Multiclass Classification using Neural Networks and Interval Neutrosophic Sets

Size: px
Start display at page:

Download "Multiclass Classification using Neural Networks and Interval Neutrosophic Sets"

Transcription

1 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, Multicla Claification uing Neural Network and Interval Neutroophic Set PAWALAI KRAIPEERAPUN CHUN CHE FUNG KOK WAI WONG School of Information Technology Murdoch Univerity Perth, Wetern Autralia AUSTRALIA {p.kraipeerapun l.fung Abtract:- Thi paper preent a new approach to the problem of multicla claification. The propoed approach ha the capability to provide an aement of the uncertainty value aociated with the reult of the prediction. Two feed-forward backpropagation neural network, each with multiple output, are ued. One network i ued to predict degree of truth memberhip and another network i ued to predict degree of fale memberhip. Indeterminacy memberhip or uncertainty in the prediction of thee two memberhip i alo etimated. Together thee three memberhip value form an interval neutroophic et. Hence, a pair of ingle multicla neural network with multiple output produce multiple interval neutroophic et. We experiment our technique to the claical benchmark problem including balance, ecoli, gla, lene, wine, yeat, and zoo from the UCI machine learning repoitory. Our approach improve claification performance compared to an exiting technique which applied only to the truth memberhip created from a ingle neural network with multiple output. Key Word:- multicla claification, uncertainty, interval neutroophic et, multicla neural network, feedforward backpropagation neural network 1 Introduction Multicla neural network claification involve building neural network that map the input feature vector to the network output containing more than two clae [1]. In general, there are two exiting neural network architecture ued to claify multiple clae. The firt approach i to build multiple binary neural network in which each network can be modeled independently. One advantage of uing thi technique i that different feature can be applied to train different neural network [2]. However, each neural network i trained only baed on local knowledge which may produce overlap or gap in the claification boundary zone [1]. The econd approach i the implementation of a ingle neural network with multiple output. The complexity of thi approach i uually high [3]. However, the claification boundarie are harp [1]. In order to avoid uncertainty in the claification boundary zone, the econd approach i applied in thi paper. The multiple output of a ingle neural network can be modeled uing a ditributed output code in which each cla i aigned a unique codeword, which i a binary tring of length n. The column of the codeword hould neither identical nor complementary in order to avoid error correlation [4]. There are variou technique to define a codeword [3, 4, 5]. One of the model uing a imple codeword i One- Againt-All neural network (OAA). The length of the codeword ued in thi model i equal to the number of clae. For a k-cla neural network, the codeword for the i-th cla can be defined with the length k. The bit in the codeword at the i-th poition i equal to 1, and the ret i equal to 0. In the teting phae, a ample i aigned to the i-th cla if the network output at the i-th poition ha the highet confidence value. In order to keep our approach imple, we apply thi model to our propoed model. Hanen and Salamon [6] uggeted that enemble of accurate and divere neural network give better reult and le error than a ingle neural network. Diverity can be conducted by manipulating input data or output data. Deigning the codeword for multicla neural network i an example of manipulating the diverity uing output data [7]. Example of the enemble diverity uing input data are bagging [8] and booting [9] neural network. Bagging provide diverity by randomly reampling the original training data into everal training et wherea booting provide diverity by manipulating each training et

2 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, according to the performance of the previou claifier. Diverity can alo be provided by uing artificial training ample. Melville and Mooney [10] built each training et for a new committee by adding artificially contructed ample to the original training data. They aigned the cla label that diagree with the current enemble to the contructed ample label. In thi paper, we implement diverity neural network enemble uing two network which are oppoite to each other. Two multicla neural network are trained with the ame input feature vector but diagree in the target codeword. The firt network predict the degree of truth memberhip and the econd network predict the degree of fale memberhip. The boundary between thee two predicted output may not be harp. Uncertainty can occur in the boundary zone. Thi paper alo etimate thi uncertainty and repreent it in the form of indeterminacy memberhip. Thee three memberhip form an interval neutroophic et [11]. The final deciion of the claification i decided from thee three memberhip. The ret of thi paper i organized a follow. Section 2 preent the baic theory of interval neutroophic et. Section 3 explain the propoed model for the muticla neural network claification with the aement of uncertainty uing interval neutroophic et. Section 4 decribe the data et and the reult of our experiment. Concluion and future work are preented in Section 5. 2 Interval Neutroophic Set In our previou paper [12, 13], we applied an interval neutroophic et to the problem of binary claification. We found that an interval neutroophic et can repreent uncertainty information and upport the binary claification quite well. In order to expand our approach to repreent uncertainty in the multicla claification, an interval neutroophic et i alo ued in thi paper. An interval neutroophic et i an intance of a neutroophic et, which i generalized from the concept of claical et, fuzzy et, interval valued fuzzy et, intuitionitic fuzzy et, interval valued intuitionitic fuzzy et, paraconitent et, dialetheit et, paradoxit et, and tautological et [14]. The memberhip of an element to the interval neutroophic et i expreed by three value: t, i, and f. Thee value repreent truth memberhip, indeterminacy memberhip, and fale memberhip, repectively. The three memberhip are independent. In ome pecial cae, they can be dependent. In thi tudy, the indeterminacy memberhip depend on both truth and fale memberhip. The three memberhip can be any real ub- Input feature vector Truth Multicla NN Indeterminacy memberhip Fality Multicla NN Truthmemberhip Fale memberhip C l a i f i c a t i o n P r o c e Output Figure 1: Multicla neural network model baed on interval neutroophic et (INS). unitary ubet and can repreent imprecie, incomplete, inconitent, and uncertain information [14]. In thi paper, the memberhip are ued to repreent uncertainty information. Thi reearch follow the definition of interval neutroophic et that i defined in [11]. Thi definition i decribed below. Let X be a pace of point (object). An interval neutroophic et in X i defined a: A = {x(t A (x), I A (x), F A (x)) x X T A : X [0, 1] I A : X [0, 1] F A : X [0, 1]} where T A i the truth memberhip function, I A i the indeterminacy memberhip function, F A i the fale memberhip function. (1) 3 The Propoed Multicla Claification In thi paper, enemble of two multicla neural network with multiple output are created to claify multiple clae. We apply one-againt-all neural network model to the two neural network. Fig.1 how our propoed model that conit of a et of input feature vector, two multicla neural network, three memberhip, a claification proce, and a final output. In thi experiment, the Truth Multicla NN i a feed-forward backpropagation neural network with multiple output. Thi network i trained to predict degree of truth memberhip. For a k-cla truth neural network, the length of a codeword i equal to k. The codeword for the i-th cla ha a bit at the i-th

