Data Modeling
Volume 5 Number 1 2024
Volume 5 Number 1 2024
Data Modeling
Data Modeling
Descriptive Table of Contents
Research Article
(Unspecified)
Manuscript-In-Press
Shafia Sultana 1*
Proposes an automated sachet-water model that could affordably reduce waterborne disease burden in Bangladesh through consistent, traceable production quality.
Abstract | Full Text | Open Access Article
Manuscript-In-Press
KMFusionNet: An Alternating Tree-Estimator Boosting Framework for Imbalanced Binary Classification
Shazib Sheikh1*, Ethan Debnath2, Zulkarnain Saurav 3, Kamruzzaman Mithu3, Swakkhar Shatabda 4
KMFusionNet improves minority class detection in imbalanced datasets through computationally efficient alternating tree-estimator boosting.
Abstract | Full Text | Open Access Article
Manuscript-In-Press
Kamruzzaman Mithu 1*,
This work eases a long-standing bandwidth bottleneck in Byzantine consensus while preserving deterministic finality, offering a practical scaling path.
Abstract | Full Text | Open Access Article
Manuscript-In-Press
Mohammad Nazmush Shamael 1*, Khondaker Abdullah Al Mamun 1
Comparing five transfer learning models, MobileNet achieved superior accuracy for multi-class skin cancer classification, enabling accessible, cost-effective AI screening tools.
Abstract | Full Text | Open Access Article
Manuscript-In-Press
Kamruzzaman Mithu 1*
FLCMD shows promise for improving insulin resistance, lipid profiles, and body composition in PCOS, warranting further clinical research validation.
Abstract | Full Text | Open Access Article
IoT-Based Smart Aquaculture Water Quality Monitoring and Health Prediction Using Machine Learning
Kamruzzaman Mithu 1*, Md. Nesar Uddin1 ,Md. Ataur Rahman1, Sayed Rokibul Hossain1, A.K.M. Muzahidul Islam1
Low-cost IoT and machine learning integration enables practical real-time aquaculture health prediction, supporting sustainable fish farming and environmental monitoring.
Abstract | Full Text |PDF (674 KB) | Open Access Article
Manuscript-In-Press
Sayed Rokibul Hossain1*, Kamruzzaman Mithu 1, Md. Nesar Uddin1, Md. Ataur Rahman1, Khondaker Abdullah Al Mamun1
This study demonstrates that XGBoost can enhance early thyroid disease detection by handling imbalance and improving classification accuracy in clinical datasets.
Abstract | Full Text | Open Access Article
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