�ݺ�ߣshows by User: Marwa

�ݺ�ߣshows by User: Marwa_Alrikaby / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Marwa_Alrikaby / Tue, 12 May 2015 14:46:36 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Marwa_Alrikaby DNA Compression (Encoded using Huffman Encoding Method) /slideshow/dna-compression/48052386 dnacompression-150512144637-lva1-app6892
DNA compression basics for analyzing DNA sequences and using compression method as a post-processing step to compress the DNA sequence size.]]>
DNA compression basics for analyzing DNA sequences and using compression method as a post-processing step to compress the DNA sequence size.]]> Tue, 12 May 2015 14:46:36 GMT /slideshow/dna-compression/48052386 Marwa_Alrikaby@slideshare.net(Marwa_Alrikaby) DNA Compression (Encoded using Huffman Encoding Method) Marwa_Alrikaby DNA compression basics for analyzing DNA sequences and using compression method as a post-processing step to compress the DNA sequence size. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dnacompression-150512144637-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> DNA compression basics for analyzing DNA sequences and using compression method as a post-processing step to compress the DNA sequence size.

DNA Compression (Encoded using Huffman Encoding Method) from Marwa Al-Rikaby

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0 Intelligent Text Document Correction System Based on Similarity Technique /slideshow/intelligent-text-document-correction-system-based-on-similarity-technique/46504305 intelligenttextdocumentcorrectionsystembasedonsimilaritytechnique-150331125519-conversion-gate01
Automatic text correction is one of the human-computer interaction challenges. It is directly interposed with several application areas like post handwritten text digitizing correction or indirectly such as user's queries correction before applying a retrieval process in interactive databases. Automatic text correction process passes through two major phases: error detection and candidates suggestion. Techniques for both phases are categorized into: Procedural and statistical. Procedural techniques are based on using rules to govern texts acceptability, including Natural Language Processing Techniques. Statistical techniques, on the other hand, are dependent on statistics and probabilities collected from large corpus based on what is commonly used by humans. In this work, natural language processing techniques are used as bases for analysis and both spell and grammar acceptance checking of English texts. A prefix dependent hash-indexing scheme is used to shorten the time of looking up the underhand dictionary which contains all English tokens. The dictionary is used as a base for the error detection process. Candidates generation is based on calculating source token similarity, measured using an improved Levenshtein method, to the dictionary tokens and ranking them accordingly; however this process is time extensive, therefore, tokens are divided into smaller groups according to spell similarity in such a way keeps the random access availability. Finally, candidates suggestion involves examining a set of commonly committed mistakes related features. The system selects the optimal candidate which provides the highest suitability and doesn't violate grammar rules to generate linguistically accepted text. Testing the system accuracy showed better results than Microsoft Word and some other systems. The enhanced similarity measure reduced the time complexity to be on the boundaries of the original Levenshtein method with an additional error type discovery.]]>
Automatic text correction is one of the human-computer interaction challenges. It is directly interposed with several application areas like post handwritten text digitizing correction or indirectly such as user's queries correction before applying a retrieval process in interactive databases. Automatic text correction process passes through two major phases: error detection and candidates suggestion. Techniques for both phases are categorized into: Procedural and statistical. Procedural techniques are based on using rules to govern texts acceptability, including Natural Language Processing Techniques. Statistical techniques, on the other hand, are dependent on statistics and probabilities collected from large corpus based on what is commonly used by humans. In this work, natural language processing techniques are used as bases for analysis and both spell and grammar acceptance checking of English texts. A prefix dependent hash-indexing scheme is used to shorten the time of looking up the underhand dictionary which contains all English tokens. The dictionary is used as a base for the error detection process. Candidates generation is based on calculating source token similarity, measured using an improved Levenshtein method, to the dictionary tokens and ranking them accordingly; however this process is time extensive, therefore, tokens are divided into smaller groups according to spell similarity in such a way keeps the random access availability. Finally, candidates suggestion involves examining a set of commonly committed mistakes related features. The system selects the optimal candidate which provides the highest suitability and doesn't violate grammar rules to generate linguistically accepted text. Testing the system accuracy showed better results than Microsoft Word and some other systems. The enhanced similarity measure reduced the time complexity to be on the boundaries of the original Levenshtein method with an additional error type discovery.]]> Tue, 31 Mar 2015 12:55:19 GMT /slideshow/intelligent-text-document-correction-system-based-on-similarity-technique/46504305 Marwa_Alrikaby@slideshare.net(Marwa_Alrikaby) Intelligent Text Document Correction System Based on Similarity Technique Marwa_Alrikaby Automatic text correction is one of the human-computer interaction challenges. It is directly interposed with several application areas like post handwritten text digitizing correction or indirectly such as user's queries correction before applying a retrieval process in interactive databases. Automatic text correction process passes through two major phases: error detection and candidates suggestion. Techniques for both phases are categorized into: Procedural and statistical. Procedural techniques are based on using rules to govern texts acceptability, including Natural Language Processing Techniques. Statistical techniques, on the other hand, are dependent on statistics and probabilities collected from large corpus based on what is commonly used by humans. In this work, natural language processing techniques are used as bases for analysis and both spell and grammar acceptance checking of English texts. A prefix dependent hash-indexing scheme is used to shorten the time of looking up the underhand dictionary which contains all English tokens. The dictionary is used as a base for the error detection process. Candidates generation is based on calculating source token similarity, measured using an improved Levenshtein method, to the dictionary tokens and ranking them accordingly; however this process is time extensive, therefore, tokens are divided into smaller groups according to spell similarity in such a way keeps the random access availability. Finally, candidates suggestion involves examining a set of commonly committed mistakes related features. The system selects the optimal candidate which provides the highest suitability and doesn't violate grammar rules to generate linguistically accepted text. Testing the system accuracy showed better results than Microsoft Word and some other systems. The enhanced similarity measure reduced the time complexity to be on the boundaries of the original Levenshtein method with an additional error type discovery. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intelligenttextdocumentcorrectionsystembasedonsimilaritytechnique-150331125519-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Automatic text correction is one of the human-computer interaction challenges. It is directly interposed with several application areas like post handwritten text digitizing correction or indirectly such as user's queries correction before applying a retrieval process in interactive databases. Automatic text correction process passes through two major phases: error detection and candidates suggestion. Techniques for both phases are categorized into: Procedural and statistical. Procedural techniques are based on using rules to govern texts acceptability, including Natural Language Processing Techniques. Statistical techniques, on the other hand, are dependent on statistics and probabilities collected from large corpus based on what is commonly used by humans. In this work, natural language processing techniques are used as bases for analysis and both spell and grammar acceptance checking of English texts. A prefix dependent hash-indexing scheme is used to shorten the time of looking up the underhand dictionary which contains all English tokens. The dictionary is used as a base for the error detection process. Candidates generation is based on calculating source token similarity, measured using an improved Levenshtein method, to the dictionary tokens and ranking them accordingly; however this process is time extensive, therefore, tokens are divided into smaller groups according to spell similarity in such a way keeps the random access availability. Finally, candidates suggestion involves examining a set of commonly committed mistakes related features. The system selects the optimal candidate which provides the highest suitability and doesn't violate grammar rules to generate linguistically accepted text. Testing the system accuracy showed better results than Microsoft Word and some other systems. The enhanced similarity measure reduced the time complexity to be on the boundaries of the original Levenshtein method with an additional error type discovery.

