An Evaluation of Classification and Outlier Detection Algorithms

This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-series datasets. The analyses demonstrate that the choice of algorithm is task and data specific but that we can derive heuristics for choosing. Gradient Boosting Machines are generally best for classification but there is no single winner for outlier detection though Gradient Boosting Machines (again) and Random Forest are better. Hence, we recommend running evaluations of a number of algorithms using our heuristics

A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes

The Signal Data Explorer: A high performance Grid based signal search tool for use in distributed diagnostic applications

We describe a high performance Grid based signal search tool for distributed diagnostic applications developed in conjunction with Rolls-Royce plc for civil aero engine condition monitoring applications. With the introduction of advanced monitoring technology into engineering systems, healthcare, etc., the associated diagnostic processes are increasingly required to handle and consider vast amounts of data. An exemplar of such a diagnosis process was developed during the DAME project, which built a proof of concept demonstrator to assist in the enhanced diagnosis and prognosis of aero-engine conditions. In particular it has shown the utility of an interactive viewing and high performance distributed search tool (the Signal Data Explorer) in the aero-engine diagnostic process. The viewing and search techniques are equally applicable to other domains. The Signal Data Explorer and search services have been demonstrated on the Worldwide Universities Network to search distributed databases of electrocardiograph data

An Evaluation of Phonetic Spell Checkers

In the work reported here, we describe a phonetic spell-checking algorithm integrating aspects of Soundex and Phonix. We increase the number of letter codes compared to Soundex and Phonix. We also integrate phonetic rules but use far less than Phonix where retrieval may be slow due to the computational cost of comparing the input to a large list of transformation rules. Our algorithm aims to repair spelling errors where the user has substituted homophones in place of the correct spelling. We evaluate our algorithm by comparing it to three alternative spell-checking algorithms and three benchmark spell checkers (MS Word 97 & 2000 and UNIX `ispell') using a list of phonetic spelling errors. We find that our approach has superior recall (percentage of correct matches retrieved) to the alternative approaches although the higher recall is at the expense of precision (number of possible matches retrieved). We intend our phonetic spell checker to be integrated into an existing spell checker so the precision will be improved by integration thus high recall is the aim for our approach in this paper

A Binary Neural Network Framework for Attribute Selection and Prediction

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus journey times from traffic sensor data and show how attribute selection can both speed our k-NN and increase the prediction accuracy by removing noise and redundant attributes from the data

Sequential Data Mining using Correlation Matrix Memory

This paper proposes a method for sequential data mining using correlation matrix memory. Here, we use the concept of the Logical Match to mine the indices of the sequential pattern. We demonstrate the uniqueness of the method with both the artificial and the real datum taken from the NCBI databank

Software Sustainability: The Modern Tower of Babel

<p>The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability?</p

Junior Recital

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Ms. Austin is a student of John Lawless.https://digitalcommons.kennesaw.edu/musicprograms/1606/thumbnail.jp