Rafid Mostafiz

Accurate symptom-to-disease classification and clinically-grounded treatment recommendations remain challenging, particularly in heterogeneous patient settings with high diagnostic risk. Existing large language model (LLM)-based systems often lack medical grounding and fail to quantify uncertainty, resulting in unsafe outputs. We propose CLIN-LLM, a safety-constrained hybrid pipeline that integrates multimodal patient encoding, uncertainty-calibrated disease classification, and retrieval-augmented treatment generation. Our framework fine-tunes BioBERT on 1,200 clinical cases from the Symptom2Disease dataset and incorporates Focal Loss with Monte Carlo Dropout to generate confidence-aware predictions from free-text symptoms and structured vital signs. Low-certainty cases (18%) are automatically flagged for expert review, ensuring human oversight. For treatment generation, CLIN-LLM employs Biomedical Sentence-BERT to retrieve top-k relevant dialogues from the 260,000-sample MedDialog corpus. The retrieved evidence and patient context are fed into a fine-tuned FLAN-T5 model for personalized treatment generation, followed by post-processing with RxNorm for antibiotic stewardship and drug–drug interaction (DDI) screening. CLIN-LLM achieves 98% accuracy and F1 score, outperforming ClinicalBERT by 7.1% (p < 0.001), with 78% top-5 retrieval precision and a clinician-rated validity of 4.2/5. Reduces unsafe antibiotic suggestions by 67% compared to GPT-5. These results demonstrate CLIN-LLM’s robustness, interpretability, and clinical safety alignment. The proposed system provides a deployable, human-in-the-loop decision support framework for resource-limited healthcare environments.

As network‐based services continue to expand and cyber threats grow increasingly sophisticated, the deployment of effective network intrusion detection systems (NIDS) has become crucial for the protection of sensitive data and the preservation of digital infrastructure integrity. These systems are indispensable in ensuring the security and reliability of digital environments. This study presents an all‐encompassing approach that integrates feature selection, ensemble learning, clustering, and diverse analytical methods to improve the precision and effectiveness of network intrusion detection systems. Using data sets that have been preprocessed and classified using the Random Forest and XGBoost algorithms, we extract key features and categorize key variables using K‐means clustering, thus constructing a robust feature set for intrusion detection. Our experimental results demonstrate high precision in detecting benign and malicious activities using the CIC‐IDS‐17 and UNSW‐NB15 datasets. These outcomes underscore the efficacy of our proposed approach in monitoring network traffic, effectively identifying threats, and preventing potential intrusions.

Pneumonia, a severe lung infection caused by various viruses, presents significant challenges in diagnosis and treatment due to its similarities with other respiratory conditions. Additionally, the need to protect patient privacy complicates the sharing of sensitive clinical data. This study introduces FLPneXAINet, an effective framework that combines federated learning (FL) with deep learning (DL) and explainable AI (XAI) to securely and accurately predict pneumonia using chest X-ray (CXR) images. We utilized a benchmark dataset from Kaggle, comprising 8,402 CXR images (3,904 normal and 4,498 pneumonia). The dataset was preprocessed and augmented using a cycle-consistent generative adversarial (CycleGAN) network to increase the volume of training data. Three pre-trained DL models named VGG16, NASNetMobile, and MobileNet were employed to extract features from the augmented dataset. Further, four ensemble DL (EDL) models were used to enhance feature extraction. Feature optimization was performed using recursive feature elimination (RFE), analysis of variance (ANOVA), and random forest (RF) to select the most relevant features. These optimized features were then inputted into machine learning (ML) models, including K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM), and RF, for pneumonia prediction. The performance of the models was evaluated in a FL environment, with the EDL network achieving the best results: accuracy 97.61%, F1 score 98.36%, recall 98.13%, and precision 98.59%. The framework’s predictions were further validated using two XAI techniques—Local Interpretable Model-Agnostic Explanations (LIME) and Grad-CAM. FLPneXAINet offers a robust solution for healthcare professionals to accurately diagnose pneumonia, ensuring timely treatment while safeguarding patient privacy.

Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). We tested the presented framework on two well-known datasets, ISBI2016 and ISBI2017. The data was first preprocessed by several techniques: resizing, normalization, balancing, and augmentation. Six advanced DL algorithms—VGG16, Xception, DenseNet169, InceptionV3, MobileViT, and InceptionResNetV2—were applied for classification tasks. Among these, the DenseNet169 algorithm obtained the highest accuracy of 83.3% in ISBI2016 and 92.67% in ISBI2017. All models were then tested in an FL platform to maintain data privacy. In the FL platform, the VGG16 algorithm showed the best results, with 92.08% accuracy on ISBI2016 and 94% on ISBI2017. To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. We believe the proposed framework offers a dependable tool for SC diagnosis while protecting sensitive medical data.

Early detection and characterization are crucial for treating and managing Parkinson's disease (PD). The increasing prevalence of PD and its significant impact on the motor neurons of the brain impose a substantial burden on the healthcare system. Early‐stage detection is vital for improving patient outcomes and reducing healthcare costs. This study introduces an ensemble boosting machine, termed PD_EBM, for the detection of PD. PD_EBM leverages machine learning (ML) algorithms and a hybrid feature selection approach to enhance diagnostic accuracy. While ML has shown promise in medical applications for PD detection, the interpretability of these models remains a significant challenge. Explainable machine learning (XML) addresses this by providing transparency and clarity in model predictions. Techniques such as Local Interpretable Model‐agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) have become popular for interpreting these models. Our experiment used a dataset of 195 clinical records of PD patients from the University of California Irvine (UCI) Machine Learning repository. Comprehensive data preparation included encoding categorical features, imputing missing values, removing outliers, addressing data imbalance, scaling data, selecting relevant features, and so on. We propose a hybrid boosting framework that focuses on the most important features for prediction. Our boosting model employs a Decision Tree (DT) classifier with AdaBoost, followed by a linear discriminant analysis (LDA) optimizer, achieving an impressive accuracy of 99.44%, outperforming other boosting models.