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All Journal Journal of Information Systems and Informatics Journal of Neurointervention and Stroke (JNeViS)
Islam, Md. Mominul
Unknown Affiliation
Computer Science & IT Medicine & Pharmacology Neuroscience

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The Impact of Artificial Intelligence (AI) for Transforming Tourism Marketing on the USA Industry Practices Abid, Raghib; Saha, Palash; Islam, Md. Mominul
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1016

Abstract

This study explores the transformative role of Artificial Intelligence (AI) in tourism marketing, highlighting its ability to enhance personalization, operational efficiency, and consumer engagement. The objective is to bridge the gap between theoretical capabilities and practical applications of AI in the USA tourism marketing. The methodology employs a PRISMA-based approach, focusing on recent studies from 2020 onward to analyze AI’s impact on marketing practices. A thorough examination of 389 publications obtained from databases like Scopus, Google Scholar, and Scimago for detailed qualitative analysis. The key contribution of this paper lies in its structural approach, which discuss the potential of various AI tools such as tailored recommendations and AI chatbots etc., offering fresh insights on their influence on the American Tourism Marketing sector. The report presents a framework for assessing the impact of AI on customer satisfaction and productivity, providing pragmatic solutions for tourism enterprises. In near future AI will develop enhancing human critical thinking and converting human cognition capabilities.
Enhancing Stroke Risk Prediction with Explainable AI: Leveraging Resampling and Machine Learning for Improved Accuracy Mahin, Minhazul Alam; Islam, Md. Mominul; Alam, Md. Zulfikar; Pollob, Arnob Dutta; Zaman, Oxita
Journal of Neurointervention and Stroke Vol. 1 No. 2: NOVEMBER 2025
Publisher : Neurointervention Working Group of Indonesian Neurological Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63937/jnevis-2025.12.16

Abstract

Highlight: Advanced resampling techniques improved class balance in stroke datasets Gradient Boosting with SMOTE reached 92% accuracy with SHAP interpretability ABSTRACT Introduction: Stroke represents a significant global health concern, impacting millions worldwide and contributing substantially to morbidity and mortality. Early detection and accurate risk prediction remain critical for effective prevention strategies. Objective: This study aimed to improve stroke risk prediction by employing machine learning algorithms on health survey data to identify key predictors and enhance predictive performance. Method: A dataset derived from the National Health and Nutrition Examination Survey, comprising 4,603 participants, was utilized. The dataset exhibited class imbalance, with only 7.86% of individuals diagnosed with stroke. To address this imbalance, advanced resampling techniques, including SMOTE, SMOTETomek, and ADASYN, were applied. A range of tree-based algorithms was implemented, including Gradient Boosting, AdaBoost, XGBoost, and a Voting Classifier integrating Decision Tree, AdaBoost, and Gradient Boosting classifiers. Model evaluation included accuracy and AUC scores. Explainable Artificial Intelligence (XAI) analyses were conducted using SHAP (SHapley Additive exPlanations) to interpret feature importance. Result: The Gradient Boosting classifier, in conjunction with SMOTE, achieved the highest performance with an accuracy of 92% and an AUC score of 0.70. SHAP analysis identified age, general health condition, marital status, and BMI as the most influential predictors of stroke risk. Conclusion: This study underscores the essential need for ongoing advancements in early stroke detection methodologies. The findings highlight the transformative potential of machine learning and XAI in predictive healthcare, offering valuable insights for stroke prevention strategies.
Co-Authors Abid, Raghib Alam, Md. Zulfikar Mahin, Minhazul Alam Pollob, Arnob Dutta Saha, Palash Zaman, Oxita