Browsing by Author "Pasha, Syed Tangim"

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A Federated Learning Approach for Type-2 Diabetes Detection Using a Naive Bayes Classifier
(, The International Diabetes Federation (IDF), 2023, 2023-10) Rahman, M. M.; Islam, Ashraful; Pasha, Syed Tangim; Islam, M. Usama; Alam, Md Zahangir
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 by exploiting the heterogeneity of data. Common phenomena of FL is to employ deep learning models. Nonetheless, simple machine learning models such as Naive Bayes have promising potentials for detecting diabetes mellitus in a FL environment. To explore the practical prospects of building a privacy-preserving model for identifying patients with diabetes mellitus, utilizing their individual data. A cohort of 103 persons are enrolled in this study. Each participant was sent a questionnaire to answer with their own personal data about their age, Body Mass Index (BMI), insulin level, glucose concentration, skin thickness of an individual. Subsequently, an initial model, built using the Pima Indian Diabetes dataset, was sent to their mobile devices [1]. The participants utilized the initial model to train with their own data. Following this, the model parameters are updated and sent to the server. The server aggregated the parameters and averaged them to make a global model. This completes a single iteration of federated learning life cycle. Participants are diversed in gender: male (60.2%) and female (39.8%); in age groups: 20-35 (14.6%), 36-50 (46.6%), 51-65 (38.8%). The work shows an accuracy of 89.32% and a precision of 88.89% for those having diabetes while 90.32% precision in detecting patients not having a diabetes mellitus. The number of communication rounds was 50 where in each round at least two participants participants in building federated model updates. Since one of the key reasons for using FL is to improve data privacy, quantifying the level of privacy is critical. An network intruder could decoded the model updates by examining the changes in the global model over time. However, a membership inference attack (MIA) is measured in various differential privacy (DP) budgets. DP aims to prevent this kind of inference by adding noise to the data (model updates). For instance, if the model update would normally be a weight change of +0.5, a noise from a Laplacian distribution with mean 0 is added. Hence, the resulting noisy update might then be +0.52. A naive bayes based federated learning system is built to detect diabetes mellitus (Type-2) preserving the privacy of user data at the first place.
Agro-Health
(Daffodil International University, 2024-01-01) Pasha, Syed Tangim
Presenting "AgroHealth," a cutting-edge online application designed to satisfy the many demands of farmers and medical professionals working in the agriculture industry. This dynamic platform aims to improve the general health and well-being of crops by acting as a central point for smooth communication and collaboration. With the help of AgroHealth's user-friendly interface, farmers may effectively report and record health-related problems impacting crops. A consolidated database with vital details about agricultural circumstances, illness histories, and particular health needs is established via the online program. Healthcare workers may make wellinformed judgments and offer prompt support with the help of this repository. One of AgroHealth's best features is its live video calling feature, which lets users find the closest agricultural health centers. This makes sure that health-related issues are routed quickly and accurately, linking farmers to the closest medical providers. Moreover, AgroHealth delivers real-time updates and notifications, keeping farmers and healthcare professionals informed about crucial concerns and trends in their agricultural community. This function improves the industry's general awareness and reactivity. With the use of AgroHealth's collaboration tools, farmers and medical experts can communicate more effectively and share vital information including immunization schedules, disease management plans. This simplified method improves agricultural health practices and raises the general health of crops and cattle.
Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques
(IEEE, 2023-07-13) Pasha, Syed Tangim; Islam, Ashraful; Rahman, Mohammed Masudur; Ahmed, Eshtiak; Foysal, Md. Ferdouse Ahmed; Alam, Md Zahangir
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 separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.
Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques
(Independent University, Bangladesh, 2023-05) Pasha, Syed Tangim; Islam, Ashraful; Rahman, Mohammed Masudur; Ahmed, Eshtiak; Foysal, Md. Ferdouse Ahmed; Alam, Md Zahangir
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 separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.
Using an Ensemble Machine Learning Model with Explainable AI (XAI) to Diagnose Gestational Diabetes Mellitus
(The International Diabetes Federation (IDF), 2023, 2023-10) Pasha, Syed Tangim; Islam, Ashraful; Sikder, Sanker; Habib, Md Tarek; Alam, Md Zahangir; Amin, M Ashraful
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 percent of pregnant women had GDM, affecting 21.1 million live births [1]. Predictive models using an Explainable AI technique to detect GDM are currently unavailable, even though early detection can significantly reduce risks to human life. To develop an ensemble machine learning model with the XAI approach for diagnosing GDM. Our study used a 1,012 GDM patient records dataset with 7 attributes sourced from [1]. Due to their unsuitability for our studies, attributes like ‘Age’ and ‘Pregnancy No.’ were omitted from the dataset. The ‘Height’ attribute was also eliminated because of its negative correlation with the ‘BMI’ feature. We used the Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced class issues in the target attributes after performing feature scaling on the remaining attributes. Our strategy required developing a Stacking Ensemble model that integrated other models, including the Decision Tree, Random Forest, XGBoost, MLP, and Logistic Regression. We employed metrics, e.g., Receiver Operating Characteristic (ROC) curve, Area under the ROC Curve (AUC), and SHapley Additive exPlanations (SHAP) values to evaluate the model's effectiveness. 70% of the dataset was used for training and 30% for testing. We achieved 85% accuracy with an AUC score of 0.91 in the experiment, and the ROC curve is shown as the performance curve in Fig. 1(a). The feature plot in Fig. 1(b) shows that the ‘Heredity’ feature is more important than the ‘Weight’ and ‘BMI’ features, whereas the summary plot in Fig. 1(b) combines feature effects and importance. Findings show that 'Heredity' has a high and positive impact on predicting GDM in this dataset whereas 'Weight' and 'BMI' have a positive impact but are lower than 'Heredity'. We developed an XAI approach-based ensemble machine learning model to diagnose GDM.