Browsing by Author "Islam, Md. Fahim"

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A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
(BRAC University, 9/28/2022) Chowdhury, Asif Hasan; Islam, Md. Fahim; Riad, M Ragib Anjum; Hashem, Faiyaz Bin; Alam, Md. Golam Rabiul; Reza, Md Tanzim
The significant advancements in computational power create the vast opportunity for using Artificial Intelligence in different applications of healthcare and medical science. A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging a combination of SWIN transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available technology that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model Swin Transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this thesis, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information.
A hybrid FL-Enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
(BRAC University, 9/22/2022) Chowdhury, Asif Hasan; Islam, Md. Fahim; Riad, M Ragib Anjum; Hashem, Faiyaz Bin; Alam, Md. Golam Rabiul; Reza, Md Tanzim
The significant advancements in computational power create the vast opportunity for using Artificial Intelligence in different applications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available technology that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this thesis, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information.
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.