The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise t...The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.展开更多
Language plays a central role in how gender and sexuality are described. In Bangla or Bengali, physicians, when educating and counseling women patients, do not have a socially acceptable word for “vagina”. If langua...Language plays a central role in how gender and sexuality are described. In Bangla or Bengali, physicians, when educating and counseling women patients, do not have a socially acceptable word for “vagina”. If language is missing for female genitalia or important female sexual functions, could this absence reflect on the position of women in society, reproductive rights, and access to healthcare? Is there a relationship between language and the high rates of the gender-based cervical and breast cancers in some low and middle-income countries? This commentary examines scholarship on the topic of language, the female body, gender-based violence, disparities of healthcare for women, and the consequences of language on sexual attitudes and health.展开更多
We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuab...We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.展开更多
<strong>Background:</strong> Novel corona virus (SARS-Coronavirus-2 SARS-CoV-2) which emerged in China has spread to multiple countries rapidly. Little information is known about delayed viral clearance in...<strong>Background:</strong> Novel corona virus (SARS-Coronavirus-2 SARS-CoV-2) which emerged in China has spread to multiple countries rapidly. Little information is known about delayed viral clearance in mild to moderate COVID-19 pa-tients. As it is highly contagious, health care workers including physicians are high risk of being infected in hospital care. <strong>Case Report:</strong> A 37 years old Bangladeshi physician working in a paediatric unit of a medical college hos-pital with multiple co-morbidities, hypertension, diagnosed axial spondy-loarthropathy (ankylosing spondylitis) taking disease modifying anti rheu-matic drugs— DMARDs (Salfasalazine) from 2016 till now, chronic persis-tent bronchial asthma on medication developed sore throat, increasing breathlessness and cough admitted to his own hospital on 22 April, 2020. He had a history of contact with a relapse nephrotic syndrome (glomerulone-phritis) patient admitted with severe respiratory distress later confirmed as COVID-19 following RT PCR test on 14 April, 2020. After 3 days of contact with the patient, the physician also developed the symptoms mentioned above. The RT PCR test result of the physician came positive on 18 April, 2020. The physician primarily taken only azithromycin 500 mg once daily along with other regular drugs. On 5, 12 and 18 May, 2020, his sample was taken for re-test and came positive subsequently. After that he started Iver-mectin (0.15 mg/kg) once daily for 3 days and doxycycline 100 mg BD for 7 days. He gave samples again on 27 and 29 May, 2020 which were came nega-tive after 39 days. On full recovery he was discharged from hospital on day 40. We choose the patient because presence of co-morbidities may be asso-ciated with delayed viral clearance and physicians with co-morbidities working in a hospital have high risk of being infected.展开更多
When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People ...When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.展开更多
Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using ...Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.展开更多
At the invitation of CAFIU,a 14-member neighboring NGO delegation composed of representatives from Bangladesh,India,Indonesia and the Philippines visited China’s Beijing and Chengdu from July 7 to 16,2015.CAFIU Deput...At the invitation of CAFIU,a 14-member neighboring NGO delegation composed of representatives from Bangladesh,India,Indonesia and the Philippines visited China’s Beijing and Chengdu from July 7 to 16,2015.CAFIU Deputy Secretary-General Liu Kaiyang accompanied the delegation to Chengdu.As a CAFIU staff member,I had the honor to accompany the展开更多
In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically...In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar. Afterward, this language holds an important place because this language is spoken by 26 core people all over the world. As a result, it has taken a new method to summarize Bengali documents. The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary. Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review. We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement. Checking the similarity between two sentences in order to exclude one since it has less duplication. Hereby, we’ve taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax. In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents. According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score. It has been found that the proper pronoun replacement was 72%.展开更多
This article is aimed at describing a hybrid scheme for English to Bangla translation. The translated output in English scripts is useful for learning Bengali language. This is a significant contribution to Human Lang...This article is aimed at describing a hybrid scheme for English to Bangla translation. The translated output in English scripts is useful for learning Bengali language. This is a significant contribution to Human Language Technology generation also. About two hundred million people in West Bengal and Tripura (two states in India) and in Bangladesh (a country whose people speak and write Bangla as their first language). This proposed translator would benefit Bengalee society because rural people are not usually very conversant with English. The English to Bangla Translator is being enhanced. This system (English- Bangla-ANUBAD or EB-ANUBAD) takes a paragraph of English sentences as input sentences and produces equivalent Bangla sentences. EB-ANUBAD system is comprised of a preprocessor, morphological parser, semantic parser using English word ontology for context disambiguation, an electronic lexicon associated with grammatical information and a discourse processor, and also uses a lexical disambiguation analyzer. This system does not rely on a stochastic approach. Rather, it is based on a special kind of hybrid architecture of transformer and rule-based Natural Language Engineering (NLE) architectures along with various linguistic knowledge components of both English and Bangla.展开更多
基金MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.
