Lei Liu, Hua Yang Business School, Hunan University, Changsha, Hunan, P.R. China, Abstract

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1 , pp Research on the Enterprse Performance Management Informaton System Development and Robustness Optmzaton based on Data Regresson Analyss and Mathematcal Optmzaton Theory Le Lu, Hua Yang Busness School, Hunan Unversty, Changsha, Hunan, P.R. Chna, Abstract Performance management s core part of enterprse human resources management, for any organzaton or enterprse a good, the secret to ts success les n the effectve performance management. Through the enterprse performance management, t can motvate employees, mprove employee satsfacton, wll help the staff's personal goals combned wth enterprse strategy, realze the goal of the enterprse and the balance of personal development, promote enterprse realze sustanable development. In ths paper, we conduct research on enterprse performance management nformaton system development and the robustness optmzaton based on data regresson analyss and mathematcal optmzaton theory. We frstly revew the modern regresson analyss approaches for large-scale data wth the ntroducton of our proposed revsed regresson model for enterprses. Later, we summarze the state-of-the-art mathematcal optmzaton models to select the most feasble one to serve as the tool for our system. Then, we analyze the performance of the current exstng performance management nformaton system and adopt the pror knowledge to optmze the system. The expermental result proves the feasblty and effectveness of our system. The performance evaluaton accuracy s enhanced from 86.7% to 93.5% and the system robustness s enhanced as well. Keywords: Management Informaton System; System Securty; Enterprse Performance; Data Regresson Analyss; Mathematcal Optmzaton; Robustness Optmzaton. 1. Introducton Informaton management system s to provde the nformaton, support, busness operaton, management and decson functon of the system, t holds a lot of useful nformaton, how to make use of ths nformaton, query functon s an mportant and ndspensable part. However, tradtonal nformaton management system such as query mantenance dffcultes, especally to ncrease some nformaton wth a greater dfference between exstng nformaton types, nformaton system must be changed by orgnal storage structure or new storage structure, ths wll ncrease mantenance workload of software code, so that the system cannot meet the needs of the enterprses and nsttutons n a tmely manner. Management nformaton system constructon n order to better for the management of the full and the accurate nformaton, nformaton s recorded n the socal lfe of all knds of nformaton symbols and basc data, from the pont of the current socal management, nformaton s an mportant object of socal management and components management plays an mportant role n the modern socety. The constructon of nformaton management system s the nevtable demand of the nformaton management whch s one of the mportant socal publc management system development drectons. The current nformaton management system n the publc affars ISSN: IJSIA Copyrght c 2016 SERSC

2 management has made full applcaton and ts functon manly for followng several aspects. (1) Informaton management system wth functons of plan. Informaton management system n the process of management of the nformaton, can be n varous management levels accordng to the known nformaton of the detaled management plan, t make plans to meet actual requrements of management, mprove management effcency of management. (2) Informaton management system wth functons of auxlary decson-makng. Due to masterng a large amount of basc data nformaton management system, and can accordng to practcal we need to make a comprehensve utlzaton data, ncludng the data modelng, t s concluded that the optmal mathematcal model, so as to provde the correspondng bass for correct decson-makng. (3) Informaton management system wth functons of data processng. Because the applcaton of computer technology, nformaton management system has a certan advantage n the data processng, manly collected data can be calculated, transmsson, storage, processng and the output, ensure the accuracy of the data to greatest extent. (4) Informaton management system has forecast functon [1-2]. Informaton management system can not only analyze the known data and processng and also can use mathematcal calculaton wth known nformaton as the condtons, through mathematcal modelng and calculaton, analyss and forecast the future development trend. In the fgure one, we show the sample structure of MIS system [3-4]. Fgure 1. The Sample Structure of the Management Informaton System Structure Informaton management system s to store huge amounts of data nformaton, establsh a good data storage structure to manage these large amounts of data, wll affect the effcency of nformaton system and user satsfacton to the system. In tradtonal enterprse personnel management work, n the aspect of the development of people usually took the form of theory of endowment lne. Wth the progress of the socety, the establshment of modern enterprse human resources management, more and more companes tend to use based on performance of human resource development strategy. Subsequent, performance management has become the hot topc of human resource management [5-6]. Compensaton allocaton and performance evaluaton s performance management, human resource plannng and the bass of ncentve mechansm and the foothold. In the tradtonal performance apprasal, dfferent people were affected by same standards that are compared each other, but n modern performance, dfferent people from dfferent 378 Copyrght c 2016 SERSC

