Md Shahnawaj

RETHINKING REQUIREMENT ANALYSIS FOR AI-BASED PROJECTS

The rapid adoption of Artificial Intelligence (AI) across industries has revealed limitations in ... more The rapid adoption of Artificial Intelligence (AI) across industries has revealed limitations in traditional requirement analysis methodologies, which were not designed to address the complexities and iterative nature of AI-based projects. This paper proposes a refined thought process for requirement analysis tailored to the needs of AI-driven initiatives, whether AI is the primary focus or an integrated component of a larger system. By emphasizing the dynamic interplay between data, models, and deployment environments, the proposed approach departs from linear methodologies, advocating for an adaptive and iterative process. Using case studies, we demonstrate how this concept ensures better alignment with business goals, enhances data utility, and improves model performance while addressing ethical considerations and practical constraints. This paper aims to provide practitioners, researchers, and project owners with actionable insights to optimize AI project outcomes in an increasingly complex technological landscape.

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Deep Hybrid Models for Multi-Class Brain Tumor Classification: Integrating CNN, Transformers, and Explainable AI for Enhanced MRI-Based Detection

Classifications of brain tumors using MRI or magnetic resonance imaging is a critical responsibil... more Classifications of brain tumors using MRI or magnetic resonance imaging is a critical responsibility in neurooncology, directly influencing diagnosis, treatment planning, and prognosis. In like this study, We suggest a fresh deep search hybrid framework include convolutional Neural Networks (CNNs), Transformers, and Explainablinclude convolutionale AI (XAI) techniques to classify brain Low-grade glioma (LGG), high-grade glioma (HGG), tumors, and Healthy classes. Leveraging multicontrast MRI modalities (FLAIR, T1, T1ce, T2), our approach enhances both the representational depth and clinical interpretability of tumor detection systems.We introduce and evaluate five advanced architectures: (1) YoReCNNBi-Net, a YOLO-CNN

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AL-KINDI CENTER FOR RESEARCH AND DEVELOPMENT

Early-stage Parkinson's disease (Early PD) detection using speech analysis has emerged as a promi... more Early-stage Parkinson's disease (Early PD) detection using speech analysis has emerged as a promising and non-invasive approach for improving neurological healthcare in the United States. However, existing studies remain difficult to compare due to variations in datasets, speech tasks, languages, evaluation strategies, and definitions of Early PD. To address these limitations, this study proposes a comprehensive benchmark framework for speech-based Early PD detection using speaker-independent evaluation protocols to ensure fair, reproducible, and clinically reliable comparisons. The proposed benchmark evaluates multiple speech tasks under different training-resource settings and provides multidimensional performance analysis based on dataset characteristics, gender, aggregation level, and disease severity. Experimental findings offer actionable insights into the robustness and generalizability of speech-based Parkinson's detection systems. The proposed benchmark establishes a reliable reference framework for advancing explainable, scalable, and clinically meaningful Early PD detection technologies within modern U.S. healthcare and neurological diagnostic systems.

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AL-KINDI CENTER FOR RESEARCH AND DEVELOPMENT

Early-stage Parkinson's disease (Early PD) detection using speech analysis has emerged as a promi... more Early-stage Parkinson's disease (Early PD) detection using speech analysis has emerged as a promising and non-invasive approach for improving neurological healthcare in the United States. However, existing studies remain difficult to compare due to variations in datasets, speech tasks, languages, evaluation strategies, and definitions of Early PD. To address these limitations, this study proposes a comprehensive benchmark framework for speech-based Early PD detection using speaker-independent evaluation protocols to ensure fair, reproducible, and clinically reliable comparisons. The proposed benchmark evaluates multiple speech tasks under different training-resource settings and provides multidimensional performance analysis based on dataset characteristics, gender, aggregation level, and disease severity. Experimental findings offer actionable insights into the robustness and generalizability of speech-based Parkinson's detection systems. The proposed benchmark establishes a reliable reference framework for advancing explainable, scalable, and clinically meaningful Early PD detection technologies within modern U.S. healthcare and neurological diagnostic systems.

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AL-KINDI CENTER FOR RESEARCH AND DEVELOPMENT

Lung diseases continue to impose a significant healthcare burden in the United States, making ear... more Lung diseases continue to impose a significant healthcare burden in the United States, making early and accurate diagnosis essential for effective treatment and patient management. This study proposes an explainable fusion-based transfer learning framework for automated lung disease classification using chest X-ray and CT scan images. The proposed system integrates state-of-the-art deep learning architectures, including ResNet50, ResNet101, and EfficientNetB0, along with feature-level and decision-level fusion strategies to enhance classification accuracy and robustness. Experimental results demonstrate that the hybrid fusion models outperform standalone architectures, achieving 95.68% accuracy for chest X-ray classification and 99.61% accuracy for CT scan classification. To improve transparency and clinical reliability, Explainable Artificial Intelligence (XAI) methods such as SHAP, LIME, and Integrated Gradients were incorporated to visualize diagnostically relevant regions. The proposed framework offers an accurate, interpretable, and reliable AI-driven solution for pulmonary disease diagnosis in modern U.S. healthcare systems.

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