Intelligent Text Document Correction System Based on Similarity Technique from Marwa Al-Rikaby

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0 Model checking of time petri nets /slideshow/model-checking-of-time-petri-nets-18296318/18296318 modelcheckingoftimepetrinets-130406080834-phpapp02
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]]> Sat, 06 Apr 2013 08:08:34 GMT /slideshow/model-checking-of-time-petri-nets-18296318/18296318 Marwa_Alrikaby@slideshare.net(Marwa_Alrikaby) Model checking of time petri nets Marwa_Alrikaby <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modelcheckingoftimepetrinets-130406080834-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br>

Model checking of time petri nets from Marwa Al-Rikaby

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0 Output primitives computer graphics c version /Marwa_Alrikaby/output-primitives-computer-graphics-c-version outputprimitives-computergraphicscversion-130320092516-phpapp01
how to generate output primitives in computer graphics]]>
how to generate output primitives in computer graphics]]> Wed, 20 Mar 2013 09:25:16 GMT /Marwa_Alrikaby/output-primitives-computer-graphics-c-version Marwa_Alrikaby@slideshare.net(Marwa_Alrikaby) Output primitives computer graphics c version Marwa_Alrikaby how to generate output primitives in computer graphics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/outputprimitives-computergraphicscversion-130320092516-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> how to generate output primitives in computer graphics

Output primitives computer graphics c version from Marwa Al-Rikaby

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0 https://cdn.slidesharecdn.com/profile-photo-Marwa_Alrikaby-48x48.jpg?cb=1555503483 https://cdn.slidesharecdn.com/ss_thumbnails/dnacompression-150512144637-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/dna-compression/48052386 DNA Compression (Encod... https://cdn.slidesharecdn.com/ss_thumbnails/intelligenttextdocumentcorrectionsystembasedonsimilaritytechnique-150331125519-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intelligent-text-document-correction-system-based-on-similarity-technique/46504305 Intelligent Text Docum... https://cdn.slidesharecdn.com/ss_thumbnails/modelcheckingoftimepetrinets-130406080834-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/model-checking-of-time-petri-nets-18296318/18296318 Model checking of time...