Mobile Data Mining for Intelligent Healthcare Support
|
|
- Bridget Lawson
- 8 years ago
- Views:
Transcription
1 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces Moble Daa Mg for Iellge Healhcare uppor Par Delr Haghgh, Arkady Zaslavsky, hoal Krshaswamy, Mohamed Medha Gaber Ceer for Dsrbued ysems ad ofware Egeerg Moash Uversy, Ausrala {par.delrhaghgh, arkady.zaslavsky, shoal.krshaswamy,, mohamed.gaber}@foech.moash.edu.au Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble healhcare applcaos. I hs paper we propose a ovel approach for uao-aware Adapve Processg (AAP) of daa sreams for smar ad real-me aalyss of daa. The mplemeao ad evaluao of he framework for a healh moorg applcao s descrbed.. Iroduco Recely, ovaos moble commucaos ad low-cos of wreless bosesors have paved he way for developme of moble healhcare applcaos ha provde a covee, safe ad cosa way of moorg of val sgs of paes. A key he provso of moble healhcare servces s he ssue of usg echologcal ovao o suppor couous moorg of pae codos, provdg a degree of self-dagoss ad eablg effecve real-me decso makg o reduce faales. Ubquous Daa ream Mg (UDM) echques [] such as lghwegh, oe-pass daa sream mg algorhms [2-3] ca perform real-me aalyss o-board small/moble devces whle cosderg avalable resources such as baery charge ad avalable memory. However, o perform smar ad ellge aalyss of daa o moble devces, s mperave for adapao sraeges o facor coexual formao. Coexual formao ca be relaed o a ework, applcao, evrome, process, user or devce. As a mea-level cocep over coex we defe he oo of a suao ha s ferred from coexual formao [4]. uao-awareess provdes applcaos wh a more geeral ad absrac vew of her evrome raher ha focusg o dvdual peces of coex. uao-aware adapve daa sream mg leverages he full poeal of UDM by gog beyod mere avalable resources ad ca eable, f o guaraee, he couy ad cossecy of he rug applcaos. I real-world, suaos evolve ad chage o oher suaos (e.g. healhy chages o hypereso). Chages ha occur bewee suaos are also good dcaors of suaos ha may emerge albe wh some vagueess ad uceray. To eable suao-awareess moble healhcare applcaos, s mpora for he suao modelg ad reasog approach o represe uceray ad vagueess assocaed wh healh-relaed suaos. Revewg rece works moble healhcare reveals ha mos of hese proecs [5-8] have maly focused o usg, ehacg or combg exsg echologes ad coex-aware proecs [9-3] mosly deal wh a lmed scope (.e. o applcable o oher coex-aware scearos). I moble healhcare compug, a geeral approach for modelg ad reasog abou ucera, healh suaos ad performg smar ad cos-effce aalyss of daa real-me has o bee roduced ad s a ope ssue. I hs paper we propose suao-aware adapve processg (AAP) of daa sreams for moble healhcare applcaos. The ovely ad corbuo of hs proec are as follows: ) suao-awareess s acheved by Fuzzy uao Iferece (FI) ha combes fuzzy logc prcples wh he Coex paces (C) model, a formal ad geeral coex modelg ad reasog for pervasve compug evromes. The sreghs of fuzzy logc for modelg of vague suaos are combed wh he C models uderlyg heorecal bass for supporg coex-aware pervasve compug scearos; ) AAP corporaes suao-awareess o daa sream mg ad provdes gradual ug of daa sreamg parameers accordg o occurrg suaos ad avalable resources. Ths approach mproves daa sream mg operaos a ellge ad coseffce maer. The AAP approach eables couy ad cossecy of rug operaos ha are of hgh mpora for healh moorg applcaos ha deal wh sesve ad crcal daa /09 $ IEEE
2 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces A cearo Joh has had a hear aack ad s released from hospal bu here are cocers ha he mgh be suscepble o aoher hear aack ad s also experecg blood pressure flucuaos. Cosa moorg of hs val sgs could help hm o reduce hs axey, decrease he eed for roue vss o medcal facles, ad also deec early warg feaures of a possble mpedg eve. He has a smar phoe wh AAP salled o ad s wllg o wear bosesors o measure hs val sgs. The daa s wrelessly se o hs moble where AAP deecs ay chages o oly hs val sgs bu ay coexual formao ha s relaed o he applcao (e.g. he baery level of he moble phoe). AAP uses hs formao o reaso abou suaos real-me ad accordg o ferred suaos, performs ellge ad cos-effce aalyss of daa. Whe flucuaos of val sgs are wh a specfed accepable hreshold, here s o eed for freque measureme ad use of resources ca be reduced ad moderaed. However, whe hese flucuaos are over he hreshold, hs suao warras a closer moorg by he sysem ad more freque measuremes. Ths ype of adapao requres facorg boh avalable resources ad crcaly of healh suaos. Ths paper s srucured as follows: eco 2 dscusses he relaed work. eco 3 preses he AAP archecure. eco 4 descrbes he Fuzzy uao Iferece (FI) ha eables suaoawareess. eco 5 dscusses he adapao ege. eco 6 ad 7 descrbes mplemeao ad evaluao respecvely. Fally seco 8 cocludes he paper ad dscusses he fuure work. moble healh moorg applcaos ha has bee suded [8]. Coex-awareess s oe of he key requremes of healh moorg sysems ha eables auoomous operaos whou paes erveo ad ehaces decso makg of healhcare professoals o pae codo [9]. However, here are lmed researches ha have aemped o fully address he coexawareess or provde a geeral ad formal represeao of coex [0-2]. Oe of he works moble healhcare ha corporaes boh coexawareess ad adapao s proposed [3] bu he paper does o provde he deals of how ad whe he proposed adapao sraeges are appled. udes daa sream processg [4-5] are very applcaospecfc ad focus o very lmed areas of research. A geeral approach for smar ad cos-effce aalyss of daa for moble healhcare sysems has o bee roduced he curre sae-of-he-ar ad s sll a ope ssue. 3. uao-aware Adapve Processg (AAP) of Daa reams The archecure for uao-aware Adapve Processg (AAP) of daa sreams cosss of hree compoes of Fuzzy uao Iferece (FI), Resource Moor (RM) ad Adapao Ege (AE) as show Fgure. 2. Relaed Work Moble healhcare compug s a ew ad evolvg area of research ha explos he rece developme moble eworks ad commucaos for healh moorg applcaos. EPI-MEDIC [5] s a large scale Europea proec ha provdes persoal moorg of ECG sgals for early deeco of cardac schema ad arrhyhma ad geerag dffere levels of alarms. Aoher Europea proec called he MobHealh proec [6] uses 2.5 (GPR) ad 3G (UMT) echologes o egrae all he sesors ad acuaors o a wreless ework called Body Area Nework (BAN). The proec of ubmo (Ubquous Moorg Evrome for Wearable ad Implaable esors) [7] ams o provde couous maageme of paes maly focusg o sesors ad wreless echology raher ha daa aalyss echques. Persoalzao s aoher area of focus developg Fgure. The archecure of AAP (uao- Aware Adapve Processg) of Daa reams The FI ege eables suao-awareess usg fuzzy logc prcples. Resource Moor (RM) s a sofware compoe ha couously moors avalable resources such as avalable memory ad baery usage ad repors her avalably o he adapao ege. The Adapao Ege (AE) s resposble for gradual ug of daa sream processg parameers real-me accordg o he occurrg suaos ad avalable resources. The 2
