance ◽  
Learning Techniques ◽  
Optimal Values ◽  
Specific Speed

Abstract The main intent of this work is the exploration of the rotor-only fan design space to identify the correlations between fan performance and enriched geometric and kinematic parameters. In particular, the aim is to derive a multidimensional “Balje chart,” where the main geometric and operational parameters are taken into account in addition to the specific speed and diameter, to guide a fan designer toward the correct choice of parameters such as hub solidity, blade number, hub-to-tip ratio (HR). This multidimensional chart was built using performance data derived from a quasi-3D in-house software for axisymmetric blade analysis and then explored by means of machine learning techniques suitable for big data analysis. Principal component analysis (PCA) and projection to latent structure (PLS) allowed finding optimal values of the main geometric parameters required by each specific speed/specific diameter pair.

A Multi-Dimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence Based Meta-Models

2019 ◽  
Author(s):  
Gino Angelini ◽  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Lorenzo Tieghi
Keyword(s):  
Axial Flow ◽  
Principal Component ◽  
Correct Choice ◽  
Machine Learning Techniques ◽  
Operational Parameters ◽  
Kinematic Parameters ◽  
Fan Performance ◽  
Learning Techniques ◽  
Optimal Values ◽  
Specific Speed

Abstract The main intent of this work is the exploration of the rotor-only fan design-space to identify correlations between fan performance and enriched geometric and kinematic parameters. In particular, the aim is to derive a multidimensional “Balje chart”, where the main geometric and operational parameters are taken into account in addition to the specific speed and diameter, to guide a fan designer towards the correct choice of parameters such as hub solidity, blade number, hub-to-tip ratio. This multidimensional chart was built using performance data derived from a quasi-3D in-house software for axisymmetric blade analysis and then explored by means of machine learning techniques suitable for big data analysis. Principal Component Analysis (PCA) and Projection to Latent Structure (PLS) allowed finding optimal values of the main geometric parameters required by each specific speed/specific diameter pair.

scholarly journals Making Data and Workflows Findable for Machines

2020 ◽  
Vol 2 (1-2) ◽  
pp. 40-46 ◽  
Author(s):  
Tobias Weigel ◽  
Ulrich Schwardmann ◽  
Jens Klump ◽  
Sofiane Bendoukha ◽  
Robert Quick
Keyword(s):  
Workflow Management ◽  
Machine Learning Techniques ◽  
Data Generation ◽  
Data Types ◽  
Multiple Stakeholders ◽  
Data Services ◽  
Learning Techniques ◽  
Research Administrators ◽  
Data Infrastructures ◽  
Data Objects

Research data currently face a huge increase of data objects with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.

scholarly journals Learning to Classify Documents According to Formal and Informal Style

2012 ◽  
Vol 8 ◽  
Author(s):  
Fadi Abu Sheikha ◽  
Diana Inkpen
Keyword(s):  
Computational Linguistics ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Medical Texts ◽  
Learning Techniques ◽  
Sentence Level ◽  
Vector Machines ◽  
Different Sources ◽  
Document Level

This paper discusses an important issue in computational linguistics: classifying texts as formal or informal style. Our work describes a genre-independent methodology for building classifiers for formal and informal texts. We used machine learning techniques to do the automatic classification, and performed the classification experiments at both the document level and the sentence level. First, we studied the main characteristics of each style, in order to train a system that can distinguish between them. We then built two datasets: the first dataset represents general-domain documents of formal and informal style, and the second represents medical texts. We tested on the second dataset at the document level, to determine if our model is sufficiently general, and that it works on any type of text. The datasets are built by collecting documents for both styles from different sources. After collecting the data, we extracted features from each text. The features that we designed represent the main characteristics of both styles. Finally, we tested several classification algorithms, namely Decision Trees, Naïve Bayes, and Support Vector Machines, in order to choose the classifier that generates the best classification results.

scholarly journals Machine Learning: The New Language for Applications

2019 ◽  
Vol 8 (4) ◽  
pp. 411 ◽  
Author(s):  
Venkatsai Siddesh Padala ◽  
Kathan Gandhi ◽  
Pushpalatha Dasari
Keyword(s):  
Machine Learning ◽  
Technological Development ◽  
Cost Benefit ◽  
Risk Tolerance ◽  
Machine Learning Techniques ◽  
Major Influence ◽  
Future Directions ◽  
Learning Techniques ◽  
Financial Issues ◽  
Tolerance Cost

<p>Machine learning and artificial intelligence are becoming a major influence in various research and commercial fields. This review attempts to explain machine learning techniques and applications in various fields. Challenges and future directions are also proposed, including data analysis suggestions, effective algorithms based on the situation, industrial implementation, organization’s risk tolerance, cost-benefit comparisons and the future of machine learning techniques. Applications discussed in this paper range from technological development and health care to financial issues and sports analytics.</p>

scholarly journals Disease prediction in big data healthcare using extended convolutional neural network techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 85
Author(s):  
Asadi Srinivasulu ◽  
Asadi Pushpa
Keyword(s):  
Machine Learning Techniques ◽  
Statistical Computing ◽  
Disease Prediction ◽  
Prediction System ◽  
Reliable Prediction ◽  
Learning Techniques ◽  
The World ◽  
Different Sources ◽  
Medical Dataset ◽  
Nerve Disease

<span>Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes.  The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.</span>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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