3 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, poition equal to 1, and the ret i equal to 0. The Fality Multicla NN i alo a feed-forward backpropagation neural network with multiple output. It ha the ame architecture and propertie a the one ued for the truth neural network. The only difference i that the fality network i trained to predict degree of fale memberhip uing the complement of target codeword ued for training data in the truth network. For example, if the codeword ued to train the truth network for the i-th cla at the i-th bit i equal to 1 and the ret i equal to 0, then the codeword ued to train the fality network for the i-th cla at the i-th bit i equal to 0 and the ret i equal to 1. For a given unknown input pattern, let T j be the truth memberhip of the j-th output for the truth network. Let F j be the fale memberhip of the j-th output for the fality network. Thee two predicted output are uppoed to be oppoite. If the truth memberhip value i high then the fale memberhip value hould be low, and vie vera. Otherwie, uncertainty occur in the prediction of thee two output. Hence, one way to repreent the degree of uncertainty in the prediction or indeterminacy memberhip value can be calculated a the difference between the truth and fale memberhip value. If the difference between thee two value i high then the uncertainty i low. In contrat, if the difference between both value i low then the uncertainty i high. Let I j be the indeterminacy memberhip of the j-th output. The indeterminacy memberhip value can be calculated a I j = 1 T j F j. The three memberhip form an interval neutroophic et. Let A j be an interval neutroophic et of the j-th output. A j can be defined a A j = {x(t Aj (x), I Aj (x), F Aj (x))} where T Aj i the truth memberhip function of the j-th output, I Aj i the indeterminacy memberhip function of the j-th output, and F Aj i the fale memberhip function of the j-th output. Intead of uing only the truth memberhip, we apply the three memberhip of an element in an interval neutroophic et to claify multiple clae. Hence, the predicted binary tring i created from the truth, indeterminacy, and fale memberhip value. In order to create the predicted binary tring with the length k where k i the number of cla, each bit repreenting each output in the multiple output i conidered. For the j-th output, if T j > F j then the bit in the binary tring at the j-th bit i equal to 1. Otherwie, the bit i equal to 0. In the claification, the predicted binary tring will be matched to the truth codeword. In order to match thee two tring, the predicted bit tring mut have only one bit equal to 1 and the ret i equal to 0. However, if all bit are equal to 0 then the bit that ha the highet indeterminacy memberhip value will be changed from 0 to 1. If there are more than one bit equal to 1 then the bit that ha a value 1 with the minimum indeterminacy memberhip value will be aigned a value 1, and the ret will be 0. The unknown input pattern i aigned to the i-th cla if it predicted binary tring matche the codeword that ha the i-th bit equal to 1. The degree of uncertainty in the claification can be expreed uing the average indeterminacy memberhip value of the predicted multiple output. 4 Experiment 4.1 Data et Seven data et from UCI Repoitory of machine learning data et [15] are employed in thi paper. Table 1 ummarie the characteritic of thee even data et. Table 1: Data et ued in thi tudy. Name No. of No. of Feature Size of Clae Feature Type Sample balance 3 4 numeric 625 ecoli 8 7 numeric 336 gla 7 9 numeric 214 lene 3 4 nominal 24 wine 3 13 numeric 178 yeat 10 8 numeric 1484 zoo 7 16 numeric, 101 nominal 4.2 Experimental methodology and reult In thi paper, even data et from UCI Repoitory are ued to tet our propoed model. Thee data et are balance-cale, ecoli, gla, lene, wine, yeat, and zoo. Each data et i plit into a training et containing 80% of the data and a teting et containing 20% of the data. For each UCI data et ued in thi experiment, twenty pair of feed-forward backpropagation neural network with multiple output are trained with twenty different randomized training et. For each pair of neural network, the firt network i ued a the Truth Multicla NN to predict degree of truth memberhip and another network i ued a the Fality Multicla NN to predict degree of fale memberhip. The number of input-node and outputnode for each network are equal to the number of feature and the number of clae, repectively. Both network include one hidden layer contituting of 2n neuron where n i the number of feature. The ame parameter value are applied to the two network and

4 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, Table 2: Average claification accuracy for the tet data et obtained by applying the propoed model uing the three memberhip memberhip (T IF ) and the exiting model uing only the truth (T ) memberhip. Name %correct %correct (T IF ) (T ) balance ecoli gla lene wine yeat zoo both network are initialized with the ame random weight. The only difference i that the target codeword for the fality network are equal to the complement of the target codeword ued to train the truth network. The indeterminacy memberhip value i calculated uing the different between the truth and fale memberhip value for each pair of network. After the three memberhip value are determined for training and tet et, the truth, indeterminacy, and fale memberhip value are ued to create the predicted binary tring uing our propoed technique explained in the previou ection. After that, the predicted binary tring are matched to the truth codeword in order to claify multiple clae. For each UCI data et, twenty claification reult are averaged. The average percentage of the correct claification reult for the tet data are hown in Table 2. In thi table, the reult from our propoed model are compared to the reult from the exiting one-againtall (OAA) neural network model that applie to only the truth memberhip for the multicla claification. The table how that ix reult produced from our technique outperform the reult produced from the exiting technique. Furthermore, we alo compare our reult to the reult produced from [16]. In [16], Draghici created the contraint baed decompoition (CBD) technique, a contructive neural network technique guaranteed the convergence and can deal with both binary and multicla problem. In hi experiment, ome data et from UCI machine learning repoitory were applied and each data et wa randomly plit into 80% training et and 20% tet et. He compared the reult obtained from CBD and reult obtained from other exiting machine learning technique by reporting the average reult for tet data over five trail. In order to compare the reult obtained from our propoed technique to the reult obtained from other neural and non-neural machine learning technique, we compare our reult to ome of the exiting reult obtained from hi experiment in [16]. Table 3 how claification accuracy comparion between our propoed technique (column 2) and everal exiting technique obtained from [16] (column3-13). Thee exiting technique include C4.5, C4.5 uing claification rule (C4.5r), incremental deciion tree induction (ITI), linear machine deciion tree (LMDT), learning vector quantization (LVQ), induction of oblique tree (OCI), Nevada backpropagation (NEVP), k-nearet neighbor with k=5 (K5), Q*, and contraint baed decompoition (CBD). In addition, our approach ha an ability to repreent uncertainty in the claification. Uncertainty in the claification for each input pattern can be calculated a the average of indeterminacy memberhip value produced from the multiple output of the truth and fality neural network. Table 4 how ample of individual predicted output and their uncertaintie reulted from our propoed model for the tet et of ecoli data et. The individual predicted output for the traditional approach applying only the truth memberhip value are alo hown in thi table under the heading (one-againtall). Uncertainty of individual predicted output can be ued to enhance and upport the confidence in the claification. For example, the actual value for the output in the fifth row of thi table i im, but our propoed model claifie thi output a imu, which i wrong. However, uncertainty for thi output i which i very high comparing to the maximum uncertainty which i Hence, the deciion maker can claify the unknown pattern by uing uncertainty value to upport their confidence in the claification. Conidering the lat row of data from thi table, both predicted output claified from our approach and the traditional approach are incorrectly claified. The traditional approach cannot provide uncertainty information for thi claification, but our approach can explain that the output i miclaified with the uncertainty value Hence, the deciion-maker can ue thi information to upport the confidence in deciion making. The table ha hown a comparion of the uncertainty value a Low, Med and High. It can be een that in all cae that when the uncertainty value are Low, a correct reult i predicted for both approache. When the value are Medium, 3 out of 4 of the prediction from the traditional approach are wrong where a the propoed approach yield correct reult. Finally, when the uncertainty value i High, the prediction ha been wrong and attention from the operator hould be drawn in uch cae.