文摘Language plays a central role in how gender and sexuality are described. In Bangla or Bengali, physicians, when educating and counseling women patients, do not have a socially acceptable word for “vagina”. If language is missing for female genitalia or important female sexual functions, could this absence reflect on the position of women in society, reproductive rights, and access to healthcare? Is there a relationship between language and the high rates of the gender-based cervical and breast cancers in some low and middle-income countries? This commentary examines scholarship on the topic of language, the female body, gender-based violence, disparities of healthcare for women, and the consequences of language on sexual attitudes and health.
文摘We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.
文摘<strong>Background:</strong> Novel corona virus (SARS-Coronavirus-2 SARS-CoV-2) which emerged in China has spread to multiple countries rapidly. Little information is known about delayed viral clearance in mild to moderate COVID-19 pa-tients. As it is highly contagious, health care workers including physicians are high risk of being infected in hospital care. <strong>Case Report:</strong> A 37 years old Bangladeshi physician working in a paediatric unit of a medical college hos-pital with multiple co-morbidities, hypertension, diagnosed axial spondy-loarthropathy (ankylosing spondylitis) taking disease modifying anti rheu-matic drugs— DMARDs (Salfasalazine) from 2016 till now, chronic persis-tent bronchial asthma on medication developed sore throat, increasing breathlessness and cough admitted to his own hospital on 22 April, 2020. He had a history of contact with a relapse nephrotic syndrome (glomerulone-phritis) patient admitted with severe respiratory distress later confirmed as COVID-19 following RT PCR test on 14 April, 2020. After 3 days of contact with the patient, the physician also developed the symptoms mentioned above. The RT PCR test result of the physician came positive on 18 April, 2020. The physician primarily taken only azithromycin 500 mg once daily along with other regular drugs. On 5, 12 and 18 May, 2020, his sample was taken for re-test and came positive subsequently. After that he started Iver-mectin (0.15 mg/kg) once daily for 3 days and doxycycline 100 mg BD for 7 days. He gave samples again on 27 and 29 May, 2020 which were came nega-tive after 39 days. On full recovery he was discharged from hospital on day 40. We choose the patient because presence of co-morbidities may be asso-ciated with delayed viral clearance and physicians with co-morbidities working in a hospital have high risk of being infected.
文摘When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.
基金supported and funded by the Information and Communication Technology(ICT)Division,Ministry of Posts,Telecommunications and IT,Government of the People’s Republic of Bangladesh.
文摘Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.
文摘At the invitation of CAFIU,a 14-member neighboring NGO delegation composed of representatives from Bangladesh,India,Indonesia and the Philippines visited China’s Beijing and Chengdu from July 7 to 16,2015.CAFIU Deputy Secretary-General Liu Kaiyang accompanied the delegation to Chengdu.As a CAFIU staff member,I had the honor to accompany the
文摘In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar. Afterward, this language holds an important place because this language is spoken by 26 core people all over the world. As a result, it has taken a new method to summarize Bengali documents. The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary. Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review. We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement. Checking the similarity between two sentences in order to exclude one since it has less duplication. Hereby, we’ve taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax. In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents. According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score. It has been found that the proper pronoun replacement was 72%.
文摘This article is aimed at describing a hybrid scheme for English to Bangla translation. The translated output in English scripts is useful for learning Bengali language. This is a significant contribution to Human Language Technology generation also. About two hundred million people in West Bengal and Tripura (two states in India) and in Bangladesh (a country whose people speak and write Bangla as their first language). This proposed translator would benefit Bengalee society because rural people are not usually very conversant with English. The English to Bangla Translator is being enhanced. This system (English- Bangla-ANUBAD or EB-ANUBAD) takes a paragraph of English sentences as input sentences and produces equivalent Bangla sentences. EB-ANUBAD system is comprised of a preprocessor, morphological parser, semantic parser using English word ontology for context disambiguation, an electronic lexicon associated with grammatical information and a discourse processor, and also uses a lexical disambiguation analyzer. This system does not rely on a stochastic approach. Rather, it is based on a special kind of hybrid architecture of transformer and rule-based Natural Language Engineering (NLE) architectures along with various linguistic knowledge components of both English and Bangla.
