The rapid spread of fake news through social media and the internet poses a major challenge, especially in developing countries like Bangladesh. Fake news, consisting of misleading or fabricated content, can severely impact individuals, organizations, and society. Many existing detection methods are complex and resource-intensive, making them unsuitable for real-time applications or low-resource languages. To address this issue, we proposed an efficient fake news detection system using a stacked machine learning model with TF-IDF for feature extraction. TF-IDF converts text into a numerical representation by emphasizing key terms, while the stacked ensemble model improves classification accuracy by integrating multiple machine learning algorithms. We also explored various machine learning classifiers, as well as the mBERT and Word2Vec feature vectorization techniques. We evaluated our system using three datasets: one English and two Bangla fake news detection datasets. The TF-IDF + Stacking model achieved 99.6% accuracy and a 99.8% F1-score on the English dataset. For the first Bangla dataset, accuracy improved from 74.8% to 85.2% after applying SMOTE. On the second Bangla dataset, BanFakeNews, the model achieved 98.4% accuracy, demonstrating strong performance across languages. This research introduces a lightweight yet highly effective machine learning-based fake news detection system, making it suitable for real-world applications, especially in multilingual and resource-limited settings.

@article{Hossen2023Sophisticated,
  title={A Sophisticated Feature Vectorization-Based Stacked Machine Learning 
    Approach for Fake News Detection in Bangla and English},
  author={Hossen, Md. Sabbir and Al Farid, Fahim and Shaha, Pabon and Twake, 
    Md. Mowahibur Rahman and Sabah, Fahjimatus and Rezwan, Mursalin Billah 
    and Rahman, Anichur and Karim, Hezerul Abdul and Miah, Abu Saleh Musa},
  journal={Social Network Analysis and Mining},
  year={2025}, 
  publisher={Springer}
}

The July Revolution in Bangladesh marked a significant student-led mass uprising, uniting people across the nation to demand justice, accountability, and systemic reform. Social media platforms played a pivotal role in amplifying public sentiment and shaping discourse during this historic mass uprising. In this study, we present a hybrid transformer-based sentiment analysis framework to decode public opinion expressed in social media comments during and after the revolution. We used a brand new dataset of 4,200 Bangla comments collected from social media. The framework employs advanced transformer-based feature extraction techniques, including BanglaBERT, mBERT, XLM-RoBERTa, and the proposed hybrid XMB-BERT, to capture nuanced patterns in textual data. Principle Component Analysis (PCA) were utilized for dimensionality reduction to enhance computational efficiency. We explored eleven traditional and advanced machine learning classifiers for identifying sentiments. The proposed hybrid XMB-BERT with the voting classifier achieved an exceptional accuracy of 83.7% and outperform other model classifier combinations. This study underscores the potential of machine learning techniques to analyze social sentiment in low-resource languages like Bangla.

@inproceedings{hossen2025july, 
author = {Hossen, Md Sabbir and Saiduzzaman, Md and Shaha, Pabon}, 
title = {Social Media Sentiments Analysis on the July Revolution in 
    Bangladesh: A Hybrid Transformer Based Machine Learning Approach}, 
booktitle = {Proceedings of the 17th International Conference on Electronics,
    Computers and Artificial Intelligence (ECAI)}, 
year = {2025}, 
publisher = {IEEE} }

Maternal health during pregnancy is a severe issue, particularly in the rural areas of developing countries like Bangladesh, where a lack of access to healthcare and inadequate infrastructure increase risks. Maternal healthcare has a great deal of difficulty due to the absence of reliable tools for forecasting health concerns. Negative results frequently result from the traditional method's inability to diagnose and manage pregnancy-related problems correctly. While there are several ways to monitor maternal health conditions, machine learning has the potential to increase diagnosis accuracy, efficiency, and speed. In this research, by using several machine learning classifiers, we built a model that can analyze the maternal health risk during pregnancy. The Maternal Health Risk dataset from the UCI machine learning repository was used in this study. SMOTE was utilized to address the class imbalance data and generated an additional 99000 data. We assess the model before and after using SMOTE. Accuracy, precision, recall, and F1-Score were utilized to evaluate the model's performance. Extreme Gradient Boosting (XGBoost) is our standout performer, with an accuracy of 84% and 95% before and after using SMOTE, respectively. Additionally, Explainable AI was used to increase the model's readability. This study demonstrates the power of machine learning, which could revolutionize maternal health care by identifying maternal health risks early.