5 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, Table 3: Claification accuracy comparion between our propoed technique (column TIF) and everal exiting technique from Draghici [16]. Name TIF C4.5 C4.5r ITI LMDT CN2 LVQ OC1 NEVP K5 Q* CBD balance gla wine zoo Table 4: Sample output from the traditional claification baed on truth memberhip value (One-againt-all) compared to the propoed model for the tet et of ecoli data et (propoed OAA). Actual value Predicted value Predicted value Uncertainty value (One-againt-all) (propoed OAA) cp cp cp Low cp cp cp Low im cp im Med im im im Low im im imu High im cp cp High imu im imu Med om oml om Med pp pp pp Med pp cp cp High 3 error are marked by 5/10 correct 7/10 correct high uncertainty value 5 Concluion and Future Work In thi paper, we apply an interval neutroophic et to the multicla claification. Two neural network with multiple output are created for the prediction of the truth memberhip and fale memberhip value. Thee two memberhip value are then ued to calculate an indeterminacy memberhip value. The three memberhip value contitute an interval neutroophic et. Multiple interval neutroophic et are created for multiple output and are ued to claify the input pattern into multiple categorie. Uncertainty in the claification i calculated from the average indeterminacy memberhip value produced from the multiple output for each pattern. The advantage of our propoed model over a traditional OAA approach i that the indeterminacy memberhip value provide an etimate of the uncertainty in the multicla claification. Moreover, our experimental reult indicate that our propoed model improve the claification performance compared to the exiting OAA model applied only to the truth memberhip value. In the future, interpolation technique will be applied to our approach in order to quantify uncertainty in the multicla claification. Furthermore, we will apply our technique to a real world problem of well log data analyi in the oil and ga indutry. Reference: [1] G. Ou, Y. L. Murphey and L. A. Feldkamp, Multicla Pattern Claification Uing Neural Network, in Proceeding of the 17th International Conference on Pattern Recognition (ICPR), 2004, pp [2] Y. L. Murphey and Y. Luo, Feature Extraction for a Multiple Pattern Claification Neural Network Sytem, in Proceeding of the 16th International Conference on Pattern Recognition (ICPR), 2002, pp [3] R. Erenhteyn, P. Lakov, D. M. Saxe and R. A. Fould, Ditributed Output Encoding for Multi- Cla Pattern Recognition, in Proceeding of the 1oth International Conference on Image Analyi and Proceing (ICIAP), 1999, pp [4] T. G. Dietterich and G. Bakiri, Solving Multicla Learning Problem via Error-Correcting Output Code, Journal of Artificial Intelligence Reearch, Vol. 2, 1995, pp [5] K. Crammer and Y. Singer, On the Learnability and Deign of Output Code for Multicla Prob-

6 Proceeding of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, lem, Machine Learning, Vol. 47, No. 2-3, 2002, pp [6] L. K. Hanen and P. Salamon, Pattern Analyi and Machine Intelligence, in IEEE Tranaction on Pattern Analyi and Machine Intelligence, Vol. 12, October 1990, pp [7] G. Brown, J. Wyatt, R. Harri and X. Yao, Diverity Creation Method: A Survey and Categoriation, Journal of Information Fuion, Vol. 6, No. 1, 2005, pp [8] L. Breiman, Bagging Predictor, Machine Learning, Vol. 24, No. 2, 1996, pp [9] H. Schwenk and Y. Bengio, Booting Neural Network, Neural Computation, Vol. 12, No. 8, 2000, pp [10] P. Melville and R. J. Mooney, Contructing Divere Claifier Enemble uing Artificial Training Example, in Proceeding of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), 2003, pp [11] H. Wang, D. Madiraju, Y.-Q. Zhang and R. Sunderraman, Interval neutroophic et, International Journal of Applied Mathematic and Statitic, Vol. 3, March 2005, pp [12] P. Kraipeerapun, C. C. Fung and W. Brown, Aement of Uncertainty in Mineral Propectivity Prediction Uing Interval Neutroophic Set, Lecture Note in Artificial Intelligence, Springer Verlag, LNAI 3802, 2005, pp [13] P. Kraipeerapun, C. C. Fung, W. Brown and K. W. Wong, Mineral Propectivity Prediction uing Interval Neutroophic Set, in Proceeding of IASTED International Conference on Artificial Intelligence and Application, 2006, pp [14] H. Wang, F. Smarandache, Y.-Q. Zhang and R. Sunderraman, Interval Neutroophic Set and Logic: Theory and Application in Computing, Neutroophic Book Serie, No.5. May [15] D. Newman, S. Hettich, C. Blake and C. Merz, UCI Repoitory of machine learning databae, [Online]. Available: mlearn/mlrepoitory. html. [16] S. Draghici, The contraint baed decompoition (CBD) training architecture, Neural Network, Vol. 14, No. 4 5, 2001, pp

A Spam Message Filtering Method: focus on run time

A Spam Message Filtering Method: focus on run time , pp.29-33 http://dx.doi.org/10.14257/atl.2014.76.08 A Spam Meage Filtering Method: focu on run time Sin-Eon Kim 1, Jung-Tae Jo 2, Sang-Hyun Choi 3 1 Department of Information Security Management 2 Department

More information

Partial optimal labeling search for a NP-hard subclass of (max,+) problems

Partial optimal labeling search for a NP-hard subclass of (max,+) problems Partial optimal labeling earch for a NP-hard ubcla of (max,+) problem Ivan Kovtun International Reearch and Training Center of Information Technologie and Sytem, Kiev, Uraine, ovtun@image.iev.ua Dreden

More information

Mixed Method of Model Reduction for Uncertain Systems

Mixed Method of Model Reduction for Uncertain Systems SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol 4 No June Mixed Method of Model Reduction for Uncertain Sytem N Selvaganean Abtract: A mixed method for reducing a higher order uncertain ytem to a table reduced

More information

Support Vector Machine Based Electricity Price Forecasting For Electricity Markets utilising Projected Assessment of System Adequacy Data.

