V
Victoria J. Hodge
Researcher at University of York
Publications - 58
Citations - 4458
Victoria J. Hodge is an academic researcher from University of York. The author has contributed to research in topics: Artificial neural network & Pattern matching. The author has an hindex of 15, co-authored 54 publications receiving 3876 citations. Previous affiliations of Victoria J. Hodge include Universities UK.
Papers
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Journal ArticleDOI
A Survey of Outlier Detection Methodologies
Victoria J. Hodge,Jim Austin +1 more
TL;DR: A survey of contemporary techniques for outlier detection is introduced and their respective motivations are identified and distinguish their advantages and disadvantages in a comparative review.
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Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
TL;DR: Practical engineering solutions are focused on which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies, which identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
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A comparison of standard spell checking algorithms and a novel binary neural approach
Victoria J. Hodge,Jim Austin +1 more
TL;DR: This paper proposes a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system that has the highest recall rate of the techniques evaluated.
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Hierarchical growing cell structures: TreeGCS
Victoria J. Hodge,Jim Austin +1 more
TL;DR: The proposed TreeGCS algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors.
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Deep reinforcement learning for drone navigation using sensor data
TL;DR: This paper describes a generic navigation algorithm that uses data from sensors on-board the drone to guide the drones to the site of the problem and uses the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement it.