3 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces AAP layer s bul o he op of he daa sream mg algorhms rug o moble devces ad provdes hem wh suao-aware adapao. The ex seco dscusses he FI echque. 4. Fuzzy uao Iferece (FI) FI s a suao modelg ad reasog approach ha egraes fuzzy logc o he Coex paces (C) model [4]. FI uses he beefs of he C model for supporg pervasve compug evromes whle corporag fuzzy logc o deal wh uceray assocaed wh vague ad real-world suaos. 4.. The Coex paces model The Coex paces model (hereafer C) represes coexual formao as geomercal obecs muldmesoal space called suaos [4]. The cocep of a suao space s characerzed by a se of regos. Each rego s a se of accepable values of a coex arbue ha sasfes a predcae. I addo o basc coceps ad echques for suao modelg ad reasog, he C model provdes heurscs developed specfcally for addressg coex-awareess uder uceray. These heurscs are egraed o reasog echques ha are uly-based daa fuso algorhms ad compue he cofdece level he occurrece of a suao [6]. The C deals wh uceray maly assocaed wh sesors accuraces. Ye here s aoher aspec of uceray huma coceps ad real-world suaos ha eeds o be represeed by he coex model ad refleced he resuls of suao reasog. Fuzzy logc uses mul-value logc ad has he beef of dealg wh hs level of uceray by assgg membershp degrees o values uao Modelg FI cosss of hree subcompoe cludg fuzzfer, rules ad ferece ege. Fuzzfer, as a sofware compoe, maps crsp pu (.e. values of coex arbues) o fuzzy ses usg rapezodal membershp fucos. I a fuzzy se, membershp of a em s gradual ad s represeed by a degree bewee 0 ad [7]. I FI, suaos of eres are defed usg fuzzy rules by doma expers ad sored a rule reposory. Each FI rule cosss of mulple codos oed wh he AND operaor bu a codo ca self be a dsuco of codos [8]. To model he mporace of codos, we assg a wegh w o each codo wh a value ragg bewee 0 ad. The sum of weghs s per rule. A wegh represes he mporace of s assged codo relave o oher codos defg a suao. A example of a FI rule s as follows: f Room-Temperaure s ho ad Hear-Rae s fas ad ( Age s mddle-aged or old) he suao s hea sroke The ex subseco dscusses suao reasog uao Reasog To reaso abou a suao, rules eed o be evaluaed o produce a sgle oupu ha deermes he membershp degree of he coseque [9]. The codos oed wh he OR operaor are evaluaed usg he maxmum fuco. However, o evaluae he codos oed wh he AND operaor, FI provdes four reasog echques as show Table. Table. Reasog echques Heursc: weghs ad corbuo level C Cofdece = w c = FI Cofdece = w μ ( x ) = Heursc: sesors accuracy C Cofdece = w. Pr( a A ) FI = Cofdece = w μ ( f ( x, e = Heursc: symmerc ad asymmerc arbues C Cofdece = w.pr( a A ) FI where = a CA CA Cofdece = w μ ( f ( x, e = where x F ad F LV LVA Heursc: paral ad complee coame C m Cofdece = q w. p( a A ) + q p( a A FI A )) )) 2 k k ) = k= + q2 = where q ad a CA CA, a CA A = + q2 = k Cofdece = q w. μ( f ( x, e ) + q μ( f ( xk, ek )) where q ad x F, F LV LVA x k F, F LV m 2 k = 3
4 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces These echques egrae fuzzy logc o he C reasog mehods o provde aoher aspec of uceray (.e. uceray of suaos ad dela chages of coex) he compuao of cofdece value for he occurrece of a suao. The suao reasog echques of C are based o four heurscs ha are roduced o maage uceray pervasve compug evromes. These heurscs are as follows: ) relave weghs of coex arbues ad cofdece level of values; ) sesors accuracy; ) symmerc ad asymmerc coex arbues; v) ad paral ad complee coame of symmerc coex arbues. Table depcs reasog mehods of C, her FI equvale ha are combed wh fuzzy logc ad her uderlyg heurscs ad heorecal coceps. The ex subsecos dscuss each heursc ad reasog echque more deal Weghs ad corbuo level. The frs reasog echque of C s based o he weghs of coex arbues ad he level of cofdece of arbues values. Weghs are assged o coex arbues ad represe relave mporace of each coex arbue for ferrg a suao. Level of cofdece s assged o each eleme ad reflecs how ha eleme relaes o he modeled suao. I hs heursc, he corbuo fuco ha compues he corbuo level s proposed a a cocepual level ad s mplemeao s laer roduced he secod reasog echque based o sesors accuracy. I FI, he cocep of weghs s assocaed wh lgusc varables (.e. coex arbues). The cocep of corbuo level s smlar o he membershp degree of elemes a fuzzy se bu hey are mplemeed usg membershp fucos. The resul of w μ ( x ) represes a weghed membershp degree of x ad represes he umber of codos a rule () esors accuracy. To provde auomac compuao of he corbuo level a ru-me, he secod reasog mehod of C uses he mpac of sesor accuraces ad urelably as a deermg facor o compue he corbuo level. Ths mehod compues he probably of a coex arbue correc value a beg coaed he rego A. To compue he probably value based o he relably of a sesor, he relably of readg (e.g. 95%) s used o represe he probably value (.e. =0.95). The secod opo o compue he probably value s o egrae he sesors accuracy of readg raher ha he relably of readg. Usg hs opo, he probably value s calculaed he followg forma: ) Pr( e a m( A )) Pr( e a max( A )). where a deoes he sesed value of he coex arbue, e represes he sesor readg error (.e. a - a ) ad m( ) A ad max( A ) represe mmum ad maxmum values of he rego. The secod reasog mehod of C deals wh uceray facorg accuraces of sesors however hs equao does o reflec dela chages of values he equao ad s o adequae o reaso abou vague suaos. The FI equvale echque o oly corporaes he corbuo level assocaed wh sesors accuracy bu cludes he membershp of he values as aoher facor affecg he corbuo level. I he FI model, we frs calculae he correc value based o he relably or error rae ad he pass o he membershp fuco. The fuco f calculaes he correc value of he coex based o he accuracy value e. If e s a relably rae, he sesed value s mulpled by ad f s a error rae (.e. ±) s added o he sesed value ymmerc ad asymmerc coex arbues. The hrd reasog echque of C roduces he coceps of symmerc coex arbue CA ad asymmerc coex arbue CA A. A symmerc coex arbue creases he cofdece ferrg a suao f s value s wh he correspodg rego ad decreases he cofdece f s ousde ha rego (e.g. reasog abou he hypereso suao based o blood pressure). A asymmerc coex arbue creases he cofdece ferrg a suao f s value s wh he correspodg rego bu would o decrease he cofdece f s ousde ha rego (e.g. reasog abou he hea sroke suao based o age). Wheever a asymmerc arbue s o coaed wh s rego, he redsrbuo mehod assgs 0 o he wegh of he arbue ad recalculaes he relave weghs for he remag arbues as follows. 2) w = w / w = The cocep of symmerc ad asymmerc arbues ad s correspodg reasog echque s appled o FI (as show Table ). However, sce 4