Support Vector Machine Based Electricity Price Forecasting For Electricity Markets utilising Projected Assessment of System Adequacy Data. The Sixth International Power Engineering Conference (IPEC23, 27-29 November 23, Singapore Support Vector Machine Baed Electricity Price Forecating For Electricity Maret utiliing Projected Aement of Sytem

More information

Bi-Objective Optimization for the Clinical Trial Supply Chain Management

Bi-Objective Optimization for the Clinical Trial Supply Chain Management Ian David Lockhart Bogle and Michael Fairweather (Editor), Proceeding of the 22nd European Sympoium on Computer Aided Proce Engineering, 17-20 June 2012, London. 2012 Elevier B.V. All right reerved. Bi-Objective

More information

A technical guide to 2014 key stage 2 to key stage 4 value added measures

A technical guide to 2014 key stage 2 to key stage 4 value added measures A technical guide to 2014 key tage 2 to key tage 4 value added meaure CONTENTS Introduction: PAGE NO. What i value added? 2 Change to value added methodology in 2014 4 Interpretation: Interpreting chool

More information

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS Chritopher V. Kopek Department of Computer Science Wake Foret Univerity Winton-Salem, NC, 2709 Email: kopekcv@gmail.com

More information

Assessing the Discriminatory Power of Credit Scores

Assessing the Discriminatory Power of Credit Scores Aeing the Dicriminatory Power of Credit Score Holger Kraft 1, Gerald Kroiandt 1, Marlene Müller 1,2 1 Fraunhofer Intitut für Techno- und Wirtchaftmathematik (ITWM) Gottlieb-Daimler-Str. 49, 67663 Kaierlautern,

More information

TRADING rules are widely used in financial market as

TRADING rules are widely used in financial market as Complex Stock Trading Strategy Baed on Particle Swarm Optimization Fei Wang, Philip L.H. Yu and David W. Cheung Abtract Trading rule have been utilized in the tock market to make profit for more than a

More information

Performance of a Browser-Based JavaScript Bandwidth Test

Performance of a Browser-Based JavaScript Bandwidth Test Performance of a Brower-Baed JavaScript Bandwidth Tet David A. Cohen II May 7, 2013 CP SC 491/H495 Abtract An exiting brower-baed bandwidth tet written in JavaScript wa modified for the purpoe of further

More information

Simulation of Sensorless Speed Control of Induction Motor Using APFO Technique

Simulation of Sensorless Speed Control of Induction Motor Using APFO Technique International Journal of Computer and Electrical Engineering, Vol. 4, No. 4, Augut 2012 Simulation of Senorle Speed Control of Induction Motor Uing APFO Technique T. Raghu, J. Sriniva Rao, and S. Chandra

More information

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS G. Chapman J. Cleee E. Idle ABSTRACT Content matching i a neceary component of any ignature-baed network Intruion Detection

More information

Scheduling of Jobs and Maintenance Activities on Parallel Machines

Scheduling of Jobs and Maintenance Activities on Parallel Machines Scheduling of Job and Maintenance Activitie on Parallel Machine Chung-Yee Lee* Department of Indutrial Engineering Texa A&M Univerity College Station, TX 77843-3131 cylee@ac.tamu.edu Zhi-Long Chen** Department

More information

Morningstar Fixed Income Style Box TM Methodology

Morningstar Fixed Income Style Box TM Methodology Morningtar Fixed Income Style Box TM Methodology Morningtar Methodology Paper Augut 3, 00 00 Morningtar, Inc. All right reerved. The information in thi document i the property of Morningtar, Inc. Reproduction

More information

1) Assume that the sample is an SRS. The problem state that the subjects were randomly selected.

1) Assume that the sample is an SRS. The problem state that the subjects were randomly selected. 12.1 Homework for t Hypothei Tet 1) Below are the etimate of the daily intake of calcium in milligram for 38 randomly elected women between the age of 18 and 24 year who agreed to participate in a tudy

More information

Optical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng

Optical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng Optical Illuion Sara Bolouki, Roger Groe, Honglak Lee, Andrew Ng. Introduction The goal of thi proect i to explain ome of the illuory phenomena uing pare coding and whitening model. Intead of the pare

More information

REDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND TAGUCHI METHODOLOGY. Abstract. 1.

REDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND TAGUCHI METHODOLOGY. Abstract. 1. International Journal of Advanced Technology & Engineering Reearch (IJATER) REDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND Abtract TAGUCHI METHODOLOGY Mr.

More information

CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY

CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Annale Univeritati Apuleni Serie Oeconomica, 2(2), 200 CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Sidonia Otilia Cernea Mihaela Jaradat 2 Mohammad

More information

A note on profit maximization and monotonicity for inbound call centers

A note on profit maximization and monotonicity for inbound call centers A note on profit maximization and monotonicity for inbound call center Ger Koole & Aue Pot Department of Mathematic, Vrije Univeriteit Amterdam, The Netherland 23rd December 2005 Abtract We conider an

More information

A Resolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networks

A Resolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networks A Reolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networ Joé Craveirinha a,c, Rita Girão-Silva a,c, João Clímaco b,c, Lúcia Martin a,c a b c DEEC-FCTUC FEUC INESC-Coimbra International

More information

Queueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems,

Queueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems, MANAGEMENT SCIENCE Vol. 54, No. 3, March 28, pp. 565 572 in 25-199 ein 1526-551 8 543 565 inform doi 1.1287/mnc.17.82 28 INFORMS Scheduling Arrival to Queue: A Single-Server Model with No-Show INFORMS

More information

TIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME

TIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME TIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME RADMILA KOCURKOVÁ Sileian Univerity in Opava School of Buine Adminitration in Karviná Department of Mathematical Method in Economic Czech Republic

More information

INFORMATION Technology (IT) infrastructure management

INFORMATION Technology (IT) infrastructure management IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 2, NO. 1, MAY 214 1 Buine-Driven Long-term Capacity Planning for SaaS Application David Candeia, Ricardo Araújo Santo and Raquel Lope Abtract Capacity Planning

More information

INTERACTIVE TOOL FOR ANALYSIS OF TIME-DELAY SYSTEMS WITH DEAD-TIME COMPENSATORS

INTERACTIVE TOOL FOR ANALYSIS OF TIME-DELAY SYSTEMS WITH DEAD-TIME COMPENSATORS INTERACTIVE TOOL FOR ANALYSIS OF TIMEDELAY SYSTEMS WITH DEADTIME COMPENSATORS Joé Lui Guzmán, Pedro García, Tore Hägglund, Sebatián Dormido, Pedro Alberto, Manuel Berenguel Dep. de Lenguaje y Computación,

More information

Risk Management for a Global Supply Chain Planning under Uncertainty: Models and Algorithms

Risk Management for a Global Supply Chain Planning under Uncertainty: Models and Algorithms Rik Management for a Global Supply Chain Planning under Uncertainty: Model and Algorithm Fengqi You 1, John M. Waick 2, Ignacio E. Gromann 1* 1 Dept. of Chemical Engineering, Carnegie Mellon Univerity,

More information

Acceleration-Displacement Crash Pulse Optimisation A New Methodology to Optimise Vehicle Response for Multiple Impact Speeds

Acceleration-Displacement Crash Pulse Optimisation A New Methodology to Optimise Vehicle Response for Multiple Impact Speeds Acceleration-Diplacement Crah Pule Optimiation A New Methodology to Optimie Vehicle Repone for Multiple Impact Speed D. Gildfind 1 and D. Ree 2 1 RMIT Univerity, Department of Aeropace Engineering 2 Holden

More information

Two Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL

Two Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL Excerpt from the Proceeding of the COMSO Conference 0 India Two Dimenional FEM Simulation of Ultraonic Wave Propagation in Iotropic Solid Media uing COMSO Bikah Ghoe *, Krihnan Balaubramaniam *, C V Krihnamurthy

