Mohamed Elhadi

Part of Speech (POS) Tag Sets Reduction and Analysis Using Rough Set Techniques

Springer eBooks, 2009

The motivation behind this work stems from an earlier work where text was transformed into string... more

Refinements of Longest Common Subsequence algorithm

Abstract-This paper reports on experiments performed to investigate possible improvements of the ... more

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

International journal of intelligence science, 2017

This paper investigates a procedure developed and reports on experiments performed to studying th... more This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text's sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet; and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then recycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.

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Arabic News Articles Classification Using Vectorized-Cosine Based on Seed Documents

Journal of Advances in Computer Engineering and Technology, May 1, 2019

Security is a vital issue in the usage of Automated Teller Machine (ATM) for cash, cashless and m... more Security is a vital issue in the usage of Automated Teller Machine (ATM) for cash, cashless and many off the counter banking transactions. Weaknesses in the use of ATM machine could not only lead to loss of customer's data confidentiality and integrity but also breach in the verification of user's authentication. Several challenges are associated with the use of ATM smart card such as: card cloning, card skimming, cost of issuance and maintenance. In this paper, we present secure bio-cryptographic authentication system for cardless ATM using enhanced fingerprint biometrics trait and encrypted Personal Identification Number (PIN). Fingerprint biometrics is used to provide automatic identification/verification of a legitimate customer based on unique feature possessed by the customer. Log-Gabor filtering algorithm was used for enhancing low image quality and effect of noise on feature extracted from customer's fingerprint minutiae. Truncated SHA 512/256 hash algorithm was used to secure the integrity and confidentiality of the PIN from sniffers and possible adversary within the channel of remote ATM banking transactions. Performance evaluation was carried out using False Acceptance Rate (FAR), False Rejection Rate (FRR) metrics and Collision Attack was performed on the Truncated SHA-512/256 hashed data (PIN). Results of the system performance shows Genuine Acceptance Rate (1-FRR) of 97.5% to 100%, Equal Error Rate of 0.0015% and Collision Attack carried out on the encrypted PIN message digest gave an unsuccessful attack. Therefore, the results of performance evaluation show the applicability of the developed system for secure cardless ATM transaction.

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A Rough Sets Approach to the Identification and Analysis of Factors Affecting Biological Control of Leafy Spurge

Springer eBooks, Aug 5, 2008

This paper constitutes an account of the authors&#x27; experiences and a presentation of resu... more

Use of text syntactical structures in detection of document duplicates

Using Text's Terms and Syntactical Properties for Document Similarity

International journal of intelligent information systems, 2016

This paper reports on experiments performed to investigate the use of syntactical structures of s... more This paper reports on experiments performed to investigate the use of syntactical structures of sentences combined with sentences' terms for document similarity calculation. The document's sentences were first converted into ordered Part of Speech (POS) tags that were then fed into the Longest Common Subsequence (LCS) algorithm to determine the size and count of the LCSs found when comparing the document sentence by sentence. As a first stage, these syntactical features of the text were used as a structural representation of the document's text. However, the produced strings of tags not only work as text representative but also provide for text size reduction. This improves the processing efficiency of comparing the document's representative strings using the LCS. A score is generated by computing an accumulative value based on the number of the LCSs found. In the second stage, documents that score well in the first stage are subjected to further comparison using the actual words of the sentences (content) in a sentence by sentence fashion. An overall final is generated as a measure of similarity using the common words (accumulated for the whole document) and the total number of LCSs from the first step. Experiments were done on two different corpora. Results obtained have showed the utility of the proposed procedure in calculating similarities between written documents. The overall discrimination power was maintained while the size of the documents was reduced using only a representative of the document based on the tagged string.

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Arabic Text Copy Detection using Full, Reduced and Unique Syntactical Structures