5 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces values ha lgusc varables ake are o umerc (.e. hese values are called erms ha represe fuzzy ses), he cocep of symmerc ad asymmerc coceps are appled o he values of fuzzy ses assocaed wh lgusc varables Paral ad complee coame. The fourh heursc deals wh he fac ha he value of a mpora coex arbue should affec he resul of he suao ferece more ha he oher arbues (.e. less mpora oes) ad whe several arbues are sgfca for he evaluao of a suao we may wa o esure ha all of hem are coaed her regos. Ths heursc has bee egraed o he fourh reasog echque ha ams o address he rade-off bewee complee coame of all symmerc coex arbues (.e. whe all values of symmerc arbues are coaed her correspodg regos) ad her dvdual corbuo usg he hrd reasog echque. Ths heursc does o apply o asymmerc arbues because hey do o decrease he cofdece for he occurrece of a suao. To address he rade-off bewee complee ad paral coame, he fourh reasog echque preses each aspec of coame wh a dmeso usg uly weghs (.e q ad q 2 ) ad combes hem owards ferrg he occurrece of a suao. The uly weghs of wo dmesos deerme whch aspec of coame s more mpora (.e. complee or paral). The cocep of paral ad complee coame ad s reasog echque are appled o FI. mlar o he hrd reasog mehod, FI maps values of symmerc coex arbues o he values of fuzzy ses correspodg o symmerc lgusc varables. Resuls of suao reasog usg he echques dscussed earler suggess he degree of cofdece he occurrece of a suao. I FI, f he oupu of a rule evaluao for he hypereso suao yelds a degree of 0.885, we ca sugges ha he level of cofdece he occurrece of hypereso s Ths value ca be compared o a cofdece hreshold bewee 0 ad (.e. predefed by he applcaos desgers) o deerme wheher a suao s occurrg. The ex seco dscusses he compoe of he AE (Adapao Ege). 5. Adapao Ege (AE) The AE (Adapao Ege) s resposble for gradual ug of daa sream processg parameers accordg o he occurrg suao/s ad avalable resources real me. Lghwegh daa sream mg echques such LWC, LWCLass, RA-Cluser, ERA- Cluser, ad DRA-Cluser [2-3, 20-22] are adapve o avalably of resources va adusg he algorhm parameers. These parameers corol oupu, pu ad/or he process of he algorhm. I hese algorhms, he adapao process s doe hrough Algorhm Graulary (AG) approach. AG has hree dffere varaos of AOG (Algorhm Oupu Graulary), AIG (Algorhm Ipu Graulary) ad APG (Algorhm Processg Graulary) [2-22]. AOG corols he algorhm oupu rae based o he avalably of memory va chagg he daa sream mg algorhm parameers o ecourage or dscourage he creao of ew oupu srucures. mlarly AIG ad APG [22] corol he pu rae ad cosumpo of processg power accordg o he baery level ad CPU usage respecvely. We have spred by he coceps of AG ad developed hree dffere adapao sraeges. These sraeges clude resource-aware, suao-aware ad hybrd sraeges as show Fgure 2. Fgure 2. Adapao of daa sream mg AE cosaly moors occurrg suaos ha are ferred by FI ad avalably of resources repored by RM. Each pre-defed suao eeds o be assged a crcaly value (.e. a value bewee 0 ad ) ha dcaes her mporace. For boh suaos () ad compuaoal resources (R), here are wo hresholds (.e. lower ad upper bouds), a value bewee 0 ad, whch dcae safe, medum ad crcal levels. The hgher he value s, he hgher he suao mporace ad resource usage s. Based o hese levels of crcaly for suaos ad resources, here ca be e possble varaos (cases) of adapao a ru 5
6 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces me. Coroller ha s a subcompoe of AE makes decsos o whch sraegy eeds o be performed accordg o hese hresholds. These e cases are preseed Table 2. We have allocaed he adapao sraeges accordg o hese e cases. Whe resources are crcal meas ha he moble devce ca o coue he mg operaos ad he adapao sraeges ha we provde are o adequae o address he ssue. Therefore oher sraeges such as mgrao of he daa or he process eed o be performed whch are ou of he scope of hs proec. Cases Table 2. Adapao Cases Adapao sraegy - f R a safe level ad a safe level 2- f R a safe level ad a medum level 3- f R a safe level ad a crcal level 4- f R a medum level ad a safe level 5- f R a medum level ad a medum level 6- f R a medum level ad a crcal level 7- f R a crcal level ad a safe level 8- f R a crcal level ad a medum level 9- f R a crcal level ad a crcal level uao-aware sraegy uao-aware sraegy uao-aware sraegy Resource-aware sraegy Hybrd sraegy Hybrd sraegy Oher sraeges e.g. mgrao 5.. Resource-aware Adapao raegy Resource-aware adapao sraegy occurs whe he suao s a safe level bu resource avalably s a medum level. Ths s because ormal suaos do o requre freque moorg ad he resuls of resource-aware adapao do o coradc he requremes of ormal suaos. Resource-awareess s spred by he AG approach. Oe of he AOG-based cluserg algorhms s called LghWegh Cluserg (LWC) [29]. LWC cosders a hreshold dsace measure for cluserg of daa. Icreasg hs hreshold dscourages formg of ew clusers ad ur reduces resource cosumpo. AOG s a hree-sage, resource-aware dsacebased mg daa sreams approach. The process of mg daa sreams usg AOG sars wh a mg phase. I hs sep, a value of hreshold dsace measure s deermed. Ths hreshold has he ably o corol he oupu rae of he rug mg algorhm. The secod sage AOG-mg approach s he adapao phase. I hs phase, he hreshold value s adused o cope wh he daa rae of he comg sream, avalable memory, ad me cosras o fll he memory wh geeraed kowledge (daa mg oupu). The las sage AOG approach s he kowledge egrao phase. Ths sage represes he mergg of geeraed resuls whe he memory s full. Ths egrao allows he couy of he mg process o resource-cosraed devces. The ex subseco dscusses suao-aware adapao sraegy based o he resuls of he FI uao-aware Adapao raegy uao-aware adapao AE s performed whe resources are avalable ad a safe level. uao-aware adapao occurs based o occurrg suaos ferred by FI. These resuls are mulple suaos wh dffere level of cofdece. To provde a fe-graed adapao ad reflecg he level of cofdece of each suao he adapao phase, we compue weghed average of he daa mg parameer value based o cofdece values of suaos ad he pre-se value of he parameer for each suao. The pre-se values of parameers are auomacally calculaed based o he mporace values of he suaos ha wll be dscussed furher he evaluao seco. The suao-aware adapao eables reflecg all he resuls of suao ferece he adapao of parameer values ad s represeed as follows: 3) p = μ p / = = μ where p represes he se value of a parameer for a pre-defed suao, μ deoes he membershp degree of suao where ad represes he umber of pre-defed suaos, ad p represes aggregaed value of he parameer. uao-aware adapao self resuls coseffcecy because whe a suao has a lower mporace value, he compued se value for he hreshold wll be a hgher value. Ths decreases he oupu of he LWC algorhm ad reduces he memory cosumpo. The ex subseco descrbes hybrd adapao sraegy Hybrd Adapao raegy Whe resources are a medum level ad suaos are a medum or crcal level (.e. cases 5 ad 6 Table 2), he coroller apples he hybrd adapao 6
