Z
Zhihua Cui
Researcher at Taiyuan University of Science and Technology
Publications - 206
Citations - 7013
Zhihua Cui is an academic researcher from Taiyuan University of Science and Technology. The author has contributed to research in topics: Particle swarm optimization & Multi-swarm optimization. The author has an hindex of 35, co-authored 189 publications receiving 4755 citations. Previous affiliations of Zhihua Cui include Beijing Institute of Petrochemical Technology & Nanjing University.
Papers
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Monarch butterfly optimization
TL;DR: A comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out, and the results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms.
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Detection of Malicious Code Variants Based on Deep Learning
TL;DR: A novel method that used deep learning to improve the detection of malware variants using a convolutional neural network that could extract the features of the malware images automatically was proposed.
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A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN
TL;DR: A blockchain based multi-WSN authentication scheme for IoT is proposed and the analysis of security and performance shows that the scheme has comprehensive security and better performance.
Book
Swarm Intelligence and Bio-Inspired Computation: Theory and Applications
TL;DR: This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions.
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Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios
TL;DR: A novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-me means) called TCCF is proposed, which can provide a higher quality recommendation by analyzing the user's behaviors and cluster similar users together for further quick and accurate recommendation.