Classification of Pistachio Nuts Using a String Matching Technique

1996 ◽  
Vol 39 (3) ◽  
pp. 1197-1202 ◽  
Author(s):  
A. Ghazanfari ◽  
J. Irudayaraj
Keyword(s):  
String Matching ◽  
Pistachio Nuts ◽  
Matching Technique

scholarly journals <i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs

2021 ◽  
Vol 14 (11) ◽  
pp. 6711-6740
Author(s):  
Ranee Joshi ◽  
Kavitha Madaiah ◽  
Mark Jessell ◽  
Mark Lindsay ◽  
Guillaume Pirot
Keyword(s):  
Open Source ◽  
Mineral Exploration ◽  
String Matching ◽  
Drill Hole ◽  
Geological Survey ◽  
Database Structure ◽  
Automated Processing ◽  
Study Case ◽  
Textual Form

Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021​​​​​​​) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).

Application of a Multi-Structure Neural Network (MSNN) to Sorting Pistachio Nuts

1997 ◽  
Vol 08 (01) ◽  
pp. 55-61 ◽  
Author(s):  
Ahmad Ghazanfari ◽  
Anthony Kusalik ◽  
Joseph Irudayaraj
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Back Propagation ◽  
Classification Performance ◽  
Fourier Descriptor ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Pistachio Nuts ◽  
The Individual

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptor of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.

CLASSIFICATION OF CLOSED- AND OPEN-SHELL PISTACHIO NUTS USING VOICE-RECOGNITION TECHNOLOGY

2004 ◽  
Vol 47 (2) ◽  
pp. 659-664 ◽  
Author(s):  
A. E. Cetin ◽  
T. C. Pearson ◽  
A. H. Tewfik
Keyword(s):  
Voice Recognition ◽  
Open Shell ◽  
Pistachio Nuts

Pseudo 2D String Matching Technique for High Efficiency Screen Content Coding

2016 ◽  
Vol 18 (3) ◽  
pp. 339-350 ◽  
Author(s):  
Liping Zhao ◽  
Tao Lin ◽  
Kailun Zhou ◽  
Shuhui Wang ◽  
Xianyi Chen
Keyword(s):  
High Efficiency ◽  
String Matching ◽  
Screen Content ◽  
Matching Technique ◽  
Screen Content Coding
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