@inproceedings{hossen2025maternal,
  author       = {M. Sabbir Hossen and P. Shaha and M. Saiduzzaman and 
    M. Shovon and A. Khatun Akhi and M. Sadiq Iqbal},
  title        = {An Explainable AI Driven Machine Learning Approach for 
    Maternal Health Risk Analysis},
  booktitle    = {27th International Conference on Computer and Information 
    Technology (ICCIT)},
  organization = {IEEE},
  year         = {2024}
}

Jellyfish are a diverse category of marine gelatinous organisms that are vital to the balance of aquatic ecosystems, yet they present notable difficulties for biodiversity preservation and ecological management due to their rapid growth and environmental influence. Precisely identifying different types of jellyfish is vital for ecosystem observation and effective control. In this research, we introduced a convolutional neural network-based approach for detecting and classifying jellyfish types utilizing an underwater image dataset. This approach incorporates sophisticated feature extraction methods, such as VGG16, EfficientNetV2-B0, ResNet50, and MobileNetV3, in conjunction with three Feedforward Neural Network classifiers and seven conventional machine learning classifiers, to enhance species recognition accuracy. Moreover, we employed the softmax function for direct classification of jellyfish species through CNN architectures. The integration of the MobileNetV3 with Artificial Neural Network emerged as the most accurate configuration, attaining an impressive accuracy of 98%, and markedly surpassing other combinations of feature extractors and classifiers. This research demonstrates the potential of hybrid models and CNN architecuters in tackling biodiversity-related issues and improving species classification in aquatic ecosystems

@inproceedings{hossen2025jelly,
  author    = {Hossen, M. S. and Saiduzzaman, M. and Shaha, P.
    and Nasir, M. K.},
  title     = {Jellyfish species identification: A CNN-based artificial 
    neural network approach},
  booktitle = {Proceedings of the International Conference on Quantum
    Photonics, Artificial Intelligence, and Networking (QPAIN)},
  year      = {2025},
  publisher = {IEEE}

Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous strawberry leaf diseases can impede the growth of strawberry plants. Leaf Scorch is one of the dangerous strawberry leaf diseases caused by the fungus “Diplocarpon Earliana.” Strawberry leaf disease diagnosis by traditional methods requires a lot of effort and time and is also challenging to identify accurately. In order to increase strawberry production, it is therefore essential to automatically detect strawberry leaf diseases. Machine learning and Deep learning have the potential to detect strawberry leaf diseases automatically. In this paper, we proposed a hybrid Machine Learning methodology using CNN feature extraction with the traditional ML classifier to detect Strawberry Leaf Diseases. For this instance, we used the strawberry leaf dataset from the PlantVillage Updated dataset. Based on our research, ResNet50 with Support Vector Machine diagnoses strawberry leaf scorch with 99.80% accuracy. This research assured sustainable advancements in strawberry production by integrating and offering an effective way to detect strawberry leaf diseases.

@inproceedings{hossen2025strawberry,
  author       = {M. Sabbir Hossen and P. Shaha and M. Saiduzzaman},
  title        = {A Hybrid Machine Learning Approach Utilizing CNN Feature
    Extraction with Traditional Classifier to Identify Strawberry Leaf Diseases},
  booktitle    = {4th International Conference on Electrical, Computer and 
    Communication Engineering (ECCE)},
  organization = {IEEE},
  year         = {2025}
}

Oral Squamous Cell Carcinoma (OSCC) is a prevalent and deadly form of oral cancer, responsible for approximately 3% of all cancer cases worldwide and over 330,000 deaths annually. Early detection is critical for effective treatment and improved outcomes. Traditional diagnosis through manual examination of histopathological images is time-consuming and subjective. Recent advances in AI-guided computer vision have shown promise, though many approaches require extensive computational resources and large datasets. This paper introduces a cascaded network that integrates deep learning and traditional machine learning techniques for detecting Oral Squamous Cell Carcinoma (OSCC) from histopathology images. The proposed framework employs a comprehensive approach that includes three feature selection strategies—Principal Component Analysis (PCA), Bacterial Foraging Optimization (BFO), and a no-optimization baseline—to assess the impact of feature refinement on classification performance. An exhaustive search strategy was used to evaluate five widely adopted Convolutional Neural Network (CNN) models for feature extraction. The extracted features were subsequently either optimized using PCA or BFO or left unoptimized to identify the most informative subsets. These feature sets were then fed into seven traditional machine learning classifiers to perform OSCC detection. The MobileNetV2-PCA-LR cascaded network demonstrated the best overall performance among the configurations evaluated. This model achieved near-perfect results with an accuracy, precision, recall, and F1-score of 99.70%, while also offering faster image processing times. The proposed cascaded framework thus delivers a balanced solution that combines efficiency, high accuracy, scalability, and robustness, making it a strong candidate for practical and automated OSCC screening in clinical environments.