3 measure s compared wth themselves. Qualtatve analyss of knowledge sharng s gven motvatng ndvdual knowledge sharng behavor gudelnes as the mplementaton of ncentve s needed based on quanttatve assessment to ensure mplementaton of the enterprse knowledge management effect s of great sgnfcance, therefore whch has become one of the problems of knowledge management research. Suffcent lterature revews has combned the mentoned aspects. In [7], Tsa s group conducted research on the nfluence of the enterprse resource plannng systems' performance on earnngs management. For system evaluaton s dffcult to be the objectve, comprehensve and accurate problem, puts forward the management nformaton system of fuzzy comprehensve evaluaton theory, the establshment of a system, a complete evaluaton ndex system usng the herarchcal clusterng method to buld up the management nformaton system evaluaton of class herarchy usng fuzzy herarchcal clusterng method of the ndex weght, and set up a comprehensve evaluaton s applcable to the management nformaton system of multlevel fuzzy mathematcs model. In [8], Sunl conducted research on the nformaton management capablty nfluences frm performance. Transacton drven method s studed n the applcaton of management nformaton system analyss of specfc areas, construct a transacton tree, tree algorthm and dscusses the constructon affars. The transacton tree s helpful to determne management nformaton system n specfc doman model element and the correspondng relaton of element n the desgn space. In [9], Danel conducted research on the supply chan ntegraton and performance: the effects of long-term relatonshps, nformaton technology and sharng, and logstcs ntegraton. To the operaton of the data s an mportant content n management nformaton system, usng code automatc generaton technology can effectvely solve problem of code reuse, mprove the development qualty and effcency of the management nformaton system. More applcatons of the recent management nformaton system could be found n [10-13]. In ths paper, to enhance the performance and safety of tradton management nformaton system for the performance evaluaton, we conduct research on the enterprse performance management nformaton system development and robustness optmzaton based on the data regresson analyss and mathematcal optmzaton theory. The remndng of the paper s organzed as the follows. We frstly analyze the data regresson methods to serve as the pre-analyss of the later dscusson for the MIS system data optmzaton. Then, we ntroduce the mathematcal optmzaton theory nto the research and propose our new ones. Later, we brefly defne the envronment of developng MIS system and propose the detaled structure of our system wth the optmzaton. Fnally, we test the performance and conclude the work. 2. The Regresson Analyss for Large-scale Data 2.1. The Overvew of the Regresson Analyss Regresson analyss and correlaton analyss s the analyss of the two dfferent methods. Correlaton analyss has formed a set of theory wth regresson analyss, there s no complete theory can follow. Therefore the scence and technology workers cross applcaton n solvng practcal problems, to learn more and more complcated. Regresson analyss s pop analytc geometry, the curve functon. Scentfc and techncal workers n the practcal work often get two seres correspondng data wth each of the other, draw a scatter dagram, ntutvely n the mddle of the scatterplot draw the smooth curve. Prncpal component regresson analyss s a knd of n order to overcome the complex collnearty and usng regresson analyss method, n a lnear regresson model as the research object, uses the prncpal components estmate n parameter estmaton, at the certan devaton at the expense of based estmaton [14-15]. Copyrght c 2016 SERSC 379

