Z
Zaiyong Tang
Researcher at Salem State University
Publications - 24
Citations - 268
Zaiyong Tang is an academic researcher from Salem State University. The author has contributed to research in topics: Feedforward neural network & Artificial neural network. The author has an hindex of 7, co-authored 24 publications receiving 243 citations. Previous affiliations of Zaiyong Tang include Concord University & University of Florida.
Papers
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Journal ArticleDOI
Giving an “e-human touch” to e-tailing: The moderating roles of static information quantity and consumption motive in the effectiveness of an anthropomorphic information agent
TL;DR: The results indicate that the anthropomorphic information agent has a positive effect when static product information on the Web site is limited and can prove detrimental when the consumer has a utilitarian consumption motive.
Book
IT-Enabled Strategic Management: Increasing Returns for the Organization
Bruce A. Walters,Zaiyong Tang +1 more
TL;DR: In this article, the authors present perspectives on IT-enabled strategic management, processes and capabilities, technology and tools, inter-organizational and global implications, and their implications.
Journal ArticleDOI
Deterministic global optimal FNN training algorithms
Zaiyong Tang,Gary J. Koehler +1 more
TL;DR: This work considered using branch-and-bound based Lipschitz optimization methods in neural network training, and developed globally optimal training algorithms (GOTA), which improve the learning efficiency of GOTA while retaining the globally convergent property.
Proceedings ArticleDOI
Ensemble methods in bank direct marketing
Youqin Pan,Zaiyong Tang +1 more
TL;DR: This paper intends to compare bagging with boosting algorithms to check how well these methods perform when class imbalance problem occurs in bank directing marketing data.
Journal ArticleDOI
Globally Convergent Particle Swarm Optimization via Branch-and-Bound
Zaiyong Tang,Kallol Bagchi +1 more
TL;DR: The BB-PSO algorithm is developed and implemented that combines the efficiency of PSO and effectiveness of the branch-and-bound method and is effective in finding global optimal solutions to problems that may cause difficulties for the PSO algorithm.