@INPROCEEDINGS{11160086,
  author={Shuvo, Eshat Ahmad and Shovon, Md. and Sarkar, Md. Nazmul and 
    Hossen, Md. Sabbir and Shaha, Pabon and Rahman, Wahidur},
  booktitle={2025 2nd International Conference on Next-Generation Computing, 
    IoT and Machine Learning (NCIM)}, 
  title={Optimized Hybrid Cascaded Approach for Accurate Oral Cancer Detection
    in Histopathology Images Using Deep CNNs}, 
  year={2025},
  pages={1-6}
    }

Generative AI models capable of producing human-like text, images, and other content are rapidly transforming the digital landscape. As their use becomes increasingly widespread across domains such as education, media, and communication, their outputs are shaping public discourse, influencing opinions, and even spreading misinformation. In this context, accurately identifying the source of AI-generated text is becoming increasingly crucial for ensuring transparency and accountability, protecting information integrity, mitigating deception, and fostering public trust in digital content. This study presents a source attribution framework designed to distinguish between texts generated by two leading large language models: ChatGPT and Gemini. A dataset of 2,786 AI-generated samples was constructed for this purpose. We employed transformer-based feature extraction techniques, including BERT, ALBERT, and RoBERTa, as well as a Hybrid model (AR-BERT, which combines ALBERT and RoBERTa), to capture distinctive linguistic signatures. This was followed by classification using nine traditional and ensemble machine learning algorithms. Among these, the AR-BERT-based features combined with a stacking ensemble classifier, StackTrace-AI, achieved the highest accuracy of 84.44%, surpassing all other combinations. The findings underscore the viability of transformer-driven source detection and offer valuable insights for advancing AI accountability, generative content auditing, and responsible AI governance.

@inproceedings{Shaha2025StackTrace,
  title={StackTrace-AI: Identifying Generative AI Text Origins
    using Ensemble Learning},
  author={Shaha, Pabon and Hossen, Md. Sabbir and Sojib,Md. Ibrahim 
    Hosen and Akter, Sanjida and Saiduzzaman, Md. and Nasir, Mostofa Kamal},
  booktitle={{IEEE 2nd International Conference on Computing, Applications
    and Systems (COMPAS 2025)}},
  year={2025},
  address={Kushtia, Bangladesh},
  organization={IEEE}
}

Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastructure and diagnostic systems. Conventional diagnostic approaches often fall short in accurately detecting and managing heart disease risks, resulting in unfavorable outcomes. Machine learning presents a powerful means to boost the precision and reliability of cardiovascular disease prognosis and diagnosis. In this research, we introduced a unified approach incorporating classification techniques for detecting heart disease and regression techniques for forecasting associated risks. The analysis utilized the dataset, named Heart Disease, containing 1,035 instances. To mitigate the problem of data disproportion, the SMOTE was implemented, producing 100,000 additional synthetic samples. Evaluation metrics such as F1-score, recall, precision, accuracy, MAE, RMSE, MSE, and R² were adopted to evaluate the performance of the models. Among the classification algorithms, Random Forest delivered the most notable results, attaining an accuracy of 0.972 on actual data and 0.976 on artificially generated data. For prediction modeling, for both synthetic and real samples, linear regression produced the best R2 values of 0.992 and 0.984, respectively, along with the least amount of measurement errors. Furthermore, Explainable AI methods were utilized to improve the comprehensibility of the model outcomes. This paper emphasizes the transformative capabilities of machine learning for diagnosing cardiovascular disease and estimating risk levels, thereby supporting timely interventions and enhancing clinical settings.

@inproceedings{hossen2025cardiovascular,
  author    = {Sourov, M. E. A. and Hossen, M. S. and Shaha, P. 
    and Hossain, M. M. and Iqbal, M. S.},
  title     = {An Explainable AI-Enhanced Machine Learning Approach 
    for Cardiovascular Disease Detection and Risk Assessment},
  booktitle = {Proceedings of the International Conference on Quantum 
    Photonics, Artificial Intelligence, and Networking (QPAIN)},
  year      = {2025},
  publisher = {IEEE}
}

The rapid advancement of deepfake technology has raised serious concerns regarding digital authenticity, misinformation, and media trust. By enabling the creation of hyper-realistic manipulated media, deepfakes pose significant risks to security, privacy, and the integrity of digital communication. Social platforms are increasingly flooded with such AI-generated content, which can deceive human perception, spread misinformation, and undermine the credibility of multimedia information. Therefore, early detection of deepfakes is crucial. In this research, We focus on developing a robust detection framework to distinguish between genuine and artificially generated deepfake face images. For this purpose, a dataset of 12.2k authentic images was collected, and an additional 12.2k deepfake images were generated using a generative adversarial network (GAN), ensuring a balanced dataset for real versus fake image classification. We present a custom CNN-transformer hybrid framework, T-CNN, for deepfake detection. The proposed framework leverages convolutional layers for spatial feature extraction and transformer-based attention mechanisms to capture contextual dependencies within facial patterns. The experimental evaluation demonstrates that the custom CNN-transformer model achieves an exceptional accuracy of 99.3%. This study underscores the importance of hybrid deep learning approaches in addressing the growing challenge of deepfake detection and highlights their potential for safeguarding digital authenticity, particularly within the domain of facial image verification.

@inproceedings{Hossen2024TransCNN,
  title={TransCNN: A Hybrid CNN–Transformer Synergy for Reliable 
    Deepfake Forensics},
  author={Hossen, Md. Sabbir and Saiduzzaman, Md.},
  booktitle={2024 27th International Conference on Computer and 
    Information Technology (ICCIT)},
  year={2024},
  address={Cox's Bazar, Bangladesh},
  organization={IEEE}
}