Research Areas
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Biography and Research Information
OverviewAI-generated summary
Shalina Sultana Champa's research focuses on the application of artificial intelligence and machine learning across various domains. Her recent work includes developing integrated data analytics, business intelligence, and machine learning architectures for small and medium-sized enterprises (SMEs), as well as a unified AI and machine learning framework for optimization in smart manufacturing. Champa has also investigated the use of machine learning for early outcome prediction in pediatric neuroblastoma using structured clinical EHR data and explored explainable AI for detecting customer dissatisfaction in e-commerce. Additionally, her research extends to trustworthy health digital twins for clinical decision support and the adoption of generative AI in higher education. She has 11 publications with 3 citations and an h-index of 1. Champa collaborates with Richard S. Segall, with whom she shares two publications.
Metrics
- h-index: 1
- Publications: 14
- Citations: 3
Positions
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Research Assistant 2022–presentArkansas State University Business Analytics ORCID
Selected Publications
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Early Outcome Prediction in Pediatric Neuroblastoma Using Machine Learning on Structured Clinical EHR Data: A Reproducible Methodological Framework (2026)
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Replication package for: Explainable Artificial Intelligence for Detecting Customer Dissatisfaction in Ecommerce Service Reviews: Evidence from Behavioral Signals (2026)
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Toward Trustworthy Health Digital Twins: A Proof-of-Concept Design and Validation Framework for Clinical Decision Support (2026)
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Transforming E-Commerce Through AI: From Chatbots to Predictive Analytics (2026)
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Artificial Intelligence and Machine Learning Framework for Smart Manufacturing Optimization (2026)
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Integrated Data Analytics, Business Intelligence, and Machine Learning Architecture for SMEs (2026)
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Design Principles for Equity-Responsive Generative AI in IT Education and Workforce Upskilling (2026)
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Intelligent Multi-Layer Routing Framework for Energy-Efficient Nanosensor Networks: An AI-Driven Approach (2026)
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A Unified AI and Machine Learning Framework for Decision Support and Operational Optimization in Smart Manufacturing (2026)
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AI and Business Intelligence Integration: A Critical Factor for Innovation in Manufacturing Industries (2025)
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The Effects of Globalization and Competitiveness on Strategic Management (2025)
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Reassessing Corporate Direction in an Era of Global Integration and Hyper-Competition (2025)
Collaboration Network
Top Collaborators
- Integrated Data Analytics, Business Intelligence, and Machine Learning Architecture for SMEs
- Artificial Intelligence and Machine Learning Framework for Smart Manufacturing Optimization
- Replication package for: Explainable Artificial Intelligence for Detecting Customer Dissatisfaction in Ecommerce Service Reviews: Evidence from Behavioral Signals
- Early Outcome Prediction in Pediatric Neuroblastoma Using Machine Learning on Structured Clinical EHR Data: A Reproducible Methodological Framework
- Replication package for: Explainable Artificial Intelligence for Detecting Customer Dissatisfaction in Ecommerce Service Reviews: Evidence from Behavioral Signals
- Early Outcome Prediction in Pediatric Neuroblastoma Using Machine Learning on Structured Clinical EHR Data: A Reproducible Methodological Framework
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