More information

SCM- integration: organiational, managerial and technological iue M. Caridi 1 and A. Sianei 2 Dipartimento di Economia e Produzione, Politecnico di Milano, Italy E-mail: maria.caridi@polimi.it Itituto

More information

Redesigning Ratings: Assessing the Discriminatory Power of Credit Scores under Censoring

Redesigning Ratings: Assessing the Discriminatory Power of Credit Scores under Censoring Redeigning Rating: Aeing the Dicriminatory Power of Credit Score under Cenoring Holger Kraft, Gerald Kroiandt, Marlene Müller Fraunhofer Intitut für Techno- und Wirtchaftmathematik (ITWM) Thi verion: June

More information

Unit 11 Using Linear Regression to Describe Relationships

Unit 11 Using Linear Regression to Describe Relationships Unit 11 Uing Linear Regreion to Decribe Relationhip Objective: To obtain and interpret the lope and intercept of the leat quare line for predicting a quantitative repone variable from a quantitative explanatory

More information

MANAGING DATA REPLICATION IN MOBILE AD- HOC NETWORK DATABASES (Invited Paper) *

MANAGING DATA REPLICATION IN MOBILE AD- HOC NETWORK DATABASES (Invited Paper) * MANAGING DATA REPLICATION IN MOBILE AD- HOC NETWORK DATABASES (Invited Paper) * Praanna Padmanabhan School of Computer Science The Univerity of Oklahoma Norman OK, USA praannap@yahoo-inc.com Dr. Le Gruenwald

More information

Towards Control-Relevant Forecasting in Supply Chain Management

Towards Control-Relevant Forecasting in Supply Chain Management 25 American Control Conference June 8-1, 25. Portland, OR, USA WeA7.1 Toward Control-Relevant Forecating in Supply Chain Management Jay D. Schwartz, Daniel E. Rivera 1, and Karl G. Kempf Control Sytem

More information

Trusted Document Signing based on use of biometric (Face) keys

Trusted Document Signing based on use of biometric (Face) keys Truted Document Signing baed on ue of biometric (Face) Ahmed B. Elmadani Department of Computer Science Faculty of Science Sebha Univerity Sebha Libya www.ebhau.edu.ly elmadan@yahoo.com ABSTRACT An online

More information

International Journal of Heat and Mass Transfer

International Journal of Heat and Mass Transfer International Journal of Heat and Ma Tranfer 5 (9) 14 144 Content lit available at ScienceDirect International Journal of Heat and Ma Tranfer journal homepage: www.elevier.com/locate/ijhmt Technical Note

More information

Progress 8 measure in 2016, 2017, and 2018. Guide for maintained secondary schools, academies and free schools

Progress 8 measure in 2016, 2017, and 2018. Guide for maintained secondary schools, academies and free schools Progre 8 meaure in 2016, 2017, and 2018 Guide for maintained econdary chool, academie and free chool July 2016 Content Table of figure 4 Summary 5 A ummary of Attainment 8 and Progre 8 5 Expiry or review

More information

T-test for dependent Samples. Difference Scores. The t Test for Dependent Samples. The t Test for Dependent Samples. s D

T-test for dependent Samples. Difference Scores. The t Test for Dependent Samples. The t Test for Dependent Samples. s D The t Tet for ependent Sample T-tet for dependent Sample (ak.a., Paired ample t-tet, Correlated Group eign, Within- Subject eign, Repeated Meaure,.. Repeated-Meaure eign When you have two et of core from

More information

A Note on Profit Maximization and Monotonicity for Inbound Call Centers

A Note on Profit Maximization and Monotonicity for Inbound Call Centers OPERATIONS RESEARCH Vol. 59, No. 5, September October 2011, pp. 1304 1308 in 0030-364X ein 1526-5463 11 5905 1304 http://dx.doi.org/10.1287/opre.1110.0990 2011 INFORMS TECHNICAL NOTE INFORMS hold copyright

More information

SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE

SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE RAVI KUMAR G 1, C.MUTHUSAMY 2 & A.VINAYA BABU 3 1 HP Bangalore, Reearch Scholar JNTUH, Hyderabad, India, 2 Yahoo, Bangalore,

More information

Proceedings of Power Tech 2007, July 1-5, Lausanne

Proceedings of Power Tech 2007, July 1-5, Lausanne Second Order Stochatic Dominance Portfolio Optimization for an Electric Energy Company M.-P. Cheong, Student Member, IEEE, G. B. Sheble, Fellow, IEEE, D. Berleant, Senior Member, IEEE and C.-C. Teoh, Student

More information

Growing Self-Organizing Maps for Surface Reconstruction from Unstructured Point Clouds

Growing Self-Organizing Maps for Surface Reconstruction from Unstructured Point Clouds Growing Self-Organizing Map for Surface Recontruction from Untructured Point Cloud Renata L. M. E. do Rêgo, Aluizio F. R. Araújo, and Fernando B.de Lima Neto Abtract Thi work introduce a new method for

More information

CHAPTER 5 BROADBAND CLASS-E AMPLIFIER

CHAPTER 5 BROADBAND CLASS-E AMPLIFIER CHAPTER 5 BROADBAND CLASS-E AMPLIFIER 5.0 Introduction Cla-E amplifier wa firt preented by Sokal in 1975. The application of cla- E amplifier were limited to the VHF band. At thi range of frequency, cla-e

More information

Abstract parsing: static analysis of dynamically generated string output using LR-parsing technology

Abstract parsing: static analysis of dynamically generated string output using LR-parsing technology Abtract paring: tatic analyi of dynamically generated tring output uing LR-paring technology Kyung-Goo Doh 1, Hyunha Kim 1, David A. Schmidt 2 1 Hanyang Univerity, Anan, South Korea 2 Kana State Univerity,

More information

Profitability of Loyalty Programs in the Presence of Uncertainty in Customers Valuations

Profitability of Loyalty Programs in the Presence of Uncertainty in Customers Valuations Proceeding of the 0 Indutrial Engineering Reearch Conference T. Doolen and E. Van Aken, ed. Profitability of Loyalty Program in the Preence of Uncertainty in Cutomer Valuation Amir Gandomi and Saeed Zolfaghari

More information

Project Management Basics

Project Management Basics Project Management Baic A Guide to undertanding the baic component of effective project management and the key to ucce 1 Content 1.0 Who hould read thi Guide... 3 1.1 Overview... 3 1.2 Project Management

More information

EVALUATING SERVICE QUALITY OF MOBILE APPLICATION STORES: A COMPARISON OF THREE TELECOMMUNICATION COMPANIES IN TAIWAN

EVALUATING SERVICE QUALITY OF MOBILE APPLICATION STORES: A COMPARISON OF THREE TELECOMMUNICATION COMPANIES IN TAIWAN International Journal of Innovative Computing, Information and Control ICIC International c 2012 ISSN 1349-4198 Volume 8, Number 4, April 2012 pp. 2563 2581 EVALUATING SERVICE QUALITY OF MOBILE APPLICATION

