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Discovery of Ranking Fraud for Mobile Apps-IEEE PROJECT 2015-2016

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Discovery of Ranking Fraud for Mobile Apps-IEEE PROJECT 2015-2016

  1. 1. miccansinfotech +91 90036 28940 +91 94435 11725 ‘ 'l'—t‘-Ii: -i‘ §Z(II'-III'—IIIII'—'~lIi | =I= I; "i0II= «IIl OI‘-I'l‘-lIII, IIIII'-«III I-I 'IIII'l'-II~‘IIII= «J~‘——= I-lilo)-1-‘ li‘I! II: . IE1‘, III-, All-"lI: I‘I! I:, -. IrI-‘J-'1Vi'I_: I_: I_IIj-Ii‘. T _3.'1_-I‘-II -I-—w-—II-I, -i-—imhiIA'l: pm Bl! ‘ LTEIPINPIIPI ": I'- i'iMIi. :I, =‘ DI-‘ll i‘I-'I= ‘ 'Ll'II . I! IIll, |'-'1li'i. I: Ii‘Ii‘I_= Il~ i'ilI I: (IIIi‘IIi‘| ! §i'i| §I! ,|l. |l§! I($I; I&lII'I~V i'Il, l'I:7£li‘| I ', -ll i'iI,1.': I:I: (:= Ii‘Il’f~‘. 'i'II: I=IIf-I~"I-'~i‘I: "i'leI: I:, -1.-1i‘I= II IIi'ili‘| ,=II T M'1=I '. fl| IIII: _-inli-1-M: 1H-'~-Ill-Irttr l'III’, '5TI‘I-‘IPIIIPIIIEFIEI§llII'IIII= I~‘ |3I? I,=1'v -I. -I, -In-t». m-i: ..sir-Ilzi-I-In-I-1. 1~‘I3.I; .,l Z. =.. .=I1.= i.i'iI: -I?2:L. '2IVlIL. i'il": ,.i'i. ..l; I'2:L. i'i. .r': II| IL. .i'iI;1:. IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED DISCOVERY OF RANKING FRAUD FOR MOBILE APPS ABSTRACT: Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in, the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i. e. ranking based evidences, rating miccmsinfotech, NO: 8 . 100 FEET ROAD. PONDlCHERRY. WWW. M|CAN S| NFOTECH. COM ; MICANSINFOTECH@GMAlL. COM +91 90036 28940; +91 94435 11725
  2. 2. miccinsinfotech +91 90036 28940 +91 94435 11725 ' ‘I-—r= irL~ -i‘ 9Z(II'-«III'—IIIII'—<~'IIi | =I= I; "i0II= «III iI: I'l= lIII,0IIII'-«III I-I 'lIII'l'-I$. ‘IIII'~I~‘——= I-Ila)-1* li‘l! II: . Il»"I*, II: -, . ‘l| l-"lliléllll, -. iri-‘I-‘'i'I, =I. =I. |I. =Ii‘. 9:1,-i-. u -I-—w-—II-I, -i-—is~uIri! :.'l: . mi N! ‘ iTk‘| ,.li(‘lI, 'I ~'I: lv’A i'iI: Ii. :I, =‘ DI-‘ll i‘I-'I= ' 'Ll‘II . '2.luII, I'-'1Ii'i. I: Ii‘Ii‘L= I:~ i'iIl Z: (IIIi'IIi‘I! .= Ii'i| ,=I1II, Il. =I! :(lI; IaIII'2~‘i i'iI! ’I:7:1i‘| I ', -.1! i'iI,1.': r:I: (:-Ii‘Il'f: . IIlIIi': I,**. -<| ‘|= "I'I”iI: , -1.-mm IIi'iIi‘I, =tr M'l= I '. i,lIIII= _IIlIII‘-IOU-' : ?,4-'«-Ilt= m1»- I'IlI', 'iT‘l: ‘ | :Ii‘| :uII; (f1:I _IIlII'JItI= I~‘ I, =I1=l'v -I. -I. -III-r-. m-in. pit-Il= i-I-In-I-1. I~‘I5.IL. ,l Z3.. =l43.i'iliI72:L. ’2IVllL. i'il?7:. .i'i. ..ll'2:L. i'i. .?': ll! IL, .i'il;1:. IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through statistical hypotheses tests. In, addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of thejdetection algorithm as well as some regularity of ranking fraud activities. EXISTING SYSTEM: The number of mobile Apps has grown at a huge rate over the past few years. To stimulate the development of mobile Apps, many App stores launched daily App leaderboards, which demonstrate the chart rankings of most popular Apps. A higher rank on the leaderboard usually leads to a huge number of downloads and million dollars in revenue. instead of ‘relying on traditional marketing solutions, shady App developers resort to some fraudulent means to deliberately boost their Apps and eventually miccmsinfotech, NO:8.100 FEET ROAD. PONDlCHERRY. WWW. M|CAN S| NFOTECH. COM ; M| CANSlNFOTECH@GMAlL. COM +91 90036 28940; +91 94435 11725
