Browsing by Author "Md. Adham Wahid"

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    Disaster Rescue AI: Advanced AI for Finding Alive People in Disasters and Alert System
    (North South University, 2025-04-30) Md. Adham Wahid; Fatema Afsan Ema; Istiak Ahasan; Dr. Sifat Momen; 2111177042; 2022281642; 2012082642
    A developmental illness that may manifest in early childhood is Attention Deficit Hyperactivity Disorder (ADHD). It may lead to poor self-esteem and social function in children if it’s not considered at an early age. Early detection and diagnosis of ADHD results in early interventions that improve social development, academic performance, and treatment outcomes among children. This is the first integrated framework that not only predicts ADHD in early childhood using Machine learning and Natural language processing techniques but also offers a Dialogflow-based therapeutic chatbot as a primary intervention an approach not addressed in existing literature. A total of fourteen Machine Learning and five BERT-based transformer algorithms with optimized hyperparameters have been applied to classify ADHD-positive and ADHD-free children identifying the key factors. The Stacking ensemble model came out with the best-performing metrics with 94.27% accuracy, 87.00% precision, 82.07% recall and 84.30% f1 score, and AUC of 0.97 that achieves state-of-the-art performance on CAHMI survey and dataset. While DistilBERT outshined among other BERT-based transformer models with 93.45% accuracy, 19.25s runtime, and AUC of 0.968 highlighting the natural language processing. LIME and SHAP have added enhanced transparency and dynamic decision-making interpretability. Unlike prior studies that focus solely on prediction, our model emphasizes both early diagnosis and immediate therapeutic support, bridging a critical gap between detection and intervention. Moreover, domain experts raw feedback and generalization through unseen datasetscontribute significantly to the novelty and practical relevance of the proposed system. This study presents a comprehensive statistical analysis through a well-structured machine learning and transformer pipeline, incorporating detailed visualizations alongside textual explanations.