More information

Empirical correlations of overconsolidation ratio, coefficient of earth pressure at rest and undrained strength

Empirical correlations of overconsolidation ratio, coefficient of earth pressure at rest and undrained strength Second Conference of Jnior Reearcher in Civil Engineering 88 Empirical correlation of overconolidation ratio, coefficient of earth prere at ret and ndrained trength Vendel Józa BME Department of Geotechnic,

More information

MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell

MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell MATLAB/Simulink Baed Modelling of Solar Photovoltaic Cell Tarak Salmi *, Mounir Bouzguenda **, Adel Gatli **, Ahmed Mamoudi * *Reearch Unit on Renewable Energie and Electric Vehicle, National Engineering

More information

MBA 570x Homework 1 Due 9/24/2014 Solution

MBA 570x Homework 1 Due 9/24/2014 Solution MA 570x Homework 1 Due 9/24/2014 olution Individual work: 1. Quetion related to Chapter 11, T Why do you think i a fund of fund market for hedge fund, but not for mutual fund? Anwer: Invetor can inexpenively

More information

Pekka Helkiö, 58490K Antti Seppälä, 63212W Ossi Syd, 63513T

Pekka Helkiö, 58490K Antti Seppälä, 63212W Ossi Syd, 63513T Pekka Helkiö, 58490K Antti Seppälä, 63212W Oi Syd, 63513T Table of Content 1. Abtract...1 2. Introduction...2 2.1 Background... 2 2.2 Objective and Reearch Problem... 2 2.3 Methodology... 2 2.4 Scoping

More information

Design of Compound Hyperchaotic System with Application in Secure Data Transmission Systems

Design of Compound Hyperchaotic System with Application in Secure Data Transmission Systems Deign of Compound Hyperchaotic Sytem with Application in Secure Data Tranmiion Sytem D. Chantov Key Word. Lyapunov exponent; hyperchaotic ytem; chaotic ynchronization; chaotic witching. Abtract. In thi

More information

Software Engineering Management: strategic choices in a new decade

Software Engineering Management: strategic choices in a new decade Software Engineering : trategic choice in a new decade Barbara Farbey & Anthony Finkeltein Univerity College London, Department of Computer Science, Gower St. London WC1E 6BT, UK {b.farbey a.finkeltein}@ucl.ac.uk

More information

Group Mutual Exclusion Based on Priorities

Group Mutual Exclusion Based on Priorities Group Mutual Excluion Baed on Prioritie Karina M. Cenci Laboratorio de Invetigación en Sitema Ditribuido Univeridad Nacional del Sur Bahía Blanca, Argentina kmc@c.un.edu.ar and Jorge R. Ardenghi Laboratorio

More information

Mobile Network Configuration for Large-scale Multimedia Delivery on a Single WLAN

Mobile Network Configuration for Large-scale Multimedia Delivery on a Single WLAN Mobile Network Configuration for Large-cale Multimedia Delivery on a Single WLAN Huigwang Je, Dongwoo Kwon, Hyeonwoo Kim, and Hongtaek Ju Dept. of Computer Engineering Keimyung Univerity Daegu, Republic

More information

AN OVERVIEW ON CLUSTERING METHODS

AN OVERVIEW ON CLUSTERING METHODS IOSR Journal Engineering AN OVERVIEW ON CLUSTERING METHODS T. Soni Madhulatha Aociate Preor, Alluri Intitute Management Science, Warangal. ABSTRACT Clutering i a common technique for tatitical data analyi,

More information

Assigning Tasks for Efficiency in Hadoop

Assigning Tasks for Efficiency in Hadoop Aigning Tak for Efficiency in Hadoop [Extended Abtract] Michael J. Ficher Computer Science Yale Univerity P.O. Box 208285 New Haven, CT, USA michael.ficher@yale.edu Xueyuan Su Computer Science Yale Univerity

More information

Research Article An (s, S) Production Inventory Controlled Self-Service Queuing System

Research Article An (s, S) Production Inventory Controlled Self-Service Queuing System Probability and Statitic Volume 5, Article ID 558, 8 page http://dxdoiorg/55/5/558 Reearch Article An (, S) Production Inventory Controlled Self-Service Queuing Sytem Anoop N Nair and M J Jacob Department

More information

Testing Documentation for CCIH Database Management System By: John Reeves, Derek King, and Robert Watts

Testing Documentation for CCIH Database Management System By: John Reeves, Derek King, and Robert Watts Teting Documentation for CCIH Databae Management Sytem By: John Reeve, Derek King, and Robert Watt The teting proce for our project i divided into three part of Unit teting, one part of Integration/Function

More information

Control of Wireless Networks with Flow Level Dynamics under Constant Time Scheduling

Control of Wireless Networks with Flow Level Dynamics under Constant Time Scheduling Control of Wirele Network with Flow Level Dynamic under Contant Time Scheduling Long Le and Ravi R. Mazumdar Department of Electrical and Computer Engineering Univerity of Waterloo,Waterloo, ON, Canada

More information

Benchmarking Bottom-Up and Top-Down Strategies for SPARQL-to-SQL Query Translation

Benchmarking Bottom-Up and Top-Down Strategies for SPARQL-to-SQL Query Translation Benchmarking Bottom-Up and Top-Down Strategie for SPARQL-to-SQL Query Tranlation Kahlev a, Chebotko b,c, John Abraham b, Pearl Brazier b, and Shiyong Lu a a Department of Computer Science, Wayne State

More information

Review of Multiple Regression Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015

Review of Multiple Regression Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015 Review of Multiple Regreion Richard William, Univerity of Notre Dame, http://www3.nd.edu/~rwilliam/ Lat revied January 13, 015 Aumption about prior nowledge. Thi handout attempt to ummarize and yntheize

More information

QUANTIFYING THE BULLWHIP EFFECT IN THE SUPPLY CHAIN OF SMALL-SIZED COMPANIES

QUANTIFYING THE BULLWHIP EFFECT IN THE SUPPLY CHAIN OF SMALL-SIZED COMPANIES Sixth LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCEI 2008) Partnering to Succe: Engineering, Education, Reearch and Development June 4 June 6 2008,

More information

BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE

BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE Progre In Electromagnetic Reearch Letter, Vol. 3, 51, 08 BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE S. H. Zainud-Deen Faculty of Electronic Engineering Menoufia

More information

Delft. Matlab and Simulink for Modeling and Control. Robert Babuška and Stefano Stramigioli. November 1999

Delft. Matlab and Simulink for Modeling and Control. Robert Babuška and Stefano Stramigioli. November 1999 Matlab and Simulink for Modeling and Control Robert Babuška and Stefano Stramigioli November 999 Delft Delft Univerity of Technology Control Laboratory Faculty of Information Technology and Sytem Delft

More information

Control Theory based Approach for the Improvement of Integrated Business Process Interoperability

Control Theory based Approach for the Improvement of Integrated Business Process Interoperability www.ijcsi.org 201 Control Theory baed Approach for the Improvement of Integrated Buine Proce Interoperability Abderrahim Taoudi 1, Bouchaib Bounabat 2 and Badr Elmir 3 1 Al-Qualadi Reearch & Development

More information

RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds

RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds !111! 111!ttthhh IIIEEEEEEEEE///AAACCCMMM IIInnnttteeerrrnnnaaatttiiiooonnnaaalll SSSyyymmmpppoooiiiuuummm ooonnn CCCllluuuttteeerrr,,, CCClllooouuuddd aaannnddd GGGrrriiiddd CCCooommmpppuuutttiiinnnggg

More information

Principal version published in the University of Innsbruck Bulletin of 8 April 2009, Issue 55, No 233

Principal version published in the University of Innsbruck Bulletin of 8 April 2009, Issue 55, No 233 Note: The following curriculum i a conolidated verion. It i legally non-binding and for informational purpoe only. The legally binding verion are found in the Univerity of Innbruck Bulletin (in German).

