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Aryya Gangopadhyay
Researcher at University of Maryland, Baltimore County
Publications - 170
Citations - 1932
Aryya Gangopadhyay is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Computer science & Decision support system. The author has an hindex of 22, co-authored 148 publications receiving 1643 citations. Previous affiliations of Aryya Gangopadhyay include University of Maryland, Baltimore & University of Maryland, College Park.
Papers
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Discrete wavelet transform-based time series analysis and mining
TL;DR: A systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection are outlined.
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A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms
TL;DR: A novel generalized approach using the well-known energy compaction power of Fourier-related transforms to hide sensitive data values and to approximately preserve Euclidean distances in centralized and distributed scenarios to a great degree of accuracy is proposed.
Book
Electronic Commerce: Technical, Business, and Legal Issues
TL;DR: The business case for EC, the technical issues for Electronic Commerce, and the implications for economic, social, and Cultural issues are outlined.
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Fuzzy Approach Topic Discovery in Health and Medical Corpora
TL;DR: F fuzzy latent semantic analysis (FLSA) is described, a novel approach in topic modeling using fuzzy perspective that can handle health and medical corpora redundancy issue and provides a new method to estimate the number of topics.
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A Simulation Study of Supply Chain Management to Measure the Impact of Information Sharing
Zhensen Huang,Aryya Gangopadhyay +1 more
TL;DR: A simulation study is presented to investigate the effectiveness of information sharing in the supply chain, showing that from the perspectives of end inventory and back-order quantities, distributors and wholesalers gain significantly from information sharing, while not much gain can be realized for retailers.