International journal of computer applications, Nov 17, 2016

This paper reports on work performed to investigate the use of a combined Part of Speech (POS) ta... more This paper reports on work performed to investigate the use of a combined Part of Speech (POS) tagging and a minimum edit operations algorithm to determine the level of similarity between pairs of Arabic text documents. The level of similarity can be used as an indication of duplication in full or in part of the document's content. Text is first converted into POS tags that are then fed to the string similarity algorithm to determine the similarity of pairs of documents. A normalized score is calculated and used to rank documents. Documents ranked higher than some selected threshold are considered similar and can be near or complete duplicate. The performed experiments compare results based on the use of a set of selected common subsequences that are the results of translation of text into a sequence of syntactical units. The strings are first produced using full-text (FULL). These are further refined to produce a REDUCED; where repeated consecutive characters are reduced to a single character and a number, and more refined to produce a UNIQUE string; where all repeating characters are replaced by a single character. Syntactical features of the text were used as a structural representation of the documents' content. Results obtained from the experiments using the FULL, the REDUCED and the UNIQUE POS-strings showed a clear advantage over the use of the plain text in terms of reduced string size while maintaining the same discrimination power. In particular the unique (most-reduced) string has shown quite comparable results to the reduced, the full and the actual text string.

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Detection of Duplication in Documents and WebPages Based Documents Syntactical Structures through an Improved Longest Common Subsequence

International journal of information processing and management, Jul 31, 2010

This paper reports on experiments performed to investigate the use of a combined Part of Speech (... more This paper reports on experiments performed to investigate the use of a combined Part of Speech (POS) and an improved Longest Common Subsequence (LCS) in the analysis and calculation of similarity between texts. The text's syntactical structures were used as a representation for the documents. An improved LCS algorithm was applied to such a representation in order to compare and rank the documents according to the similarity of their representative strings. The approach was applied in the detection of duplicate documents within a corpus, and in the filtering of search engine results. Obtained results were encouraging.

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A Grammatically and Structurally Based Part of Speech (Pos) Tagger for Arabic Language

Zenodo (CERN European Organization for Nuclear Research), Nov 18, 2022

In this paper we report on an experimental syntactically and morphologically driven rule-based Ar... more In this paper we report on an experimental syntactically and morphologically driven rule-based Arabic tagger. The tagger is developed using Arabic language grammatical rules and regulations. The tagger requires no pre-tagged text and is developed using a primitive set of lexicon items along with extensive grammatical and structural rules. It is tested and compared to Stanford tagger both in terms of accuracy and performance (speed). Obtained results are quite comparable to Stanford tagger performance with marginal difference favoring the developed tagger in accuracy with huge difference in terms of speed of execution. The newly developed tagger named MTE Tagger has been tested and evaluated. For the evaluation of its accuracy of tagging, a set of Arabic text was manually prepared and annotated. Compared to Stanford tagger, the MTE tagger performance was quite comparable. The developed tagger makes use of no pre-annotated datasets, except of some simple lexicon consisting of list of words representing closed word types like demonstrative nouns or pronouns or some particles. For the purpose of evaluation of the new tagger, it was run on multiple datasets and results were compared to those of Stanford tagger. In particular, both taggers (the MTE and the Stanford) were run on a set of 1226 sentences with close to 20,000 tokens that was human annotated and verified to serve as testbed. The results were very encouraging where in both test runs, the MTE tagger outperformed the Stanford tagger in terms of accuracy of 87.88% versus 86.67% for the Stanford tagger. In terms of speed of tagging and in comparison Stanford tagger, MTE Taggers' performance was on average 1:50. More improved accuracy is possible in future work as the set of rules are further optimized, integrated and more of Arabic language properties such as end of word discretization are used.

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Arabic Minimal Pairs Word Detection and Disambiguation

International journal of computer applications, Oct 20, 2022

This work is an attempt to solve a common writing problem and pitfall for Arabic language. The pr... more This work is an attempt to solve a common writing problem and pitfall for Arabic language. The problem involves words that contain letters such as (among many others) ‫ظ‬ THA and ‫ع‬ DHA. The problem involves terms that are formally minimal pairs (more precisely near minimal pairs), near homographs (homophones), it requires determination of the right term and resolutions of created ambiguities. It is not just embarrassing to the authors, but in many situations, it results in wrong usage of words and consequently can lead to an ambiguous sentence(s). It becomes difficult to interpret such words or sentences, especially by computer involved in applications such as information retrieval, language translations and summarizations. A very amalgamated determination process was suggested that is comprised of multiple stages of feature selection, classifier selection and classification. A sample set of terms selected with a reasonable success rate making classifiers accuracies vary, but overall, all terms are reasonably accurate and close in values. MCC values are also variable with some reasonable good ranges. It is notable that some classifiers did not converge and the MCC is set to zero. Considering results obtained from classifiers with highest training rates and those with highest MCC, It can be easily concluded that Random Forest algorithm is the champion with high accuracy in most of the terms, and many times very close to the highest rate, classifiers were close. It also scored the highest for the mean values calculation across all terms. We can easily say that a combination of extracted features from a corpus along with machine learning classification techniques, the problem can be solved with high accuracy.