7 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces sraegy. Whe he adapao cases 5 or 6 occurs, resource-aware ad suao-aware adapao sraeges each compue dffere values accordg o resource avalably ad occurrg suaos respecvely. Therefore here s a rade-off bewee he resuls of hese wo sraeges. Hybrd adapao sraegy addresses hs ssue by compug he average value of parameer based o he resuls of he wo sraeges ad crcaly values of he suao ad resource as follows: 4) p I ( p R. crcaly R ) + ( p. crcaly ) = crcaly + crcaly R Havg dscussed he heorecal framework of our work, he followg seco preses he mplemeao ad evaluaos we have performed. 6. Implemeao We have mplemeed a prooype of healh moorg applcao based o FI J2ME ad deployed o a Noka N95 (show Fgure 3). The prooype reasos abou suaos of ormal, prehypoeso, hypoeso, pre-hypereso ad hypereso. Ths applcao ca be used by paes who suffer from blood pressure flucuaos. A rapezodal membershp fuco s used o compue membershp degree of coex values. Coexual formao used cludes sysolc ad dasolc blood pressure (BP ad DBP) ad hear rae (HR). Fgure 3. The prooype of AAP-based healh moorg applcao wh a ECG bosesor To capure he paes hear rae, we have used a wo lead ECG bosesor from Alve Techologes [23] ha rasms ECG sgals usg Blueooh o he moble phoe. For he blood pressure, we have used radomly geeraed daa ha smulaes blood pressure flucuaos. The healh moorg applcao performs suao reasog ad suao-aware adapao real-me o he moble devce usg he LWC algorhm. aus bars o he moble phoe dsplays he level of ceray ad cofdece he occurrece of each suao. The evaluao of FI ad adapao ege s preseed he ex seco. 7. Evaluao For evaluao of AAP, we have performed wo evaluaos. Frs evaluao s a comparave evaluao of FI, C ad Dempser-hafer ad secod evaluao focuses o he adapao of hreshold parameer of LWC accordg o occurrg suaos. 7.. Evaluao of FI To evaluae he FI model, we have compared he FI suao reasog echque o he C ad Dempser-hafer (hereafer D) reasog approaches. The purpose of hs evaluao s frs o valdae he FI model agas a well-kow reasog echque such as D ad a coex model developed for pervasve compug evromes such as C. The secod obecve of he evaluao s o hghlgh he beefs of he FI o deal wh ucera suaos. I hs evaluao, we have cosdered suaos of hypoeso, ormal ad hypereso. These suaos are defed usg coex arbues of sysolc blood pressure (BP) ad dasolc blood pressure (DBP) wh he scale of ad mm Hg ad hear rae (HR) wh he rage of bpm. Table 3 depcs modelg of he hree suaos he C model cludg he weghs of arbues ad her correspodg regos of values. Assged weghs are 0.4 for BP ad DBP ad 0.2 for HR. Table 3. uao defos C uao Coex arbue Rego of values Hypoeso =BP 2=DBP 3=HR Normal Hypereso =BP 2=DBP 3=HR =BP 2=DBP 3=HR >85 ad 35 >60 ad 0 >45 ad 85 >35 >0 >85 The modelg of he hree suaos he FI model s preseed Table 4. Weghs of codos for he FI rules coform o he weghs used C. 7
8 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces Table 4. uao defos FI uao Lgusc Varable Terms represeed below va FI rules =BP 2=DBP 3=HR low, ormal, hgh low, ormal, hgh slow, ormal, fas Rule: f BP s low ad DBP s low ad HR s low he suao s hypoeso Rule2: f BP s ormal ad DBP s ormal ad HR s ormal he suao s ormal Rule3: f BP s hgh ad DBP s hgh ad HR s hgh he suao s hypereso The daase used for he evaluao cosss of 3 coex saes ad her scales corbue o he occurrece of each pre-defed suao as well as he ucera suaos ha occurs whe suaos evolve. Fgure 4 preses he resuls of he evaluao of C, D ad FI for suao reasog abou hypoeso, ormal ad hypereso..2 To apply he D algorhm for reasog abou suaos, we use he Dempser s rule of combao. The ormalzed verso of he combao rule s as follows F_Hypo C_Hypo D_Hypo 5) m( R) = P Q= R P Q= φ m ( P). m ( Q) m ( P). m ( Q) where m(r) deoes he mass value compued for a proposo R gve he evdeces ad. If R represes a suao, cosderg all exsg proposos, he erseco of some of hese proposos deoed as P ad Q resuls he proposo R (.e. ) ad he erseco of oher combaos of proposos resuls a empy se. To model he hree suaos of Hypoeso (L), Normal (N) ad Hypereso (H) wh D, we frs eed o defe proposos ad eves. ce all hree suaos are compable we clude a proposo of Ukow (U) ha would coss of hree suaos. The we defy he eves ad mass values ha reflec he assocao of a eve wh he occurreces of each proposo as depced Table 5. Table 5. Defos of eves ad mass values Eve N L H U BPLow (40-85) BPMed(86-35) BPHgh( ) DBPLow(20-60) DBPMed(6-0) DBPHgh( ) HRlow(20-45) HRMed(46-85) HRFas(86-30) Mass values are assged a way ha hey reflec o wha degree each eve dcaes a suao. ce we have based our suaos o hree coex arbues, we defe hree mass fucos correspodg. The we apply D combao over all proposos ad evdece FI_N C_N D_N F_Hyper C_Hyper D_Hyper Fgure 4. Resuls of he evaluao Fgure 4 shows hree approaches of C, D ad FI have a relavely smlar red accordg o coex chages. Whe he daa correspods o a pre-defed suao he resuls of hree approaches almos overlap. However, whe chages of daa dcae he occurrece of a ukow ad ucera suao, dffereces of reasog resuls bewee C, D ad FI are more appare. Compared o FI, he resuls of suao reasog by he C ad D mehods show sudde rses ad falls wh sharp edges whe suaos chage whch do o mach he real-lfe suaos. Ths s because he D ad C approaches do o deal wh dela chages of 8
9 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces he values ad are o able o reflec he gradual evoluo of oe suao o aoher suao. Whe he value of coex arbues decreases or creases, s membershp degree also creases ad decreases accordgly ad gradually. Ths eables FI o provde more accurae suao reasog resuls erms of reflecg very mor chages of coex. The evaluao valdaes he accuracy of he FI model for suao modelg ad reasog ad also shows ha FI s able o reflec very mor chages of coex suao ferece ad represe chages a more gradual ad smooh maer. The evaluao shows ha he FI model s more approprae approach for represeao of huma coceps ad for reasog abou he real-world suaos ha are defed by couous values. Healh-relaed suaos are examples of hese ypes of scearos where FI ca prove o be more fg approach compared o he D ad C reasog approaches Evaluao of uao-aware Adapao I he mplemeao of he AAP we have used he LghWegh Cluserg (LWC) [29] algorhm as he daa sream mg algorhm. Ths algorhm s oe-pass ad operaes usg he AOG prcpals as dscussed earler he paper. The LWC algorhm provdes adapably by adusg he parameer of hreshold dsace measure accordg o he avalable memory o a devce such as a PDA. I he evaluao of suao-aware adapao, we have adused he parameer of hreshold of LWC accordg o he cofdece level of he occurrg suaos. The values of LWC hreshold for each suao are compued based o he mporace value of each suao ad he mmum ad maxmum values of he hreshold (.e. 6 ad 45 respecvely) usg he followg formula: hreshold=mvalue+(maxvalue-mvalue)*(-mporace) Usg he above formula, f we assg he suaos of ormal, hypereso ad hypoeso he mporace