The Incidence of Oral and Oropharyngeal Cancers in Betel Quid-Chewing Populations in South Myanmar Rural Areas

Oral cancer is a very common disease in South and Southeast Asia. Betel quid (BQ)- chewing and tobaccosmoking habits are etiological factors for oral cancer patients in these regions. We conducted an oral cancer screening in BQ-chewing endemic rural areas in South Myanmar for the early detection of oral cancer in BQ-chewing and smoking individuals. We examined 105 subjects who were at high risk of oral cancer due to their oral habits (BQ users and/or smokers). Three carcinoma cases were detected, and there were 8 dysplasia cases. The carcinoma detection rate was 2.9%, and the carcinoma and precancerous lesion detection rate was 10.5%. In Myanmar, oral cancer screening has been conducted sporadically on a voluntary basis, and nationwide surveys have never been performed. There are also few reports of oral cancer screening for high-risk groups among the general population in Myanmar. Our present findings highlight the need for further screening and surveys. Education on betel quid chewing- and tobacco- related oral diseases and screening for the early detection of oral cancer are of the utmost importance in the control and prevention of oral cancer

Lexicon Organisation and Contextual Methods for Online Handwritten Pitman's Shorthand Recognition

This research investigates novel solutions to the computer transcription Of handwritten Pitman's Shorthand as a rapid means of text entry (up to 100 words per minute) into today's pen-based handheld devices. Two mathematical models are developed in this work. The first deals with high level phoneticbased translation, while the second specifically concerns low level primitive-based translation. Both models are closely related to the lexicon organisation and contextual processing for online handwritten Pitman's Shorthand recognition. A number of research issues that arise from interpreting handwritten Pitman's Shorthand strokes ofdigital ink as text are addressed including: (a) a feasibility study into improving a conventional phonetic-based transliteration approach to advance word recognition; (b) an investigation into new Bayesian Network modelling of strokes and their relationships in order to solve the problem ofgeometric variations and vowel ambiguities of handwritten Pitman's Shorthand; (c) generation of a new machine-readable Pitman's Shorthand lexicon to facilitate the direct transcription of geometric features of Pitman's Shorthand into English text; (d) analysis of the impact of statistical language modelling in handwriting phrase recognition; (e) and a discussion of the graphical user interface issues in relation to the development of a commercial prototype from the frame ofreference ofthis research. The research has been carried out in close cooperation with Nanyang Technology University (NTU) in Singapore. The system is currently under a final evaluation in terms of its recognition accuracy, as well as its potential to be introduced as a commercially viable fast text input system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of Paddy Information Helpdesk System by Using Case Based Reasoning

Nowadays, helpdesk are especially importantin providing technical information about procedures andregulations , such as answering questions about a specificproblem. Case-Based Reasoning is one of the mostsuccessful methodologies copying with the bottleneck ofknowledge discovery in the helpdesk applications. Thesystem store in the case form , the experience andknowledge of the expert to solve for reasoning of theproblem . This paper proposed a CBR based Helpdesksystem. The terms concerning paddy informationpredefined in Case-based and terms are used in keywordsearching. The aim is to provide the users with theknowledge of rice production and help user to get solutionof their problem concerning paddy information. Agricultureis the backbone of Myanmar’s economy. In order to shareand reuse farming knowledge and experience amongfarmers and adviser we developed agriculture Case Base(ACB)

Evaluation of Feature Sets in the Post Processing of Handwritten Pitman's Shorthand

Innovative ways to rapidly input text becomes essential in today's world of mobile computing. The paper discusses the computer transcription of handwritten Pitman shorthand as a means of rapid text entry to pen-based computers, particularly from the aspect of linguistic post processing. Feature-to-phoneme conversion is introduced as the first stage of a text interpreter and the application of various production rules based on different pattern structures is discussed. It demonstrates that phoneme ordering is compulsory in dictionary-based transcription and the use of an approximate pattern-matching algorithm resolves the problem of recognition confusion between similar patterns. Experimental results are promising and demonstrate an overall accuracy of 84%

Segmentation and recognition of phonetic features in handwritten Pitman shorthand