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Use of Rough Sets for the Identification and Analysis of Dependencies and Relationships in Biocontrol of Weeds

This paper is a presentation of results obtained in exper-iments in which rough set theory&#x... more This paper is a presentation of results obtained in exper-iments in which rough set theory&#x27;s methods and techniques were applied in the identification and analysis of data de-pendencies and relationships on an empirical data. The data used pertains to the biological control of the Leafy Spurge ...

Using statutes-based ir drive legal cbr

Applied Artificial Intelligence, Jul 1, 2001

Page 1. Applied ArtiŪcial Intelligence, 15:587ą600, 2001 Copyright Đ 2001 Taylor &amp; Franci... more

Duplicate Detection in Documents and WebPages Using Improved Longest Common Subsequence and Documents Syntactical Structures

Abstractthis paper reports on experiments performed to investigate the use of a combined Part of... more

A Grammatically and Structurally Based Part of Speech (POS) Tagger for Arabic Language

International Journal on Natural Language Computing

In this paper we report on an experimental syntactically and morphologically driven rule-based Ar... more In this paper we report on an experimental syntactically and morphologically driven rule-based Arabic tagger. The tagger is developed using Arabic language grammatical rules and regulations. The tagger requires no pre-tagged text and is developed using a primitive set of lexicon items along with extensive grammatical and structural rules. It is tested and compared to Stanford tagger both in terms of accuracy and performance (speed). Obtained results are quite comparable to Stanford tagger performance with marginal difference favoring the developed tagger in accuracy with huge difference in terms of speed of execution. The newly developed tagger named MTE Tagger has been tested and evaluated. For the evaluation of its accuracy of tagging, a set of Arabic text was manually prepared and annotated. Compared to Stanford tagger, the MTE tagger performance was quite comparable. The developed tagger makes use of no pre-annotated datasets, except of some simple lexicon consisting of list of ...

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An experimental analysis of rough sets theory as applied to biological control of weeds

ABSTRACT Includes appendices. Thesis (M. Sc.)--University of Regina, 1991. Includes bibliographic... more

Use of Rough Sets for the Identification and Analysis of Dependencies and Relationships in Biocontrol of Weeds

This paper is a presentation of results obtained in experiments in which rough set theory's metho... more This paper is a presentation of results obtained in experiments in which rough set theory's methods and techniques were applied in the identification and analysis of data dependencies and relationships on an empirical data. The data used pertains to the biological control of the Leafy Spurge weed in the prairies of Western Canada. The project used an agent beetle known as Aphthona nigriscutis (A. n.). The rough set theory was applied to the data in order to identify and analyze the different factors affecting the success of the biological control of the host weed. Discovery and confirmation of meaningful patterns and the computation of a set of rules for the critical application of agent beetle in the control of the host weed were very encouraging.

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Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

International Journal of Intelligence Science, 2017

This paper investigates a procedure developed and reports on experiments performed to studying th... more This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text's sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet; and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then recycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.

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An IR-CBR approach to legal indexing and retrieval in bankruptcy law

Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99

Page 1. An IR-CBR Approach to Legal Indexing and Retrieval in Bankruptcy Law Mohamed T. Elhadi Co... more

Arabic News Articles Classification Using Vectorized-Cosine Based on Seed Documents

Besides for its own merits, text classification (TC) has become a cornerstone in many application... more Besides for its own merits, text classification (TC) has become a cornerstone in many applications. Work presented here is part of and a pre-requisite for a project we have overtaken to create a corpus for the Arabic text process. It is an attempt to create modules automatically that would help speed up the process of classification for any text categorization task. It also serves as a tool for the creation of Arabic text corpora. In particular, we create a text classification process for Arabic news articles downloaded from web news portals and sites. The suggested procedure is a pilot project that uses some human predefined set of documents that have been assigned to some subjects or categories. A vectorized Term Frequency, Inverse Document Frequency (TF-IDF) based information processing was used for the initial verification of the categories. The resulting validated categories used to predict categories for new documents. The experiment used 1000 initial documents pre-assigned in...

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