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All Journal Bulletin of Electrical Engineering and Informatics
Jabir Al Nahian
Daffodil International University
Electrical & Electronics Engineering

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Common human diseases prediction using machine learning based on survey data Jabir Al Nahian; Abu Kaisar Mohammad Masum; Sheikh Abujar; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3405

Abstract

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.
An insight into the intricacies of lingual paraphrasing pragmatic discourse on the purpose of synonyms Jabir Al Nahian; Abu Kaisar Mohammad Masum; Muntaser Mansur Syed; Sheikh Abujar
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3485

Abstract

The term "paraphrasing" refers to the process of presenting the sense of an input text in a new way while preserving fluency. Scientific research distribution is gaining traction, allowing both rookie and experienced scientists to participate in their respective fields. As a result, there is now a massive demand for paraphrase tools that may efficiently and effectively assist scientists in modifying statements in order to avoid plagiarism. natural language processing (NLP) is very much important in the realm of the process of document paraphrasing. We analyze and discuss existing studies on paraphrasing in the English language in this paper. Finally, we develop an algorithm to paraphrase any text document or paragraphs using WordNet and natural language tool kit (NLTK) and maintain "Using Synonyms" techniques to achieve our result. For 250 paragraphs, our algorithm achieved a paraphrase accuracy of 94.8%.
Bengali Slang detection using state-of-the-art supervised models from a given text Md. Abdul Hamid; Eteka Sultana Tumpa; Johora Akter Polin; Jabir Al Nahian; Atiqur Rahman; Nurjahan Akther Mim
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4743

Abstract

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.
Co-Authors Abu Kaisar Mohammad Masum Atiqur Rahman Eteka Sultana Tumpa Johora Akter Polin Md. Abdul Hamid Md. Jueal Mia Muntaser Mansur Syed Nurjahan Akther Mim Sheikh Abujar