Search Results - (( parallel classification rules algorithm ) OR ( using vectorisation using algorithm ))

3D silhouette rendering algorithms using vectorisation technique from Kedah topography map by Che Mat, Ruzinoor, Nordin, Norani

“…The vectorisation software has been used for producing these data. …”
Get full text
Get full text
Get full text

Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…Based on the experiment results, the classification method using the TIP approach has successfully performed rules generation and classification tasks as required during a classification operation. …”
Get full text
Get full text

A DNA-dynamic permutation-diffusion algorithm for image encryption using scaling chaotification models and advanced DNA operations by Alkawsi, Gamal, Khairullah, Mustafa Kamil, Baharuddin, Mohd Zafri, Thabit, Reema, Alomari, Mohammad Ahmed, A. Saif, Faten

“…To evaluate the proposed algorithm, extensive experiments have been conducted, and results have been compared with the latest encryption algorithms. …”
Get full text
Get full text
Get full text
Get full text

An Automated System For Classifying Conference Papers by Ngan, Seon Choon Han

“…The Knowledge Discovery in Databases (KDD) process was followed as a formal data mining methodology where 1000 AI conference papers were carefully collected, pre-processed and transformed to numerical representations through TF-IDF vectorisation. A randomised stratified 5- fold cross validation was then applied on several data mining algorithms and evaluated using the F-measure as a metric. …”
Get full text
Get full text

Projecting named entity tags from a resource rich language to a resource poor language by Zamin, Norshuhani, Oxley, Alan, Abu Bakar, Zainab

“…Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. …”
Get full text
Get full text
Get full text

Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

“…This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. …”
Get full text
Get full text

Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

“…This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. …”
Get full text
Get full text

Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

“…This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. …”
Get full text
Get full text