A RULE-BASED CLASSIFICATION OF LARYNGOPATHIES BASED ON SPECTRUM DISTURBANCE ANALYSIS - An Exemplary Study

2012 ◽  
Keyword(s):  
Rule Based ◽  
Disturbance Analysis

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

Fuzzy rule based classification of polarimetric SAR data

2002 ◽  
Vol 6 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Martin Hellmann ◽  
Gunther Jäger
Keyword(s):  
Fuzzy Rule ◽  
Polarimetric Sar ◽  
Rule Based ◽  
Sar Data

Rule based classification of neurodegenerative diseases using data driven gait features

2018 ◽  
Vol 9 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Kartikay Gupta ◽  
Aayushi Khajuria ◽  
Niladri Chatterjee ◽  
Pradeep Joshi ◽  
Deepak Joshi
Keyword(s):  
Neurodegenerative Diseases ◽  
Data Driven ◽  
Rule Based ◽  
Gait Features ◽  
Using Data

scholarly journals Rule based Classification of Graph Theory Concepts by Use Case Analysis

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
Anuja Bokhare ◽  
P. S. Metkewar ◽  
R. S. Walse
Keyword(s):  
Graph Theory ◽  
Case Analysis ◽  
Use Case ◽  
Rule Based

Rule-based algorithm for the classification of sitting postures in the sagittal plane from the Cardiff Body Match measurement system

2013 ◽  
Vol 38 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Janusz Kulon ◽  
Adam Partlow ◽  
Colin Gibson ◽  
Ian Wilson ◽  
Steven Wilcox
Keyword(s):  
Measurement System ◽  
Sagittal Plane ◽  
Rule Based

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

scholarly journals Application of Fuzzy Rule based System for Highway Research Board Classification of Soils

2020 ◽  
Vol 10 (2) ◽  
pp. 1-14
Author(s):  
Sujatha A ◽  
L Govindaraju ◽  
N Shivakumar
Keyword(s):  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based System ◽  
Highway Research
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