Now showing items 1-3 of 3

    • A Federated Learning Approach for Type-2 Diabetes Detection Using a Naive Bayes Classifier 

      Rahman, M. M.; Islam, Ashraful; Pasha, Syed Tangim; Islam, M. Usama; Alam, Md Zahangir (, The International Diabetes Federation (IDF), 2023, 2023-10)
      Federated learning (FL) is a new way of training machine learning models across decentralized devices without exchanging the raw data. This approach preserves privacy and promotes the development of more personalized models ...
    • Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques 

      Pasha, Syed Tangim; Islam, Ashraful; Rahman, Mohammed Masudur; Ahmed, Eshtiak; Foysal, Md. Ferdouse Ahmed; Alam, Md Zahangir (Independent University, Bangladesh, 2023-05)
      The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two ...
    • Using an Ensemble Machine Learning Model with Explainable AI (XAI) to Diagnose Gestational Diabetes Mellitus 

      Pasha, Syed Tangim; Islam, Ashraful; Sikder, Sanker; Habib, Md Tarek; Alam, Md Zahangir; Amin, M Ashraful (The International Diabetes Federation (IDF), 2023, 2023-10)
      The emergence of gestational diabetes mellitus (GDM) in pregnant women is a serious health concern and an alarming issue. According to the most recent data from the International Diabetes Federation (IDF), in 2021, 16.7 ...

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