4 In practce, especally n the process of chemcal producton, the mechansm of the object s not very clear, the dmensons of the nput varable s very bg, and there are serous of the collnearty, all sorts of drect solvng method are encountered the nsurmountable dffcultes: due to the exstence of collnearty could not exst. The lack of understandng of mechansm of object, nonlnear regresson functon form s very dffcult to gve and the ncreasng of the nput nodes, the dffculty of neural network tranng also dramatcally larger, and easy to cause a fttng problem. In addton, due to the large nput dmenson and the fnal regresson model by means of drect robustness are relatvely poor, ths s because the models predct stablty depends largely on model contans several ndependent varables. In the formula one, we defne the data compresson regresson analyss method. Input data compresson basc dea s: when the output varables contrbute nformaton nput varables, exsts n the nput data space of a lnear or nonlnear subspace, can fnd a dmenson less than the p subspace, and accordng to a certan pont on the way of mappng the nput data space correspondng to the projecton space, can make the pont of projecton space n an acceptable way to explanng the correspondng output varable [16]. n a x f H x a y H x (1) mn 1 We should defne the parameters defned n the formula 2~4 before conductng the further research. At frst n order to guarantee the precson of the model, all may have an mpact on the system output varables are lsted n the nput varables. Therefore t tends to have strong collnearty between the correspondng nput varables. p k t H x, H : R R (2) x f t, k p f : R R (3) y t, k p : R R (4) The prncpal component regresson and the parameters n the man dfference s that the general multvarate lnear regresson wth the prncpal component regresson usng prncpal component estmaton nstead of least squares estmaton. Prncpal component estmaton s a based estmaton. In the Fgure 2, we show the mentoned regresson model. Fgure 2. The Demonstraton of the Regresson Patterns The basc method of prncpal component regresson s by constructng lnear combnaton of the orgnal varables to produce a seres of unrelated new varables, choose a few of new varables and make them as much as possble the orgnal varable nformaton, thus makng use of ths a few new varables nstead of the orgnal varable t possble to analyze and solve the problems. In the formula 5, we defne the multple lnear regresson models [17]. 380 Copyrght c 2016 SERSC

5 2 measurement testng n Y X e, e ~ N 0, I (5) In practce, sometmes to the centralzed and standardzed data as centralzed data to make the sum of each column element of desgn matrx X s zero. After standardzaton, we can use regresson ndependent varables of relatonshp between R analyss, elmnatng the varable and the unt and value range, easy to estmate of the estmates of the regresson coeffcent of the analyss. After standardzaton center, the lnear regresson model s shown as the follows. Y X e (6) Centered Centered Centered 0 At ths tme, when usng the prncpal component as new regresson ndependent varables, the approxmate prncpal component wth zero mpact on dependent varable can be gnored, so they can be removed from the regresson model. Then usng least square method to do the rest of prncpal component regresson, and back to the orgnal argument and got prncpal component regresson. The fgure three shows regresson curves wth dfference algorthms. Fgure 3. The Regresson Curves wth Dfference Algorthms 2.2. The Large-scale Data Regresson Modfcaton Approaches If the dependent varable s a composton data and assocated wth number of ndependent varables are also composton data, how to establsh the multple regresson model between them s crucal. Typcally, each data representaton s a theme of meanng, and composton data composed of multple components. In order to establsh the regresson model, a smple method s to lst all of the component composton data together formng a large varable set, and then uses the ordnary varable analyss method for the processng. Obvously, the defect drectly s dffcult to explan the meanng of the dfferent component data meanng and the functon n the model. Therefore, n ths desgn model n addton to varables and constrants, and complete the related questons, wll also need we consder the herarchy of the varables n the model. The formula 7 defnes the reverson [18-19]. Y ε (7) β0 β1x1... βk X k So use t for regresson modelng can reflect the characterstcs of the components as the model of nterpretablty and stronger. It has completely correlaton data, makng the ordnary least squares regresson method n ths case the complete falure, and therefore need to adopt modelng partal least-squares regresson method. We revse the formula 7 nto 8. Copyrght c 2016 SERSC 381

