Md. Rashed

Md. Rashed

Lifelong learner

Publications

You can also find my articles on My Google Scholar Profile.

Hybrid Machine Learning Models for Accurate Type 2 Diabetes Mellitus Prediction Using a Stacking Classifier and a Meta-Model Approach

Published in Cureus Journal Of Computer Science, 2025

This paper presents a hybrid machine learning approach for accurate Type 2 diabetes mellitus (T2DM) prediction, integrating a stacking classifier with a meta-model to enhance predictive performance. By leveraging multiple machine learning techniques, the study aims to improve early diagnosis and assist healthcare professionals in data-driven decision-making. Published in the Cureus Journal of Computer Science, indexed in PubMed, Google Scholar, CrossRef, Scilit, SHERPA ROMEO, OpenAlex, and ROAD

Recommended citation: Rashed, M., Hossain, M. I., Mahdi, A., & Mustofa, G. (2025). Hybrid machine learning models for accurate Type 2 diabetes mellitus prediction using a stacking classifier and a Meta-Model approach. Cureus Journals. doi: https://doi.org/10.7759/s44389-025-03135-0
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Improving Speech Emotion Recognition and Classification Accuracy Using Hybrid CNN-LSTM-KNN Model

Published in Cureus Journal Of Computer Science, 2025

This paper presents a hybrid machine learning approach for accurate Type 2 diabetes mellitus (T2DM) prediction, integrating a stacking classifier with a meta-model to enhance predictive performance. By leveraging multiple machine learning techniques, the study aims to improve early diagnosis and assist healthcare professionals in data-driven decision-making. Published in the Cureus Journal of Computer Science, indexed in PubMed, Google Scholar, CrossRef, Scilit, SHERPA ROMEO, OpenAlex, and ROAD

Recommended citation: Hossain, Md Imran, Md Mojahidul Islam, Tania Nahrin, Md Rashed, and Md Atiqur. "Improving Speech Emotion Recognition and Classification Accuracy Using Hybrid CNN-LSTM-KNN Model." Journal homepage: www. ijrpr. com ISSN 2582: 7421.
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Improving Speech Emotion Recognition and Classification Accuracy Using Hybrid CNN-LSTM-KNN Model

Published in International Journal of Research Publication and Reviews, 2024

This study addresses Speech Emotion Recognition (SER), a vital aspect of human-computer interaction, using a hybrid model combining CNN, LSTM, and KNN algorithms. The CNN extracts acoustic features from speech spectrograms, while LSTM captures temporal dependencies. KNN classifies emotional states based on these features. The model, tested on the TESS dataset, achieves high accuracy and robustness, outperforming traditional methods. It holds promise for real-world applications in sentiment analysis, virtual assistants, and more, advancing SER techniques for improved human-computer interaction.

Recommended citation: Hossain, Md Imran, Md Mojahidul Islam, Tania Nahrin, Md Rashed, and Md Atiqur. "Improving Speech Emotion Recognition and Classification Accuracy Using Hybrid CNN-LSTM-KNN Model." Journal homepage: www. ijrpr. com ISSN 2582: 7421.
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OPTIMIZING WASTE MANAGEMENT WITH IOT: BOOSTING EFFICIENCY AND PROMOTING SUSTAINABILITY

Published in International Research Journal of Modernization in Engineering Technology and Science, 2024

Traditional waste management is labor-intensive and inefficient, while efficient systems are crucial for sustainable cities. This study proposes an IoT-based smart waste management system to improve trash disposal and monitoring in urban areas. The suggested solution uses smart trash cans equipped with ultrasonic sensors, ARDUINO UNO, servo motors, and GSM communication to monitor waste levels and notify relevant personnel. The system automates waste collection, enhancing scalability and adaptability. The project aims to make the technology affordable, promoting environmental sustainability and green practices.

Recommended citation: Md. Rashed, S. Ahmad, and M. Shakil Hossain, “OPTIMIZING WASTE MANAGEMENT WITH IOT: BOOSTING EFFICIENCY AND PROMOTING SUSTAINABILITY,” International Research Journal of Modernization in Engineering Technology and Science, pp. 2582–5208, doi: https://doi.org/10.56726/IRJMETS57390.
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