Artificial neural networks for cancer research: Outcome prediction

Burke, Harry B.

doi:10.1002/ssu.2980100111

Cited by 71 publications

(49 citation statements)

References 32 publications

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“…Examples include the use of ANN to predict medical outcomes (e.g. [13], [14]) and in financial analysis, for modelling stock performance (e.g. [9]).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Application of machine learning for fuel consumption modelling of trucks

Perrotta

Parry

Neves

2017

2017 IEEE International Conference on Big Data (Big Data)

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-This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R2, and lower error.

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“…Examples include the use of ANN to predict medical outcomes (e.g. [13], [14]) and in financial analysis, for modelling stock performance (e.g. [9]).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…[11], [12]), and health monitoring (e.g. [13], [14]). More recently, these techniques have also been applied to estimate the fuel consumption of road vehicles (e.g.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning for fuel consumption modelling of trucks

Perrotta

Parry

Neves

2017

2017 IEEE International Conference on Big Data (Big Data)

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“…A wide variety of applications in medical sciences have been developed like tumor detection in ultra sonograms, classification of chest x-rays, detection of calcification in mammograms, classification of cervical cancer using pap smears, analyzing breast cancer and cardiology etc [10][11][12][13][14][15][16][17][18][19][20]. So it can easily be deduced that neural networks can be used in predictions of various stages of different diseases [21].…”

Section: Neural Network In Medical Diagnosismentioning

confidence: 99%

Preserving Privacy and Optimizing Neural Network Classification by using a Mix of Soft Computing Techniques

Malik¹,

Asger²,

Ali³

et al. 2016

IJCA

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An Artificial Neural Network (ANN) commonly called as Neural Network (NN) is a mathematical tool that is used in data mining for the analysis of data. Inherently human brain has its own limitations, the inability to handle large volumes of data being one of them. NNs with its power to handle large volumes of data along with imitation of human brain provides good solutions where the size of data is too large, beyond human interpretation capacity or beyond the capacity of conventional computational methods. NNs find applicability in healthcare among other fields like business, genetics, bioinformatics, pharmaceuticals, etc. NNs are mostly not used in data mining due to the reason that it takes lots of time for training of the networks. But as far as the utility of NNs in data mining is concerned, it is a valuable technique. Data mining in general and NNs in particular are being used in various areas where privacy of individuals is at stake. This study attempts to optimize the results of NN classification on one hand and address the problem of training time consumption on the other along with preserving privacy of the stakeholders.

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“…In recent years, there has been an increased interest in the application of machine learning methods to prediction using censored data. Several groups have approached prognosis as a separation problem using different learning architectures, including backpropagation artificial neural networks (ANNs) (Burke, 1994;Burke et al, 1997), entropy maximization networks (Choong et al, 1996) and decision trees (Wolberg et al, 1992;Wolberg et al, 1994). This is done by choosing one or more endpoints and learning a yes/no classifier on concepts such as "patients who recurred in less than two years."…”

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confidence: 99%

Machine Learning: Proceedings of the Fifteenth International Conference

Shavlik¹

1998

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503. AGENCY USE ONLY (Leave blank)2. REPORT DATE July 1998 REPORT TYPE AND DATES COVEREDFinal, 7/10/98-9/30/98 TITLE AND SUBTITLEMachine Learning: Proceedings of the Fifteenth International Conference AUTHOR(S)Jude Shavlik (editor) FUNDING NUMBERSN00014-98-1-0810 PERFORMING ORGANIZATION NAMES(S) AND ADDRESS(ES)Computer Sciences Department University of Wisconsin 1210 W. Dayton Street Madison, WI 53706 PERFORMING ORGANIZATION REPORT NUMBER144-HD17 SPONSORING /MONITORING AGENCY NAMES(S) AND ADDRESS(ES)Office All rights reserved. Printed in the United StatesNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means^lectronic, mechanical, photocopying, recording, or otherwise without the prior written permission of the publisher. PrefaceThis volume contains the 66 technical papers presented at the Fifteenth International Conference on Machine Learning (ICML '98), held July 24r-27,1998, in Madison, Wisconsin U.S.A. These articles were selected based on the rigorous review and discussion of 215 submissions. All papers were presented orally as well as at an evening poster session.ICML '98 was one of ten AI-related conferences held in Madison during mid-summer 1998, in an ambitious, first-time experiment to see what kind of synergies would result from all this collocation. In particular, ICML '98 was held in the same building as, and concurrently with, the Computational Learning Theory (COLT) and Uncertainty in Artificial Intelligence (UAI) conferences. It also overlapped one day with the Inductive Logic Programming (ILP) conference.Registrants were allowed to attend, without additional costs, the technical sessions of the other conferences. COLT, ICML, and UAI each invited one of the plenary speakers and jointly invited the banquet speaker, plus there was a joint poster session and wrapup panel. The poster session contained about 150 papers, allowing conference attendees a chance to see or further discuss research presented during the four to five parallel tracks and fostering interaction among the various communities.I especially wish to thank:• the authors of all the papers for their technical contributions toward the advancement of machine learning.• Richard Sutton, who was the IMCL representative among the three joint invited speakers and spoke on "Reinforcement Learning: Ho...

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Artificial neural networks for cancer research: Outcome prediction

Cited by 71 publications

References 32 publications

Application of machine learning for fuel consumption modelling of trucks

Application of machine learning for fuel consumption modelling of trucks

Preserving Privacy and Optimizing Neural Network Classification by using a Mix of Soft Computing Techniques

Machine Learning: Proceedings of the Fifteenth International Conference

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