6 Y ln X u 1 2 (8) The sum of the squares of the resduals s smaller, the better fttng effect, llustrates the relatonshp between the two varables, the more closely. But not only the sze of the resdual sum of squares and relevant expermental raw data, and also the nature of the fttng functon, so should accordng to certan standard to collect the expermental data and try to elmnate the factors caused by resdual, more mportant s accordng to the rule of the rght to choose the approprate fttng functon shown as the followng formula 9. n( xy) ( x)( y) slope m 2 2 n( x ) ( x) (9) y m( x) nt ercept b n Tests, although consstent test condtons, but the repeated measurement results of a certan parameter wll fluctuate wthn a certan range and ths s because the test ste has random dsturbance, caused the data of the stochastc volatlty. So must go through before fttng to the orgnal data preprocessng. Here, accordng to law of measured data, a gven confdence nterval n advance, f the samplng data s beyond ths range, "for the sngular value" that the samplng data s gve out. The followng formulas show the standard for selectng proper data. V[ε ] Cov[ ε,ε 2 2 E[ε ] σ (10) j ] E[ε,ε ] 0 (11) j 3. The Mathematcal Optmzaton Theory 3.1. The Revew of the Optmzaton and the Applcatons For a long tme, the mathematcans, operatonal research, and the engneers to seek for the global optmal pont pad a hard work, and acheved frutful results, these results are bascally dvded nto two drectons, namely, determne the type of optmzaton method and random search optmzaton method. So-called determne the type of optmzaton method, t s usng analytc propertes, through the strct mathematcal process to construct a certan converge to global optmal soluton of pont to get global optmal soluton and these analytcal propertes ncludng monotoncty, contnuty, smoothness and convexty and so on. Determne the type of method s one of the bggest advantages s that t has a very good the optmalty condtons, analytcal propertes for good questons also has good convergence and convergence speed. And ts dffculty les n the nature of the demand s hgher, and to know n advance that these propertes, n order to "sut the remedy to the case", and n engneerng and practcal problems n the optmzaton problem are often unpredctable, mostly can't satsfy method to determne the type of the objectve functon and the constrant condton of analytcal requrements, or smply do not know ts analytcal nature. As a result, people turn to random search method to fnd the global optmal soluton of the problem [20-23]. Due to these propertes manly reflects the local nature of the problem, for seekng the local optmal soluton of the problem become a focus n the study of the early stage of the optmzaton method, now get a lot of strong convergence effcent algorthms, such as the Newton's method, the conjugate gradent method, quadratc programmng method and the trust regon method step by step. The method frst to get the local optmal soluton of the problem, and then come up to the global optmal soluton from two drectons, one s drect judgment of local optmal soluton s global optmal soluton, here s the man use of global convexty, monotoncty and or specal problems such as the basc quadratc 382 Copyrght c 2016 SERSC

7 programmng mplementaton; From the local optmal soluton, to seek better local optmal soluton, ths knd of method s currently become the fronter of research method to determne the type, such as the tunnel functon method and the flled functon method, and the separaton of programmng method and the monotone method step by step. To fnalze the optmzaton steps, we wll need to test the methods on the data nterpretaton plane, n the followng fgure four, we demonstrate the plane that wll be adopted later [24]. Fgure 4. The Data Interpretaton Plane for Optmzaaton 3.2. The Revsed and Modfed Optmzaton Algorthm Partcle swarm optmzaton algorthm s a random optmzaton algorthm based on swarm ntellgence, the basc dea s through the cooperaton and competton between partcles n a populaton of swarm ntellgence to gude the optmzaton search and t has smple prncple and the mechansm, to keep both the profound background of swarm ntellgence evoluton algorthm, and has a good optmzaton performance. The followng formulas defne the basc condtons of the algorthm [25]. ln j a j : a j (12) j aff a : a, 1 (13) j j j j cone jaj : a j, j 0 (14) conv a : a, 1, 0 j (15) j j j j j Dynamc servce composton technology s currently a hot research topc. In the process of the dynamc servce composton need to choose the same functon of multple servces and composte servces to provde most sutable for the user's servce and wth the ncreasngly rch, provde servces ncrease sharply to choose the scale of the problem. The tradtonal PSO algorthm n optmzaton of complex functons fashon to save a lot of shortcomngs, such as poor local search ablty and search accuracy s not hgh, easy to fall nto the local optmum, search late shock, etc. Researchers from dfferent angles on the tradtonal PSO algorthm are mproved, n order to mprove the algorthm performance. The followng formulas defne one. j j j 0 1,, c ya x j n (16) Copyrght c 2016 SERSC 383