values of 0., 0.9 ad 0.5, he compued hreshold values of each suao wll be 42, 0 ad 26 respecvely. These values are accepable gve a varao of 2 (.e. 42 dvded by 3) for ay of he coex arbues of BP, DBP ad HR has o sgfca mpac o a healhy dvdual whle a varao of 3 for hypereso ca be sgfca. To evaluae he suao-aware adapao, we have used he same 3 coex saes used for he frs evaluao. Fgure 5 shows ha he hreshold value s adused accordg o he cofdece value of each suao. Decreasg he hreshold value creases he umber of he oupu (clusers) ha s requred for closer moorg of more crcal suaos. Level of Cofdece of uao Daa ream Algorhm Threshold FI_N F_Hypo F_Hyper Fgure 5. uao-aware Adapao Resuls The ex seco cocludes he paper ad dscusses fuure work. 8. Cocluso ad Fuure Work I hs paper we proposed ad valdaed a geeral approach for suao-aware adapve processg (AAP) of daa sreams ha corporaes suaoawareess o daa sream processg usg fuzzy logc. The fuzzy suao ferece model allows modelg ad reasog abou real-world ad healhrelaed suaos. The AAP archecure eables realme aalyss of daa emaag from mulple sesors cludg bo-sesors oboard moble devces whle facorg coexual/suaoal formao ad resource avalably. Ths approach sgfcaly ehaces a rage of moble healhcare applcaos. There are several drecos whch we are exedg hs work. We are currely falzg mplemeao ad evaluao of hybrd adapao usg RA-Cluser [22] ha eables adapao of he parameers of radus hreshold, radomzao facor ad samplg rae accordg o he memory, CPU ad baery usage respecvely. Furhermore, we are workg o exesve esg of our prooype realworld suao couco wh releva healhcare professoals ad doma expers order o develop a udersadg of hgh rsk suaos for he moorg of paes ad defyg wha formao s requred from bo-sesors. 0. Refereces [] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Ubquous Daa ream Mg, Curre Research ad Fuure Drecos Workshop Proceedgs held couco 9
10 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces wh The Eghh Pacfc-Asa Coferece o Kowledge Dscovery ad Daa Mg, ydey, Ausrala, [2] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Resource-Aware Mg of Daa reams, Joural of Uversal Compuer cece. (8), 2005, pp [3] M.M. Gaber, A. Zaslavsky ad. Krshaswamy, A Cos-Effce Model for Ubquous Daa ream Mg, Proceedgs of he Teh Ieraoal Coferece o Iformao Processg ad Maageme of Uceray Kowledge-Based ysems, Peruga Ialy, [4] A. Padovz,. Loke, ad A. Zaslavsky, Towards a Theory of Coex paces, I Proceedgs of he 2d IEEE Aual Coferece o Pervasve Compug ad Commucaos, Workshop o Coex Modelg ad Reasog ( CoMoRea ), Orlado, Florda: IEEE Compuer ocey, [5] D. Kosaas, V. Joes, ad R. Buls, MobHealh- Iovave 2.5/3G Moble ervces ad Applcaos for Healhcare, 2007 [6] P. Rubel, J. Fay, G. Nollo, D. Assaell, B. L, L. Reser,. Adam,. Arod, H. Aou, M. Ohlsso, L mo- Chauemps, D. Te lsso, C. Maloss, G. Zla, A. Galass, L. Edebrad, ad P. Chevaler, Toward Persoal ehealh Cardology: Resuls from he EPI-MEDIC Telemedce Proec, Joural of Elecrocardology 2005, 38: pp [7] K. Laerhove, L. Bey, J. Ng J.,. Themarus, R. Kg,. Kwa, H. Gellerse, M. loma, O. Wells, P. Needham, N. Peers, A. Darz, C. Toumazou, G. Yag, "Medcal healhcare moorg wh wearable ad mplaable sesors", UbHealh 2004: The 2d Ieraoal Workshop o Ubquous Compug for Pervasve Healhcare Applcaosm, [8] P. Ledekkers, ad V. Gay, Persoal Hear Moorg ad Rehablao ysem usg mar Phoes, I Proceedgs of he Ieraoal Coferece o Moble Busess (ICMB'05), [9] P. Varkar,. gh, Y. Lee, Coex-Aware Daa Mg Framework for Wreless Medcal Applcao, Lecure Noes Compuer cece (LNC), Volume 2736, prger-verlag. IBN , 2003, pp [0] B. Jase, ad R. Deklerek, Coex-Aware Iacvy Recogo for Vsual Fall Deeco, I Pervasve Healh Coferece ad Workshops, [] Km, H., ad Jo, H. A Coex-Aware Traveler Healhcare ervce (TH) ysem, I Pervasve Healh Coferece ad Workshops. November-December [2] M. Becker, E. Werkma, M. Aasasopoulos, ad T. Kleberger, Approachg Ambe Iellge Home Care ysems, I Pervasve Healh Coferece ad Workshops, [3] H. Me, I. Wdya, A. Halere ad B. Erfao, A Flexble Val g Represeao Framework for Moble Healhcare, [4] G. Brelecker, H. chuld, ad R. chaz, Hyperdaabases for Peer-o-Peer Daa ream Processg, I Proceedgs of he IEEE Ieraoal Coferece o Web ervces (ICW'04). a Dego, Calfora, [5] C. Che, H. Agrawal, M. Cochwala ad D. Rosebluh, ream query processg for healhcare bosesor applcaos, I Proceedgs of he 20h Ieraoal Coferece o Daa Egeerg (ICDE'04). Boso, MA, UA: IEEE Compuer ocey, [6] A. Padovz, A. Zaslavsky ad. Loke, A Ufyg Model for Represeg ad Reasog Abou Coex uder Uceray, h Ieraoal Coferece o Iformao Processg ad Maageme of Uceray Kowledge- Based ysems (IPMU), Pars, Frace), [7] Jag, J.R., u, C. ad Mzua E., Neuro-Fuzzy ad of Compug: A Compuaoal Approach o Learg ad Mache Iellgece. Prece-Hall: Upper addle Rver, NJ, 997. [8] Bruce, G. Buchaa B.G. ad horlffe, E.D., Rulebased exper sysems : he MYCIN expermes of he aford Heursc Programmg Proec. Readg, Mass: Addso-Wesley, 984. [9] Zmmerma, H., Fuzzy e Theory - ad Is Applcaos. Kluwer Academc Publshers, Norwell, Massachuses, 996. [20] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Oboard Mg of Daa reams esor Neworks, A Book Chaper Advaced Mehods of Kowledge Dscovery from Complex Daa, (Eds.). Badhyopadhyay, U. Maulk, L. Holder ad D. Cook, prger Verlag. [2] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Adapve Mg Techques for Daa reams Usg Algorhm Oupu Graulary, The Ausralasa Daa Mg Workshop (AusDM 2003), Held couco wh he 2003 Cogress o Evoluoary Compuao (CEC 2003), Caberra, Ausrala, prger Verlag, Lecure Noes Compuer cece (LNC), [22] N. Phug, M.M. Gaber ad U. Roehm, Resource-aware Dsrbued Ole Daa Mg for Wreless esor Neworks, Proceedgs of he Ieraoal Workshop o Kowledge Dscovery from Ubquous Daa reams (IWKDUD07), couco wh ECML ad PKDD 2007, Warsaw, Polad, [23] Alve Techologes, hp:// 0
Mobile Data Mining for Intelligent Healthcare Support
Moble Daa Mg for Iellge Healhcare uppor Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble
Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument
Chaper 4 Mulple-Degree-of-Freedom (MDOF Sysems Eamples: Pacg of a srume Number of degrees of freedom Number of masses he sysem X Number of possble ypes of moo of each mass Mehods: Newo s Law ad Lagrage
Trust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs
JOURNAL OF SOFTWARE, VOL. 4, NO. 10, ECEBER 2009 1091 Trus Evaluao ad yamc Roug ecso Based o Fuzzy Theory for ANETs Hogu a, Zhpg Ja ad Zhwe Q School of Compuer Scece ad Techology, Shadog Uversy, Ja, Cha.P.R.
Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters
Joural of ewor ad Compuer Applcaos, vol. 3, o., pp. 32-46, Jauary 2008. Performace Comparsos of oad Balacg Algorhms for I/O- Iesve Worloads o Clusers Xao Q Deparme of Compuer Scece ad Sofware Egeerg Aubur
The Design of a Forecasting Support Models on Demand of Durian for Domestic Markets and Export Markets by Time Series and ANNs.
