Faciliatating complex Web queries through visual user interfaces and query relaxation
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Supporting web query expansion efficiently using multi-granularity indexing and query processing
Data & Knowledge Engineering
A Fuzzy Semantic Approach to Retrieving Bird Information Using Handheld Devices
IEEE Intelligent Systems
Customer-Driven Sensor Management
IEEE Intelligent Systems
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
The phrase-based vector space model for automatic retrieval of free-text medical documents
Data & Knowledge Engineering
A review of ontology based query expansion
Information Processing and Management: an International Journal
Query expansion with terms selected using lexical cohesion analysis of documents
Information Processing and Management: an International Journal
A mixed neural-genetic algorithm for the broadcast scheduling problem
IEEE Transactions on Wireless Communications
Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions
IEEE Transactions on Evolutionary Computation
Genetic design of biologically inspired receptive fields for neural pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving the Identifying Code Problem by a Genetic Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
This paper presents a knowledge-based plant information retrieval system that is robust to inaccurate and erroneous user queries. First, a knowledge-based genetic algorithm (GA) corrects the erroneous input vectors before these are fed into a back-propagation neural network (BPNN) that performs the actual query. Experimental results show that the strategy achieves a 75% recall rate and 25% precision rate with a cutoff level of 10 under the misjudgment of shapes. Moreover, a fully trained BPNN dynamically adapts to changes in the environment. Due to its robust and simple user interface and portability, the strategy is particularly applicable to educational settings such as outdoor fieldwork in courses on ecology.