arXiv subjects

Md Abdul Awal

Publications and source records attributed to Md Abdul Awal.

3 recordsLinked to original sources

arXiv2025

QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disorder

This study presents QuanvNeXt, an end-to-end fully quanvolutional model for EEG-based depression diagnosis. QuanvNeXt incorporates a novel Cross Residual block, which reduces feature homogeneity and strengthens cross-feature relationships while retaining parameter efficiency. We evaluated QuanvNeXt on two open-source datasets, where it achieved an average accuracy of 93.1% and an average AUC-ROC of 97.2%, outperforming state-of-the-art baselines such as InceptionTime (91.7% accuracy, 95.9% AUC-ROC). An uncertainty analysis across Gaussian noise levels demonstrated well-calibrated predictions, with ECE scores remaining low (0.0436, Dataset 1) to moderate (0.1159, Dataset 2) even at the highest perturbation (ε = 0.1). Additionally, a post-hoc explainable AI analysis confirmed that QuanvNeXt effectively identifies and learns spectrotemporal patterns that distinguish between healthy controls and major depressive disorder. Overall, QuanvNeXt establishes an efficient and reliable approach for EEG-based depression diagnosis.

cs.LG↗

arXiv2024

Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves.

q-bio.QM↗

arXiv2024

EvaluateXAI: A Framework to Evaluate the Reliability and Consistency of Rule-based XAI Techniques for Software Analytics Tasks

The advancement of machine learning (ML) models has led to the development of ML-based approaches to improve numerous software engineering tasks in software maintenance and evolution. Nevertheless, research indicates that despite their potential successes, ML models may not be employed in real-world scenarios because they often remain a black box to practitioners, lacking explainability in their reasoning. Recently, various rule-based model-agnostic Explainable AI (XAI) techniques, such as PyExplainer and LIME, have been employed to explain the predictions of ML models in software analytics tasks. This paper assesses the ability of these techniques (e.g., PyExplainer and LIME) to generate reliable and consistent explanations for ML models across various software analytics tasks, including Just-in-Time (JIT) defect prediction, clone detection, and the classification of useful code review comments. Our manual investigations find inconsistencies and anomalies in the explanations generated by these techniques. Therefore, we design a novel framework: Evaluation of Explainable AI (EvaluateXAI), along with granular-level evaluation metrics, to automatically assess the effectiveness of rule-based XAI techniques in generating reliable and consistent explanations for ML models in software analytics tasks. After conducting in-depth experiments involving seven state-of-the-art ML models trained on five datasets and six evaluation metrics, we find that none of the evaluation metrics reached 100\%, indicating the unreliability of the explanations generated by XAI techniques. Additionally, PyExplainer and LIME failed to provide consistent explanations for 86.11% and 77.78% of the experimental combinations, respectively. Therefore, our experimental findings emphasize the necessity for further research in XAI to produce reliable and consistent explanations for ML models in software analytics tasks.

cs.SE↗