The 2 d RMUTP Ieraoal Coferece 2010 Page 108 The Desg of a Forecasg Suppor Models o Demad of Dura for Domesc Markes ad Expor Markes by Tme Seres ad ANNs. Udomsr Nohacho, 1* kegpol Ahakor, 2 Kazuyosh Ish,
7.2 Analysis of Three Dimensional Stress and Strain
eco 7. 7. Aalyss of Three Dmesoal ress ad ra The cocep of raco ad sress was roduced ad dscussed Par I.-.5. For he mos par he dscusso was cofed o wo-dmesoal saes of sress. Here he fully hree dmesoal sress
Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION
Yugoslav Joural of Operaos Research 200 umber 77-9 VEHICLE FLEET MAAGEMET: A BAYESIA APPROACH Vladmr PAPI] Jova POPOVI] Faculy of Traspor ad Traffc Egeerg Uversy of Belgrade Belgrade Yugoslava Absrac:
Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold
Ieraoal Joural of Fuure Geerao Coucao ad eworkg Vol. 8, o. 6 (15), pp. 5-14 hp://d.do.org/1.1457/fgc.15.8.6. Aoaly Deeco of ework raffc Based o Predco ad Self-Adapve hreshold Haya Wag Depare of Iforao
A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions
A ew proposal for compug porfolo valuea-rsk for sem-oparamerc dsrbuos Tro-Mauel Ñíguez ad Javer Peroe Absrac Ths paper proposes a sem-oparamerc (SNP) mehodology for compug porfolo value-a-rsk (VaR) ha
REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),
REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT
Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises
Advace Joural of Food Scece ad Techology 9(6): 482-486, 205 ISSN: 2042-4868; e-issn: 2042-4876 Mawell Scefc Orgazao, 205 Submed: Aprl 9, 205 Acceped: Aprl 28, 205 Publshed: Augus 25, 205 Evaluao ad Modelg
American Journal of Business Education September 2009 Volume 2, Number 6
Amerca Joural of Bue Educao Sepember 9 Volume, umber 6 Tme Value Of Moe Ad I Applcao I Corporae Face: A Techcal oe O L Relaohp Bewee Formula Je-Ho Che, Alba Sae Uver, USA ABSTRACT Tme Value of Moe (TVM
Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies
ARICLES ACADÉMIQUES ACADEMIC ARICLES Assuraces e geso des rsques, vol. 79(3-4), ocobre 2011- javer 2012, 251-277 Isurace ad Rsk Maageme, vol. 79(3-4), Ocober 2011- Jauary 2012, 251-277 Professoal Lably
Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function
Research Joural of Mahemacs ad Sascs 3(2): 72-76, 20 ISSN: 2040-7505 Maxwell Scefc Orgazao, 20 Receved: Jauary 08, 20 Acceped: February 03, 20 Publshed: May 25, 20 Provg he ompuer Scece Theory P NP? Wh
Object Tracking Based on Online Classification Boosted by Discriminative Features
Ieraoal Joural of Eergy, Iformao ad Commucaos, pp.9-20 hp://dx.do.org/10.14257/jec.2013.4.6.02 Objec Trackg Based o Ole Classfcao Boosed by Dscrmave Feaures Yehog Che 1 ad Pl Seog Park 2 1 Qlu Uversy of
Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression
Ieraoal Joural of Grd Dsrbuo Compug, pp.53-64 hp://dx.do.org/10.1457/jgdc.014.7.5.05 Facal Tme Seres Forecasg wh Grouped Predcors usg Herarchcal Cluserg ad Suppor Vecor Regresso ZheGao a,b,* ad JajuYag
Jorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM jloa@fciencias.unam.mx
Usg UML Sae Dagrams for Moellg he Performace of Parallel Programs Uso e Dagramas e Esao UML para la Moelacó el Desempeño e Programas Paralelos Jorge Orega Aroa Deparameo e Maemácas, Facula e Cecas, UNAM
Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning
17 Geerag Iellge Teachg Learg Sysems usg Cocep Maps ad Case Based Reasog Makel L. Esposa, MSc. Naala Maríez S. y Zeada García V. Deparme of Compuer Scece, Ceral Uversy of Las Vllas, Hghway o Camajuaí,
Determinants of Foreign Direct Investment in Malaysia: What Matters Most?
Deermas of Foreg Drec Ivesme Maaysa: Wha Maers Mos? Nursuha Shahrud, Zarah Yusof ad NuruHuda Mohd. Saar Ths paper exames he deermas of foreg drec vesme Maaysa from 970-008. The causay ad dyamc reaoshp
Quantifying Environmental Green Index For Fleet Management Model
Proceedgs of he Easer Asa Socey for Trasporao Sudes, Vol.9, 20 Quafyg Evromeal ree Idex For Flee Maageme Model Lay Eg TEOH a, Hoo Lg KHOO b a Deparme of Mahemacal ad Acuaral Sceces, Faculy of Egeerg ad
Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks
Aalss of oalo Formao ad ooperao Sraeges Moble Ad hoc ewors Pero Mchard ad Ref Molva Isu Eurecom 9 Roue des rêes 06904 Sopha-Apols, Frace Absrac. Ths paper focuses o he formal assessme of he properes of
Harmony search algorithms for inventory management problems
Afrca Joural of Busess Maageme Vol.6 (36), pp. 9864-9873, 2 Sepember, 202 Avalable ole a hp://www.academcourals.org/ajbm DOI: 0.5897/AJBM2.54 ISSN 993-8233 202 Academc Jourals Revew Harmoy search algorhms
No Regret Learning in Oligopolies: Cournot vs Bertrand
No Regre Learg Olgopoles: Couro vs Berrad Ur Nadav Georgos Plouras Absrac Couro ad Berrad olgopoles cosue he wo mos prevale models of frm compeo. The aalyss of Nash equlbra each model reveals a uque predco
Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade
Dscusso Paper: 2014-005 Busess School Dscple of Face Dscusso Paper 2014-005 Modellg he crash rsk of he Ausrala Dollar carry rade Suk-Joog Km Uversy of Sydey Busess School Modellg he crash rsk of he Ausrala
55. IWK Internationales Wissenschaftliches Kolloquium International Scientific Colloquium
PROCEEDIGS 55. IWK Ieraoales Wsseschaflches Kolloquu Ieraoal Scefc Colloquu 3-7 Sepeber 00 Crossg Borders wh he BC uoao, Boedcal Egeerg ad Copuer Scece Faculy of Copuer Scece ad uoao www.u-leau.de Hoe
Value of information sharing in marine mutual insurance
Value of formao sharg mare muual surace Kev L, Joh Lu, Ja Ya 3 ad Je M Deparme of Logscs & Marme Sudes, The Hog Kog Polechc Uvers, Hog Kog. Emal address:.x.l@polu.edu.h. Deparme of Logscs & Marme Sudes,
Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function
Avalable a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 9-966 Vol., Issue December ), pp. Prevously, Vol., Issue, pp. 6 6) Applcaos ad Appled Mahemacs: A Ieraoal Joural AAM) Solvg Fuzzy Lear Programmg Problems
s :risk parameter for company size
UNDESTANDING ONLINE TADES: TADING AND EFOMANCE IN COMMON STOCK INVESTMENT Y. C. George L, Y. C. Elea Kag 2 ad Chug-L Chu 3 Deparme of Accoug ad Iformao Techology, Naoal Chug Cheg Uversy, Tawa,.O.C acycl@ccu.edu.w;
IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,
Traditional Smoothing Techniques
Tradoal Smoohg Techques Smple Movg Average: or Ceered Movg Average, assume s odd: 2 ( 2 ( Weghed Movg Average: W W (or, of course, you could se up he W so ha hey smply add o oe. Noe Lear Movg Averages
CONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1
CONVERGENCE AND SPAIAL PAERNS IN LABOR PRODUCIVIY: NONPARAMERIC ESIMAIONS FOR URKEY ugrul emel, Ays asel & Peer J. Alberse Workg Paper 993 Forhcomg he Joural of Regoal Aalyss ad Polcy, 999. We would lke
The Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes
The Ueded Cosequeces of Tor Reform: Re Seeg ew Yor Sae s Srucured Selemes Saues Publshed Joural of Foresc Ecoomcs, Volume 3 o, Wer 2 By Lawrece M. Spzma* Professor of Ecoomcs Mahar Hall Sae Uversy of ew
GARCH Modelling. Theoretical Survey, Model Implementation and
Maser Thess GARCH Modellg Theorecal Survey, Model Imlemeao ad Robusess Aalyss Lars Karlsso Absrac I hs hess we survey GARCH modellg wh secal focus o he fg of GARCH models o facal reur seres The robusess
HIGH FREQUENCY MARKET MAKING
HIGH FREQUENCY MARKET MAKING RENÉ CARMONA AND KEVIN WEBSTER Absrac. Sce hey were auhorzed by he U.S. Secury ad Exchage Commsso 1998, elecroc exchages have boomed, ad by 21 hgh frequecy radg accoued for
A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree
, pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal
Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li
Naural Gas Sorage Valuao A Thess Preseed o The Academc Faculy by Yu L I Paral Fulfllme Of he Requremes for he Degree Maser of Scece he School of Idusral ad Sysem Egeerg Georga Isue of Techology December
Lecture 13 Time Series: Stationarity, AR(p) & MA(q)
RS C - ecure 3 ecure 3 Tme Seres: Saoar AR & MAq Tme Seres: Iroduco I he earl 97 s was dscovered ha smle me seres models erformed beer ha he comlcaed mulvarae he oular 96s macro models FRB-MIT-Pe. See
Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information
JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,
CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING
CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING Q.1 Defie a lease. How does i differ from a hire purchase ad isalme sale? Wha are he cash flow cosequeces of a lease? Illusrae.
DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT
ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa
EQUITY VALUATION USING DCF: A THEORETICAL ANALYSIS OF THE LONG TERM HYPOTHESES
Ivesme Maaeme ad Facal Iovaos Volume 4 Issue 007 9 EQUIY VALUAION USING DCF: A HEOREICAL ANALYSIS OF HE LONG ERM HYPOHESES Luco Cassa * Adrea Pla ** Slvo Vsmara *** Absrac hs paper maches he sesvy aalyss
PORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4
PORTFOLIO CHOIC WITH HAVY TAILD DISTRIBUTIONS Sveloar Rachev Isabella Huber 3 Sergo Orobell 4 We are graeful o Boryaa Racheva-Joova Soya Soyaov ad Almra Bglova for he comuaoal aalyss ad helful commes.
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
A quantization tree method for pricing and hedging multi-dimensional American options
A quazao ree mehod for prcg ad hedgg mul-dmesoal Amerca opos Vlad BALLY Glles PAGÈS Jacques PRINTEMS Absrac We prese here he quazao mehod whch s well-adaped for he prcg ad hedgg of Amerca opos o a baske
ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data
ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
APPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND
FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND by Wachareepor Chaimogkol Naioal Isiue of Developme Admiisraio, Bagkok, Thailad Email: wachare@as.ida.ac.h ad Chuaip Tasahi Kig Mogku's Isiue of Techology
15. Basic Index Number Theory
5. Basc Idex Numer Theory A. Iroduco The aswer o he queso wha s he Mea of a gve se of magudes cao geeral e foud, uless here s gve also he ojec for he sake of whch a mea value s requred. There are as may
Maintenance Scheduling of Distribution System with Optimal Economy and Reliability
Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,
Bullwhip Effect Measure When Supply Chain Demand is Forecasting
J. Basic. Appl. Sci. Res., (4)47-43, 01 01, TexRoad Publicaio ISSN 090-4304 Joural of Basic ad Applied Scieific Research www.exroad.com Bullwhip Effec Measure Whe Supply Chai emad is Forecasig Ayub Rahimzadeh
Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees
Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces b Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces Bref Table of Coes Chaper. Iroduco PART I - LINEAR MODELS Chaper.
Automated Event Registration System in Corporation
teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee
RUSSIAN ROULETTE AND PARTICLE SPLITTING
RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate
An Effectiveness of Integrated Portfolio in Bancassurance
A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the
Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets
We-Hsu Kuo Asa e Pacfc al./asa Maageme Pacfc Maageme evew (005) evew 0(), (005) 3-3 0(), 3-3 Prce Volaly, Tradg Acvy ad Marke Deph: Evdece from Tawa ad Sgapore Tawa Sock Idex Fuures Markes We-Hsu Kuo a,*,
METHODOLOGY ELECTRICITY, GAS AND WATER DISTRIBUTION INDEX (IDEGA, by its Spanish acronym) (Preliminary version)
MEHODOLOGY ELEY, GAS AND WAE DSBUON NDEX (DEGA, by s Sash acroym) (Prelmary verso) EHNAL SUBDEOAE OPEAONS SUBDEOAE Saago, December 26h, 2007 HDA/GGM/GMA/VM ABLE OF ONENS Pages. roduco 3 2. oceual frameork
Impact of Interference on the GPRS Multislot Link Level Performance
Impact of Iterferece o the GPRS Multslot Lk Level Performace Javer Gozalvez ad Joh Dulop Uversty of Strathclyde - Departmet of Electroc ad Electrcal Egeerg - George St - Glasgow G-XW- Scotlad Ph.: + 8
of the relationship between time and the value of money.
TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp
Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology
I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50
A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time
Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral
Approximate hedging for non linear transaction costs on the volume of traded assets
Noame mauscrp No. wll be sered by he edor Approxmae hedgg for o lear rasaco coss o he volume of raded asses Romuald Ele, Emmauel Lépee Absrac Ths paper s dedcaed o he replcao of a covex coge clam hs a
Markit iboxx USD Liquid Leveraged Loan Index
Mark Boxx USD Lqud Leveraged Loa Idex Sepember 20 Mark Boxx USD Leveraged Loa Idex Idex Gude Coe Overvew... 4 Seleco Crera... 5 Idex Icepo/Rebalacg... 5 Elgbly Crera... 5 Loa Type... 5 Mmum facly ze...