8 0 1,, y Ax b m (17) An mproved ant colony algorthm to optmze servce portfolo and has good performance, but also can adapt to the dynamc nature of the combnatoral optmzaton problem. Here to choose the combnaton of ant colony algorthm to solve the above optmzaton problem. Through cooperaton and competton between partcles n a populaton of swarm ntellgence to gude the optmzaton search, fast convergence speed. But n the early algorthms such as low accuracy, prone to defects under the condton of convergence, as a result of all partcles to the optmal drecton, the partcle dversty, late sgnfcantly slow convergence speed and s easy to fall nto local optmum. We optmze the tradtonal ones followng the gudelne. f ( x ˆ) = max{ f ( x): x } (18) ˆx = argmax{ f ( x): x } (19) The scope of the parameter n the formulas should follow the restrcton [26]. n j xr : g x k (for 1,, r), h x l (for j 1,, q) (20) j Chaos s a state of the rule-less movement, n a determnstc nonlnear system, wthout any addtonal random factor can generate the random behavor. Chaotc system has the specal movement rule and man show s randomness, regularty and ergodcty. In the Fgure 5, we show the fnal pattern of the mathematcal optmzaton result. Fgure 5. The Fnal Patternof the Mathematcal Optmzaton Result 4. The Enterprse Performance Management Informaton System 4.1. The Performance Evaluaton Model Based on the value assessment s process ntegrty, emphass on key value drvng factors of enterprse, throughout the organzaton to mprove the strategy and the busness decsons. Based on the value assessment of the specal attenton to how to apply these concepts to make decson strategy and day-to-day operatons, management mplementaton based on the value assessment of enterprse overall goal, analyss can be ntegrated wth technology and the core management procedures promote enterprse management and decson makng wll focused on the value drvng factors, to acheve ts maxmum value. The modern enterprse how to establsh conforms to actual busness operaton performance evaluaton system shown below. 384 Copyrght c 2016 SERSC

9 Key performance evaluaton ndcators and targets set. Key performance ndcators and targets set core and s the startng pont of the performance evaluaton cycle. Key performance ndcators s set accordng to the enterprse the strategc target of the quantfable objectves, once enterprse's strategc goal to determne key performance ndcators can provde enterprses wth a clear and ntutve method to measure the busness enterprse the strategc goals to acheve or the not. In addton, the enterprse also needs to set for each key performance evaluaton ndex of short-term and longterm goal, to defne the success of enterprse standards and expectatons for the future of enterprses, key performance ndcators and goals for take a top-down approach. The establshment of enterprse strategy. Enterprse strategy s bass of performance evaluaton cycle. Enterprse strategy for enterprse development provdes a clear goal, s the other lnks n the performance evaluaton cycle n order to acheve enterprse strategc objectves. Enterprse strategc objectves and key success factors for the enterprse's key performance evaluaton ndex and target formulaton provdes the bass and drecton [27-28]. Montorng and evaluaton. Montorng and the evaluaton are based on performance targets for each department and process the actual performance of measurement and evaluaton, tmely understandng of enterprse nternal operaton and fnd the exstng problems and gap. In settng performance goals at the same tme, therefore, enterprse needs to determne the key performance evaluaton ndexes of specfc weght, as a standard of performance evaluaton for each assessment unt. Reward and gudance. Reward and gudance s last lnk of performance evaluaton cycle. Incentves to lnk set up and runnng, performance evaluaton cycle encourages enterprses wthn the correct behavor and motvate employees to acheve busness goals and efforts, At the same tme, by gudng the lnk set up and runnng, the performance evaluaton cycle was carred out on problems arsng from the enterprse nternal operaton and correct gudance n order to acheve the progress of enterprse. Company management accordng to the development of the enterprse strategc target and combned wth the company's msson and values, determne the enterprse level, functon level and process level of key performance ndcators and goals. Fgure 6. The Performance Evaluaton Model and the Parameters Copyrght c 2016 SERSC 385

10 4.2. The Performance Model and the Management System Development Enterprse mddle managers performance evaluaton s a dynamc process, dfferent due to dfferent evaluaton purpose or the same enterprse n dfferent perods, dfferent emphass of the mddle managers performance evaluaton, whch can lead to the determne dfferences n evaluaton ndex weght and ths s drectly related to the accuracy of the assessment and the scentfc features. There are many ndcators n the performance evaluaton ndex s as the descrptve, past the nspecton, the ndcators for the employee just a supportng role, s not really nvolved n the employee's performance apprasal, ths s the dsadvantages of prevous performance evaluaton. So we need to apprasal ndex and apprasal results are quanttatve processng whch s the purpose of the quanttatve evaluaton model s set up to quantfy the performance apprasal results. The proposed algorthm s demonstrated n the Fgure 7 as the bass of the pror dscusson n the prevous sectons. 5. Experment Fgure 7. The Flowchart of the Proposed Algorthm In ths part, we smulate the proposed method. The Fgure 8 shows performance evaluaton chan for further analyss. In ths Fgure, we mark all the necessary and potental elements for analyss wth the dscusson of the weght. Fgure 8. The Performance Evaluaton Chan for Analyss 386 Copyrght c 2016 SERSC