There is a wish to be able to enter text into mobile computing devices at the speed of speech. Only handwritten shorthand schemes can achieve this data recording rate. A new, overall solution to the segmentation and recognition of phonetic features in Pitman shorthand is proposed in this paper. Approaches to the recognition of consonant outlines, vowel and diphthong symbols and shortforms, which are different components of Pitman shorthand, are presented. A new rule is introduced to solve the issue of smooth junctions in the consonant outlines which was normally the bottleneck for recognition. Experiments with a set of 1127 consonant outlines, 2039 vowels and diphthongs and 841 shortforms from three shorthand writers have demonstrated that the proposed solution is quite promising. The recognition accuracies for consonant outlines, vowels and diphthongs, and shortforms achieved 75.33%, 96.86% and 91.86%, respectively. From the evaluation of 461 outlines with smooth junction, the introduction of the new rule has a great positive effect on the performance of the solution. The recognition accuracy of smooth junction improves from 37.53% to 93.41% given a writing time increase of 14.42%

Critical Technological Issues of Commercializing a Pitman Shorthand Recognition System

Pitman shorthand has been shown to possess unique advantages as a means of fast text entry on hand-held devices. Based on previous academic achievements, this paper discusses critical issues in the commercialization of Pitman shorthand online reorganization system from the technological perspective. In this paper, critical technological issues are classified into two aspects. These critical issues are illustrated by an entire online simulating system which realizes all core functions from input, through segmentation, recognition to transcription of Pitman shorthand

Transliteration of Online Handwritten Phonetic Pitman's Shorthand with the Use of a Bayesian Network

This paper presents a detailed view of a novel solution to the computer transcription of handwritten Pitman's shorthand as a means of rapid text entry (up to 100 words per minute) into today's handheld devices with the use of a Bayesian network representation. Detailed design considerations of Bayesian network based shorthand outline models, including hypothesis of missing vowel components occurring in speed writing and unclear thickness and length of electrical pen-strokes are presented, along with graphical examples. Although Pitman's shorthand is written phonetically, our outline models are also based on low-level geometric attributes rather than phonetic attributes with the intention of coping with the unique features of handwritten Pitman's shorthand. The experimental results indicate an average accuracy of 92.86%, which is a marked improvement over previous applications of the same framework

Post-processing of Handwritten Pitman's Shorthand Using Unigram and Heuristic Approaches

The computer transcription of handwritten Pitman's shorthand has enormous potential as a means of rapid text entry to today's handheld devices. Recognition errors caused in pattern segmentation and classification raises the incidence of ambiguous interpretation in existing systems and the paper proposes a well-established unigram technique and an efficient heuristic method to reduce ambiguity in a linguistic post processor. Heuristics applied in our transcription system are: - firstly, incorporating visual stimulus as used by human readers; secondly, applying knowledge of the most common words of Pitman shorthand; and finally, adding knowledge of collocation. An experiment using a phonetic Lexicon of 5000 entries shows the distribution of ambiguity in a shorthand lexicon due to the similarity of outlines' and estimates the transcription accuracy of 94%

On-line Recognition of Pitman Shorthand for Fast Mobile Text Entry

A novel solution to on-line Pitman shorthand recognition for high speed text input (> 120wpm) to computer and mobile devices such as PDA's is proposed in this paper. Approaches to the recognition of three major components of Pitman shorthand - consonant outlines, vowel and diphthong symbols and are described in detail. Evaluation shows that the proposed approach achieved 84.4% recognition accuracy

Post Processing of Handwritten Phonetic Pitman's Shorthand Using a Bayesian Network Built on Geometric Attributes

In this paper, we introduce a new approach to the computer transcription of handwritten Pitman shorthand as a rapid means of text entry (up to 100 words per minute) into today's handheld devices, almost at the rate of speech. It is different from previous applications of the same framework from two aspects: - firstly, a novel idea of using geometric attributes other than phonetic attributes in the abstraction of a phonetic Pitman's shorthand lexicon is proposed. Secondly, a Bayesian network representation for the organisation of shorthand-outline models is introduced, in which natural variability of Pitman shorthand is defined via different nodes and links. Using a probabilistic Bayesian network, the system shows a noticeable robustness not only in transcribing a variety of genuine handwriting, but also in estimating missing vowel components that may have been omitted in speed writing. The accuracy of the new approach (92.86%) is a considerable improvement over previous applications