Browsing by Author "Hamid, Md. Abdul"

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A Method for Bengali Author Detection Using State of the Arts Supervised Machine Learning Classifiers
(Springer, 2023-09-15) Hamid, Md. Abdul; Marjana, Nusrat Jahan; Tumpa, Eteka Sultana; Khan, Md. Rafidul Hasan; Afroz, Umme Sanzida; Rahman, Md. Sadekur
Text classification is an important topic of study in the area of natural language Processing. To identify the authorship of the provided Bangla text, we create a model using the State of Arts Supervised method. Because our work is a multi-class categorization, we may use it to determine who wrote articles, news, emails, or messages. It can also use to find ghostwriters, identify anonymous authors, and detect plagiarism. This article focuses on the categorization of five Bengali authors. They are well-known writers in Bengali literature and poetry. Humayun Ahmed, Rabindranath Tagore, Muhammad Zafar Iqbal, Kazi Nazrul Islam, and Sarat Chandra Chattopadhyay are the five writers. Data were manually collected from various sources in the novels or books of these five writers, and we contained over 4500 paragraphs. A completely new dataset is created for the experimental evaluation. We preprocess Bengali text for training reasons. Logistic regression, naive Bayes, decision trees, support vector machines, random forests, XG-Boost, and K-nearest neighbor are among the seven classification methods employed. In our experiment, the Support Vector Machine produces the best experimental classification report. Support vector machine gives 82% model accuracy.
A Method for Bengali Author Detection Using Supervised Classification Models
(Daffodil International University, 23-01-29) Hamid, Md. Abdul; Rahman, Md. Tanjil; Islam, Md. Fahim
Text classification is an important area of study in the field of NLP. We live in a modern world where everyone values their intellectual property. Intellectual property includes digital written ideas, blogs, poems, novels, and posts, among other things. Evil people try to steal valuable intellectual property from others and claim it as their own or pirate these properties. To avoid these problems, we created several models based on the art-of-states Supervised method for determining authorship from a given Bangla text. Because our work is a multi-class classification, we can use it to determine who created articles, news, or messages. Authorship detection can be used to identify anonymous authors as well as detect plagiarism. This article focuses on categorizing five authors in the context of Bengali text. These five authors are well-known figures in Bengali literature and poetry. Humayun Ahmed, Rabindranath Tagore, Muhammad Zafar Iqbal, Kazi Nazrul Islam, and Sarat Chandra Chattopadhyay are among those honored. Data is being gathered from over 4500 paragraphs. For the experimental evaluation, a dataset is created. We preprocess Bengali text for training purposes. Logistic regression, naive Bayes, decision trees, SVM, Random Forest, XG-Boost, and KNN are among the seven supervised classification methods used. Our deep learning Bi-Lstm model outperforms the seven supervised models in terms of accuracy. By mentioning all models, the transformers-based model, Bert uncased model learns the context very well. Bi-Lstm was used in our experiment. Bi-Lstm and Bert uncased model provides the best experimental classification report in our experiment. The Bi-Lstm model loss function yields 0.3789 with a maximum accuracy of 88% and Bert base uncased F1-Score gives 91 % accuracy.
Bengali Slang Detection Using State-of-the-Art Supervised Models From a Given Text
(Institute of Advanced Engineering and Science (IAES), 2023-08-15) Hamid, Md. Abdul; Tumpa, Eteka Sultana; Polin, Johora Akter; Nahian, Jabir Al; Rahman, Atiqur; Mim, Nurjahan Akther
Almost all Bengalis who own smartphones also have social media accounts. People from different regions occasionally employ regional Slang that is unfamiliar to outsiders and confuses the meaning of the sentence. Nearly all languages can now be translated thanks to modern technology, but only in very basic ways, which is a concern. Bengali Slang terms are difficult to translate due to a dearth of rich corpora and frequently occurring new Slang terms developed by people, making it impossible for speakers of other languages to understand the context of a sentence in which Slang is used. We developed a solution to this issue. To create models that can detect Bengali Slang terms from social media, we gather various Slang phrases from various regions and develop a modest corpus. Our suggested method nearly always succeeds in extracting Bengali Slang terms from fresh material. We create a total of 7 supervised models and assess which is the most effective for our study. One of them has a 70% accuracy and 86% recall rate for successful identification. Our models may be linked to the social media platform's backend to restrict the use of Bengali Slang in posts, blogs, comments, and other areas.
Healthcare as a Service (HAAS): CNN-Based Cloud Computing Model for Ubiquitous Access to Lung Cancer Diagnosis
(Elsevier, 2023-10-27) Faruqui, Nuruzzaman; Yousuf, Mohammad Abu; Kateb, Faris A.; Hamid, Md. Abdul; Monowar, Muhammad Mostafa
The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability—a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.
President Message at 13th Convocation of EWU 2014
(East West University, 2014-02-24) Hamid, Md. Abdul
President Message at 14th Convocation of EWU 2015
(East West University, 2015-03-19) Hamid, Md. Abdul
Tourism Marketing in Bangladesh: Issues, Strategies and Challenges
(University of Rajshahi, Rajshahi, 2020) Hamid, Md. Abdul; Reza, Md. Salim
The tourism sector of Bangladesh is perceived as very potential from the very beginning. The policymakers, concerned authorities, and other active stakeholders frequently express such opinions on different occasions. But the achievements in the last four decades do not indicate the same. Undoubtedly many factors are responsible for such slow progress. In that case, ‘Tourism marketing’ has been identified (by some studies) as one of the significant players. The researcher was fascinated to understand the marketing issues, strategies, and challenges of the tourism sector of Bangladesh. The objectives of the study were to portray the current scenario of the tourism marketing issues (elements) in Bangladesh; to gain more insights of tourist behavior in Bangladesh; to be acquainted with the competitiveness of Bangladesh as a tourism destination; and to figure out the challenges of marketing the tourism sector of Bangladesh. It was an exploratory study in nature. Both the primary and secondary data have been used. For primary data, a survey has been conducted on 390 respondents based on a structured questionnaire. The respondents were tourists and visitors of four leading tourism spots of Bangladesh. Besides, to collect qualitative data 28 active stakeholders have been interviewed from the concerned industries. To analyze the quantitative data different statistical and mathematical tools have been used. For qualitative data, a content analysis method has been used. Related secondary materials have been incorporated where appropriate.