11 Fgure 9. The Parameter Testng for the Proposed Methodology Fgure 10. The Performance Testng Across Dfferent Systems and Approaches As demonstrated n the Fgure 9, we llustrate parameter testng for proposed methodology. No matter how to dvde the performance apprasal ndex, the assessment work must be done: assessment of ndcators should be n as far as possble to quantfable, the observed ndexes s gven prorty to, should be more for behavoral descrpton and not a descrpton of the valuaton. Evaluaton ndcators should as far as possble concse, wthout much too numerous n number, otherwse dffcult to decde the sze of the weght of each evaluaton ndex. When determnng the content of the assessment ndex, want to consder the actual characterstcs and development of enterprse strategy, establsh targeted and enterprse strategc corresponds to the ndex system to support enterprse strategc target realzaton. In the Fgure 10, we show the performance testng across dfferent systems and approaches. The result shows that our method outperforms the other approaches. 6. Concluson and Summary Ths research analyzes the enterprse performance management nformaton system (MIS) development and robustness optmzaton based on data regresson analyss and mathematcal optmzaton theory. The development of nformaton scence and technology wll promote the development of scence and technology, economy and socety, and the progress of scence and technology, economy and the nformaton Copyrght c 2016 SERSC 387

12 technology changes have created the favorable condtons. In recent years, people are ntroducng new technology nto nformaton feld, make nformaton management of whole socety, nformaton retreval, nformaton analyss, reached a new level, promote the human cvlzaton and economc development, vgorously promote the progress of socety. Practce has proved that the computer nformaton systems technology use can brng huge economc benefts and socal benefts, but the development of an nformaton system, must have a certan foundaton, namely must have certan condtons and the condtons of reasonable applcaton of these systems. Our research combnes the core technques of the data regresson analyss and mathematcal optmzaton theory to enhance the effcency and performance robustness of the tradtonal systems. The experment analyss proves the feasblty of our approach whch wll be meanngful. References [1] B, France, and R E. Crossler. "Prvacy n the dgtal age: a revew of nformaton prvacy research n nformaton systems." MIS quarterly 35, no. 4 (2011), pp [2] G Severn V., S A. Leech, and P J. Schmdt. "A revew of ERP research: A future agenda for accountng nformaton systems." Journal of nformaton systems 25, no. 1 (2011):, pp [3] L Seul-K, and J-H Yu. "Success model of project management nformaton system n constructon." Automaton n constructon 25 (2012), pp [4] P Jagan A., and Sheryl R. Haut. "Patents wth eplepsy and psychogenc non-epleptc sezures: an npatent vdeo-eeg montorng study." Sezure 21, no. 1 (2012), pp [5] A Amn. "Organzatonal Levels Model for Measurng the Effectveness of Enterprse Resource Plannng System (Case Study TUGA Company, Iran)." Unversal Journal of Industral and Busness Management 2, no. 2 (2014), pp [6] B, Edward WN, and Johann Mtlohner. "Characterstcs of the multple attrbute decson makng methodology n enterprse resource plannng software decsons." Communcatons of the IIMA 5, no. 1 (2015), p.6. [7] T, Wen-Hsen, K-C Lee, Jau-Yang Lu, Sn-Jn Ln, and Yu-We Chou. "The nfluence of enterprse resource plannng (ERP) systems' performance on earnngs management." Enterprse Informaton Systems 6, no. 4 (2012), pp [8] M Sunl, N RAMASUBBU, and Va Sambamurthy. "How nformaton management capablty nfluences frm performance." MIS quarterly 35, no. 1 (2011), p [9] P Danel, and J Olhager. "Supply chan ntegraton and performance: The effects of long-term relatonshps, nformaton technology and sharng, and logstcs ntegraton." Internatonal Journal of Producton Economcs 135, no. 1 (2012), pp [10] M, Konstantnos, and K Lagkouras. "Multobjectve evolutonary algorthms for portfolo management: A comprehensve lterature revew." Expert Systems wth Applcatons 39, no. 14 (2012), pp [11] A Olubukola, P Englsh, and J Pnkney. "Systematc revew and meta-analyss of dfferent detary approaches to the management of type 2 dabetes." The Amercan journal of clncal nutrton 97, no. 3 (2013), pp [12] X, L Da. "Informaton archtecture for supply chan qualty management." Internatonal Journal of Producton Research 49, no. 1 (2011), pp [13] Z, Abdel Nasser H. "An ntegrated success model for evaluatng nformaton system n publc sectors." Journal of Emergng Trends n Computng and Informaton Scences 3, no. 6 (2012), pp [14] C Serra, C. Ordóñez, A. Saavedra, and J. R. Gallego. "Element enrchment factor calculaton usng gran-sze dstrbuton and functonal data regresson." Chemosphere 119 (2015), pp [15] Y Jnhong, and X Zhou. "Asymptotc theory n fxed effects panel data seemngly unrelated partally lnear regresson models." Econometrc Theory 30, no. 02 (2014), pp [16] L Erc Wa Mng. "An ncremental adaptve neural network model for onlne nosy data regresson and ts applcaton to compartment fre studes." Appled Soft Computng 11, no. 1 (2011), pp [17] P, Ncholas G., and James G. Scott. "Data augmentaton for non-gaussan regresson models usng varance-mean mxtures." Bometrka (2013): ass081. [18] S Tamer. "Data hdng n MPEG vdeo fles usng multvarate regresson and flexble macroblock orderng." Informaton Forenscs and Securty, IEEE Transactons on 7, no. 2 (2012, pp [19] L Nengxang, L Lang, and P Veu. "Nonparametrc regresson estmaton for functonal statonary ergodc data wth mssng at random." Journal of Statstcal Plannng and Inference 162 (2015), pp [20] Z Zhongshan, K Long, J Wang, and F Dressler. "On swarm ntellgence nspred self-organzed networkng: ts bonc mechansms, desgnng prncples and optmzaton approaches." Communcatons Surveys & Tutorals, IEEE 16, no. 1 (2014), pp Copyrght c 2016 SERSC