Numerical Methods with MS Excel
TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how
Fuzzy Forecasting Applications on Supply Chains
WSEAS TANSACTINS o SYSTEMS Haa Toza Fuzzy Forecag Applcao o Supply Cha HAKAN TZAN ZALP VAYVAY eparme of Idural Egeerg Turh Naval Academy 3494 Tuzla / Iabul TUKIYE hoza@dhoedur Abrac: - emad forecag; whch
Average Price Ratios
Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or
Valuation Methods of a Life Insurance Company
Valuao Mehods of a Lfe Isurace Comay ISORY...3 2 PRODUC ASSESSMEN : PROFI ESING...4 2. E PROFI ESING IN 3 SEPS...5 2.. Equalece Prcle...5 2..2 radoal Marg...6 2..3 Prof esg...6 2.2 COMMON CRIERIA O EVALUAE
Critical Approach of the Valuation Methods of a Life Insurance Company under the Traditional European Statutory View
Crcal Aroach of he Valuao Mehods of a Lfe Isurace Comay uder he radoal Euroea Sauory Vew Dr. Paul-Aoe Darbellay ParerRe Belleresrasse 36 C-8034 Zürch Swzerlad Phoe: 4 385 34 63 Fa: 4 385 37 04 E-mal: aulaoe.darbellay@arerre.com
Classic Problems at a Glance using the TVM Solver
C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the
Claims Reserving When There Are Negative Values in the Runoff Triangle
Clams Reservg Whe There Are Negave Values he Ruo Tragle Erque de Alba ITAM Meco ad Uversy o Waerloo Caada 7 h. Acuaral Research Coerece The Uversy o Waerloo Augus 7-0 00 . INTRODUCTION The may uceraes
The Term Structure of Interest Rates
The Term Srucure of Ieres Raes Wha is i? The relaioship amog ieres raes over differe imehorizos, as viewed from oday, = 0. A cocep closely relaed o his: The Yield Curve Plos he effecive aual yield agais
Report 52 Fixed Maturity EUR Industrial Bond Funds
Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:
Load and Resistance Factor Design (LRFD)
53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo
EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26
EXAMLE... A. Edowme... B. ure edowme d Term surce... 4 C. Reseres... 8. Bruo premum d reseres... EXAMLE 2... 4 A. Whoe fe... 4 B. Reseres of Whoe fe... 6 C. Bruo Whoe fe... 7 EXAMLE 3... 8 A.ure edowme...
Performance Center Overview. Performance Center Overview 1
Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener
TIME-VARYING RISK PREMIUM IN LARGE CROSS-SECTIONAL EQUITY DATASETS
IME-VARYING RISK PREMIUM IN LARGE CROSS-SECIONAL EQUIY DAASES Parck Gaglard a, Elsa Ossola ad Olver Scalle c * Frs draf: Decemer 2 hs verso: Novemer 2 Asrac We develop a ecoomerc mehodology o fer he pah
1. The Time Value of Money
Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg
A Parallel Transmission Remote Backup System
2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College
How To Calculate Backup From A Backup From An Oal To A Daa
6 IJCSNS Inernaonal Journal of Compuer Scence and Nework Secury, VOL.4 No.7, July 04 Mahemacal Model of Daa Backup and Recovery Karel Burda The Faculy of Elecrcal Engneerng and Communcaon Brno Unversy
CHAPTER 2. Time Value of Money 6-1
CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show
An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information
A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author
Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes
Covero of No-Lear Stregth Evelope to Geeralzed Hoek-Brow Evelope Itroducto The power curve crtero commoly ued lmt-equlbrum lope tablty aaly to defe a o-lear tregth evelope (relatohp betwee hear tre, τ,
Efficient Traceback of DoS Attacks using Small Worlds in MANET
Effcet Traceback of DoS Attacks usg Small Worlds MANET Yog Km, Vshal Sakhla, Ahmed Helmy Departmet. of Electrcal Egeerg, Uversty of Souther Calfora, U.S.A {yogkm, sakhla, helmy}@ceg.usc.edu Abstract Moble
The Economics of Administering Import Quotas with Licenses-on-Demand
The Ecoomcs of Admserg Impor uoas wh Lceses-o-Demad Jaa Hraaova, James Falk ad Harry de Gorer Prepared for he World Bak s Agrculural Trade Group Jauary 2003 Absrac Ths paper exames he effecs of raog mpor
User-credibility Based Service Reputation Management for Service Selection
Ieraoa Coferece o Copuer Scece ad Servce Sye (CSSS 4) Uer-credby Baed Servce Repuao Maagee for Servce Seeco Cao Jux, Dog Y, Q Y, u Bo, Dog Fag, Zhou Tao Schoo of Copuer Scece ad Egeerg Key aboraory of
The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets
The Icreasg arcpao of Cha he Word Soybea Marke ad Is Ipac o rce Lkages Fuures Markes by Mara Ace Móz Chrsofoe Rodofo Margao da Sva ad Fabo Maos Suggesed cao fora: Chrsofoe M. A. R. Sva ad F. Maos. 202.
An IG-RS-SVM classifier for analyzing reviews of E-commerce product
Iteratoal Coferece o Iformato Techology ad Maagemet Iovato (ICITMI 205) A IG-RS-SVM classfer for aalyzg revews of E-commerce product Jaju Ye a, Hua Re b ad Hagxa Zhou c * College of Iformato Egeerg, Cha
Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center
200 IEEE 3rd Iteratoal Coferece o Cloud Computg Dyamc Provsog Modelg for Vrtualzed Mult-ter Applcatos Cloud Data Ceter Jg B 3 Zhlag Zhu 2 Ruxog Ta 3 Qgbo Wag 3 School of Iformato Scece ad Egeerg College
An Architecture to Support Distributed Data Mining Services in E-Commerce Environments
An Archecure o Suppor Dsrbued Daa Mnng Servces n E-Commerce Envronmens S. Krshnaswamy 1, A. Zaslavsky 1, S.W. Loke 2 School of Compuer Scence & Sofware Engneerng, Monash Unversy 1 900 Dandenong Road, Caulfeld
Spline. Computer Graphics. B-splines. B-Splines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II
Lecure 4 Curves and Surfaces II Splne A long flexble srps of meal used by drafspersons o lay ou he surfaces of arplanes, cars and shps Ducks weghs aached o he splnes were used o pull he splne n dfferen
The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk
The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet
On formula to compute primes and the n th prime
Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal
Optimal Packetization Interval for VoIP Applications Over IEEE 802.16 Networks
Optmal Packetzato Iterval for VoIP Applcatos Over IEEE 802.16 Networks Sheha Perera Harsha Srsea Krzysztof Pawlkowsk Departmet of Electrcal & Computer Egeerg Uversty of Caterbury New Zealad sheha@elec.caterbury.ac.z
The impact of service-oriented architecture on the scheduling algorithm in cloud computing
Iteratoal Research Joural of Appled ad Basc Sceces 2015 Avalable ole at www.rjabs.com ISSN 2251-838X / Vol, 9 (3): 387-392 Scece Explorer Publcatos The mpact of servce-oreted archtecture o the schedulg
Green Master based on MapReduce Cluster
Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of
Study on prediction of network security situation based on fuzzy neutral network
Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(6):00-06 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Study o predcto of etwork securty stuato based o fuzzy eutral etwork
Smart Money? The Forecasting Ability of CFTC Large Traders. by Dwight R. Sanders, Scott H. Irwin, and Robert Merrin
Smar Moey? The Forecasg Ably of CFTC Large Traders by Dwgh R. Saders, Sco H. Irw, ad Rober Merr Suggesed cao forma: Saders, D. R., S. H. Irw, ad R. Merr. 2007. Smar Moey? The Forecasg Ably of CFTC Large
A MATHEMATICAL MODEL OF THE HUMAN THERMAL SYSTEM
A MAHEMAICAL MODEL OF HE HUMA HERMAL SYSEM A hess Subed o he Graduae School of Egeerg ad Sceces of zr Isue of echology aral Fullfle of he Requrees for he Degree of MASER OF SCIECE Mechacal Egeerg by Eda
An Anti-spam Filter Combination Framework for Text-and-Image Emails through Incremental Learning
An An-spam Fler Combnaon Framework for Tex-and-Image Emals hrough Incremenal Learnng 1 Byungk Byun, 1 Chn-Hu Lee, 2 Seve Webb, 2 Danesh Iran, and 2 Calon Pu 1 School of Elecrcal & Compuer Engr. Georga
6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis
6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces
INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT
IJSM, Volume, Number, 0 ISSN: 555-4 INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT SPONSORED BY: Angelo Sae Unversy San Angelo, Texas, USA www.angelo.edu Managng Edors: Professor Alan S. Khade, Ph.D. Calforna