More information

Final Award. (exit route if applicable for Postgraduate Taught Programmes) N/A JACS Code. Full-time. Length of Programme. Queen s University Belfast

Final Award. (exit route if applicable for Postgraduate Taught Programmes) N/A JACS Code. Full-time. Length of Programme. Queen s University Belfast Date of Reviion Date of Previou Reviion Programme Specification (2014-15) A programme pecification i required for any programme on which a tudent may be regitered. All programme of the Univerity are ubject

More information

CASE STUDY ALLOCATE SOFTWARE

CASE STUDY ALLOCATE SOFTWARE CASE STUDY ALLOCATE SOFTWARE allocate caetud y TABLE OF CONTENTS #1 ABOUT THE CLIENT #2 OUR ROLE #3 EFFECTS OF OUR COOPERATION #4 BUSINESS PROBLEM THAT WE SOLVED #5 CHALLENGES #6 WORKING IN SCRUM #7 WHAT

More information

Report 4668-1b 30.10.2010. Measurement report. Sylomer - field test

Report 4668-1b 30.10.2010. Measurement report. Sylomer - field test Report 4668-1b Meaurement report Sylomer - field tet Report 4668-1b 2(16) Contet 1 Introduction... 3 1.1 Cutomer... 3 1.2 The ite and purpoe of the meaurement... 3 2 Meaurement... 6 2.1 Attenuation of

More information

Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing 1 Auction Mechanim Toward Efficient Reource Sharing for Cloudlet in Mobile Cloud Computing A-Long Jin, Wei Song, Ping Wang, Duit Niyato, and Peijian Ju Abtract Mobile cloud computing offer an appealing

More information

Turbulent Mixing and Chemical Reaction in Stirred Tanks

Turbulent Mixing and Chemical Reaction in Stirred Tanks Turbulent Mixing and Chemical Reaction in Stirred Tank André Bakker Julian B. Faano Blend time and chemical product ditribution in turbulent agitated veel can be predicted with the aid of Computational

More information

Brand Equity Net Promoter Scores Versus Mean Scores. Which Presents a Clearer Picture For Action? A Non-Elite Branded University Example.

Brand Equity Net Promoter Scores Versus Mean Scores. Which Presents a Clearer Picture For Action? A Non-Elite Branded University Example. Brand Equity Net Promoter Score Veru Mean Score. Which Preent a Clearer Picture For Action? A Non-Elite Branded Univerity Example Ann Miti, Swinburne Univerity of Technology Patrick Foley, Victoria Univerity

More information

Exploiting A Support-based Upper Bound of Pearson s Correlation Coefficient for Efficiently Identifying Strongly Correlated Pairs

Exploiting A Support-based Upper Bound of Pearson s Correlation Coefficient for Efficiently Identifying Strongly Correlated Pairs Exploiting A Support-baed Upper Bound of Pearon Correlation Coefficient for Efficiently Identifying Strongly Correlated Pair Hui Xiong Computer Science Univerity of Minneota huix@c.umn.edu Shahi Shekhar

More information

Return on Investment and Effort Expenditure in the Software Development Environment

Return on Investment and Effort Expenditure in the Software Development Environment International Journal of Applied Information ytem (IJAI) IN : 2249-0868 Return on Invetment and Effort Expenditure in the oftware Development Environment Dineh Kumar aini Faculty of Computing and IT, ohar

More information

The Cash Flow Statement: Problems with the Current Rules

The Cash Flow Statement: Problems with the Current Rules A C C O U N T I N G & A U D I T I N G accounting The Cah Flow Statement: Problem with the Current Rule By Neii S. Wei and Jame G.S. Yang In recent year, the tatement of cah flow ha received increaing attention

More information

Queueing Models for Multiclass Call Centers with Real-Time Anticipated Delays

Queueing Models for Multiclass Call Centers with Real-Time Anticipated Delays Queueing Model for Multicla Call Center with Real-Time Anticipated Delay Oualid Jouini Yve Dallery Zeynep Akşin Ecole Centrale Pari Koç Univerity Laboratoire Génie Indutriel College of Adminitrative Science

More information

Algorithms for Advance Bandwidth Reservation in Media Production Networks

Algorithms for Advance Bandwidth Reservation in Media Production Networks Algorithm for Advance Bandwidth Reervation in Media Production Network Maryam Barhan 1, Hendrik Moen 1, Jeroen Famaey 2, Filip De Turck 1 1 Department of Information Technology, Ghent Univerity imind Gaton

More information

CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON MAPREDUCE FRAMEWORK

CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON MAPREDUCE FRAMEWORK CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON MAPREDUCE FRAMEWORK Sheela Gole 1 and Bharat Tidke 2 1 Department of Computer Engineering, Flora Intitute of Technology, Pune,

More information

Gabriel E. Arrobo and Richard D. Gitlin, NAI Charter Fellow

Gabriel E. Arrobo and Richard D. Gitlin, NAI Charter Fellow Technology and Innovation, Vol. 15, pp. 227 236, 2013 1949-8241/13 $90.00 +.00 Printed in the USA. All right reerved. DOI: http://dx.doi.org/10.3727/194982413x13790020921825 Copyright ã 2013 Cognizant

More information

Apigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management

Apigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management Apigee Edge: Apigee Cloud v. Private Cloud Evaluating deployment model for API management Table of Content Introduction 1 Time to ucce 2 Total cot of ownerhip 2 Performance 3 Security 4 Data privacy 4

More information

Utility-Based Flow Control for Sequential Imagery over Wireless Networks

Utility-Based Flow Control for Sequential Imagery over Wireless Networks Utility-Baed Flow Control for Sequential Imagery over Wirele Networ Tomer Kihoni, Sara Callaway, and Mar Byer Abtract Wirele enor networ provide a unique et of characteritic that mae them uitable for building

More information

Performance of Multiple TFRC in Heterogeneous Wireless Networks

Performance of Multiple TFRC in Heterogeneous Wireless Networks Performance of Multiple TFRC in Heterogeneou Wirele Network 1 Hyeon-Jin Jeong, 2 Seong-Sik Choi 1, Firt Author Computer Engineering Department, Incheon National Univerity, oaihjj@incheon.ac.kr *2,Correponding