13 [21] S Herbert M., I R. Gl, M C. Graff, Jeff Wampler, German Merlett, Te Sun, Hemal Patel et al. "3-D Hydraulc Fracturng and Reservor Flow Modelng Key to the Successful Implementaton of a Geo- Engneered Completon Optmzaton Program n the Eagle Ford Shale." Unconventonal Resources Technology Conference (URTEC), (2015). [22] B Ncolas, B Mshra, P-A. Absl, and Rodolphe Sepulchre. "Manopt, a Matlab toolbox for optmzaton on manfolds." The Journal of Machne Learnng Research 15, no. 1 (2014), pp [23] G Abhjt. "Smulaton-Based Optmzaton: An Overvew." In Smulaton-Based Optmzaton, pp Sprnger US, (2015). [24] AlM. M., M Golalkhan, and J Zhuang. "A computatonal study on dfferent penalty approaches for solvng constraned global optmzaton problems wth the electromagnetsm-lke method." Optmzaton 63, no. 3 (2014), pp [25] N Angela, and A Olshevsky. "Dstrbuted optmzaton over tme-varyng drected graphs." Automatc Control, IEEE Transactons on 60, no. 3 (2015), pp [26] G Kudong, C Du, H Wang, and S Zhang. "An Effcent of Coal and Gangue Recognton Algorthm." Internatonal Journal of Sgnal Processng, Image Processng & Pattern Recognton 6, no. 4,345 (2013): p [27] L Hue, K Lane Chen, and J Yang. "Teachng enterprse resource plannng (ERP) systems n the supply chan management course." Communcatons of the IIMA 6, no. 3 (2015), p. 8. [28] Wang, Cheng-Hua, and Wen-Ya Tsa. "Elucdatng How Interface Desgn and Cogntve Functon Affect Learnng Performance n the Enterprse Resource Plannng (ERP) Software System." Journal of Testng and Evaluaton 44, no. 1 (2014): Copyrght c 2016 SERSC 389

14 s 390 Copyrght c 2016 SERSC

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