More information

Linear energy-preserving integrators for Poisson systems

Linear energy-preserving integrators for Poisson systems BIT manucript No. (will be inerted by the editor Linear energy-preerving integrator for Poion ytem David Cohen Ernt Hairer Received: date / Accepted: date Abtract For Hamiltonian ytem with non-canonical

More information

Combining Statistics and Semantics via Ensemble Model for Document Clustering

Combining Statistics and Semantics via Ensemble Model for Document Clustering ombining tatitic and emantic via Enemble Model or Document lutering amah Jamal Fodeh Michigan tate Univerity Eat Laning, MI, 48824 odeham@mu.edu William F Punch Michigan tate Univerity Eat Laning, MI,

More information

Multi-Objective Optimization for Sponsored Search

Multi-Objective Optimization for Sponsored Search Multi-Objective Optimization for Sponored Search Yilei Wang 1,*, Bingzheng Wei 2, Jun Yan 2, Zheng Chen 2, Qiao Du 2,3 1 Yuanpei College Peking Univerity Beijing, China, 100871 (+86)15120078719 wangyileipku@gmail.com

More information

Availability of WDM Multi Ring Networks

Availability of WDM Multi Ring Networks Paper Availability of WDM Multi Ring Network Ivan Rado and Katarina Rado H d.o.o. Motar, Motar, Bonia and Herzegovina Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Univerity

More information

MECH 2110 - Statics & Dynamics

MECH 2110 - Statics & Dynamics Chapter D Problem 3 Solution 1/7/8 1:8 PM MECH 11 - Static & Dynamic Chapter D Problem 3 Solution Page 7, Engineering Mechanic - Dynamic, 4th Edition, Meriam and Kraige Given: Particle moving along a traight

More information

Name: SID: Instructions

Name: SID: Instructions CS168 Fall 2014 Homework 1 Aigned: Wedneday, 10 September 2014 Due: Monday, 22 September 2014 Name: SID: Dicuion Section (Day/Time): Intruction - Submit thi homework uing Pandagrader/GradeScope(http://www.gradecope.com/

More information

Computing Location from Ambient FM Radio Signals

Computing Location from Ambient FM Radio Signals Computing Location from Ambient FM Radio Signal Adel Youef Department of Computer Science Univerity of Maryland A.V. William Building College Park, MD 20742 adel@c.umd.edu John Krumm, Ed Miller, Gerry

More information

12.4 Problems. Excerpt from "Introduction to Geometry" 2014 AoPS Inc. Copyrighted Material CHAPTER 12. CIRCLES AND ANGLES

12.4 Problems. Excerpt from Introduction to Geometry 2014 AoPS Inc.  Copyrighted Material CHAPTER 12. CIRCLES AND ANGLES HTER 1. IRLES N NGLES Excerpt from "Introduction to Geometry" 014 os Inc. onider the circle with diameter O. all thi circle. Why mut hit O in at leat two di erent point? (b) Why i it impoible for to hit

More information

Mathematical Modeling of Molten Slag Granulation Using a Spinning Disk Atomizer (SDA)

Mathematical Modeling of Molten Slag Granulation Using a Spinning Disk Atomizer (SDA) Mathematical Modeling of Molten Slag Granulation Uing a Spinning Dik Atomizer (SDA) Hadi Purwanto and Tomohiro Akiyama Center for Advanced Reearch of Energy Converion Material, Hokkaido Univerity Kita

More information

A new application of fuzzy set theory to the Black Scholes option pricing model *

A new application of fuzzy set theory to the Black Scholes option pricing model * Expert Sytem with Application 29 2005 330 342 www.elevier.com/locate/ewa A new application of fuzzy et theory to the Blac Schole option pricing model * Cheng-Few Lee a, Gwo-Hhiung Tzeng b, Shin-Yun Wang

More information

Tap Into Smartphone Demand: Mobile-izing Enterprise Websites by Using Flexible, Open Source Platforms

Tap Into Smartphone Demand: Mobile-izing Enterprise Websites by Using Flexible, Open Source Platforms Tap Into Smartphone Demand: Mobile-izing Enterprie Webite by Uing Flexible, Open Source Platform acquia.com 888.922.7842 1.781.238.8600 25 Corporate Drive, Burlington, MA 01803 Tap Into Smartphone Demand:

More information

Distributed Monitoring and Aggregation in Wireless Sensor Networks

Distributed Monitoring and Aggregation in Wireless Sensor Networks Ditributed Monitoring and Aggregation in Wirele Senor Network Changlei Liu and Guohong Cao Department of Computer Science & Engineering The Pennylvania State Univerity E-mail: {chaliu, gcao}@ce.pu.edu

More information

How to Maximize User Satisfaction Degree in Multi-service IP Networks

How to Maximize User Satisfaction Degree in Multi-service IP Networks How to Maximize Uer Satifaction Degree in Multi-ervice IP Network Huy Anh Nguyen, Tam Van Nguyen and Deokai Choi Department of Electronic and Computer Engineering Chonnam National Univerity Gwangu, KOREA

More information

A Review On Software Testing In SDlC And Testing Tools

A Review On Software Testing In SDlC And Testing Tools www.ijec.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Iue -9 September, 2014 Page No. 8188-8197 A Review On Software Teting In SDlC And Teting Tool T.Amruthavalli*,

More information

Linear Momentum and Collisions

Linear Momentum and Collisions Chapter 7 Linear Momentum and Colliion 7.1 The Important Stuff 7.1.1 Linear Momentum The linear momentum of a particle with ma m moving with velocity v i defined a p = mv (7.1) Linear momentum i a vector.

More information

MODELLING HIGH TEMPERATURE FLOW STRESS CURVES OF TITANIUM ALLOYS

MODELLING HIGH TEMPERATURE FLOW STRESS CURVES OF TITANIUM ALLOYS MODELLING HIGH TEMPERATURE FLOW STRESS CURVES OF TITANIUM ALLOYS Z. Guo, N. Saunder, J.P. Schillé, A.P. Miodownik Sente Software Ltd, Surrey Technology Centre, Guildford, GU2 7YG, U.K. Keyword: Titanium

More information

In this paper, we investigate toll setting as a policy tool to regulate the use of roads for dangerous goods

In this paper, we investigate toll setting as a policy tool to regulate the use of roads for dangerous goods Vol. 43, No. 2, May 2009, pp. 228 243 in 0041-1655 ein 1526-5447 09 4302 0228 inform doi 10.1287/trc.1080.0236 2009 INFORMS Toll Policie for Mitigating Hazardou Material Tranport Rik Patrice Marcotte,

More information

A Novel Web-Based Student Academic Records Information System

A Novel Web-Based Student Academic Records Information System A Novel Web-Baed Student Record Information Sytem Nmaju Obai, E. O. Nwachukwu, and C. Ugwu Department of Computer Science, Univerity of Port Harcourt, Port Harcourt, River State, Nigeria nmajuobai@yahoo.com,

More information