  3. 3. miccansinfotech +91 90036 28940 +91 94435 11725 ' ‘I-—r= irL~ -I‘ 2-3:1-I-4|! -—ari-I-—~Ir« I11; "i0II= «III iI: I'l= IIII,0IIII'-«III I-I 'lIII'l'-I$. ‘IIII'~I~‘——= I-lilo)-1-T li‘l! II: . II»"I*, II: -, . ‘l| l-"lliléllll, -. iri-‘I-‘'i'I, =I. =I. |l. =Ii‘. T 9:t, -i-. i- -I-—w-—II-I, -i-—is~uIri! :.'l: . pm i‘I,1‘ iTk‘| ,.I: (‘lI, 'I ”: l?- i'iI: Ii. :I, =‘ DI-‘ll i‘I-'I= ' 'Ll‘II . ’2.luII, I'-'1Ii'i. 1:Ii‘Ii‘L= I:~ i'iII 2:(IIIi'IIi‘I! 1i'i| §I1Il! l1| : (9I; IaIII'2:i i'iL1'I:7:Ii‘| I ', -.1! i'il,1I: r:I: (:-Ii‘Il'f: . wI: I=II; l~'1:~i‘I: t-'. 'm: I:, II. '1i‘I1I IIi'iIi‘I,1r T emu '. i,lIIIII: _IIlIII‘-I-IL‘ : k4-'«-IIt= m1»- I'III', 'iT‘l: ‘ | :Ii‘| :uII; (f1:I _IIlII'IIII= I~‘ I,1,1,= 1* -I. -I. -III-r-. m-in. pit-Il= i-I-In-I-1. I~‘I5.I'. ._1Z3.113.i'ilil72:L. ’2IVllL. i'il? ':. .i'i. ..ll'2:L. i'i. .r': lI! IL. .i'il;7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED manipulate the chart rankings on an App store. This is usually implemented by using so- called “bot farms” or “human water armies” to inflate the App downloads, ratings and reviews in a very short time. DISADVANTAGE OF EXISTING SYSTEM: > When an App was promoted with the help of ranking manipulation it could be top in leaderboard and more new users could be purchased that product. > affect other App reputation. PROPOSED SYSTEM: In proposed system, we propose to develop a ranking fraud detection system for mobile Apps. Ranking fraud does not always happen in the whole life cycle of an App, so we need to detect the time when fraud happens. Indeed, our careful observation reveals that mobile Apps are not always ranked high in the leaderboard, but only in some leading miccmsinfotech, NO:8.100 FEET ROAD. PONDlCHERRY. WININ. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAIL. COM +91 90036 28940; +91 94435 11725
  4. 4. miccinsinfotech +91 90036 28940 +91 94435 11725 ' ‘I-—r= irL~ -I‘ 9Z(II'-«III'—lIIII'—'~'IIi I11; "i0II= «III iI: I'l= IIII,0IIII'-«III I-I 'lIII'l'-I$. ‘IIII'~I~‘——= I-lilo)-1-T li‘l! II: . Il»"I*, II: -, . ‘l| l-"lliléllll, -. iri-‘I-‘'i'I; I;I. |l; Ii‘. 9:t, -i-. i- -I-—w-—II-I, -i-—is~uIri! :.'l: . pm i‘l; I‘ iTk‘| ,.I: (‘lI, 'I ”: l?- i'iI: Ii. :I; ‘ DI-‘ll i‘I-'I= ' 'Ll‘II . '2.luII, I'-'1Ii'i. I: Ii‘Ii‘I; I:~ i'iIl 2:(IIIi'IIi‘I! .1i'i| ,=I,1,Il, Il.1,| :(9I; IaIII'2~‘i i'iI1’I:7:Ii‘| I all i'iI,1I: r:I: (:-Ii‘Il'f: . wI: I=IIl; l~‘i: ~i‘I: t-'. 'm: I:, II. '1i‘I1I IIi'iIi‘I,1r M'l= I '. i,lIIII= _IIlIII‘-I-IL‘ : k4-'«-IIt= m1»- I'IlI', 'iT‘l: ‘ | :Ii‘| :uII; (f1:I _IllII'IIII= I~‘ I; I,1,= 1* -I. -I. -III-r-. m-in. pit-Il= i-I-In-I-1. I~‘I5.IL. ,1Z3.; l13.i'il; l72:L. ’2IVllL. i'il?7:. .i'i. ..l; ('2:L. i'i. .r': lI! IL. .i'il;7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED events, which form different leading sessions. In other words, ranking fraud usually happens in these leading sessions. Specifically, we first propose a simple yet effective algorithm to identify the leading sessions of each App based on its historical ranking records. Then, with the analysis of Apps’ ranking behaviors, we find that the fraudulent Apps often have different ranking patterns in each leading session compared with normal Apps. ADVANTAGE OF PROPOSED SYSTEM: > Detect fraud ranking in daily App leaderboards. > Avoid ranking manipulation. MODULES: A module is a part/ .,of aprogram. Programs are composed of one or more independently developed modules that are not combined until the program is linked. A single module can contain one or several routines. miccmsinfotech, NO: 8 . 100 FEET ROAD. PONDlCHERRY. WININ. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAlL. COM +91 90036 28940; +91 94435 11725
  5. 5. miccansinfotech +91 90036 28940 +91 94435 11725 ' 'l'—t‘-Ii: -i‘ 3-3:1-I-ail! -—iri-I-—-«Ira I11; 7‘i0II= «III iI: I'l= «IIII, IIIII'-«III I-i 'lIII'l'-II~‘IIII'~I~‘——= I-Ila)-1-T li‘l! II: . IL11, II: -, All-"II: Ié1!l£, -. iri-‘I-1'i'L= I;I, II: -Ii‘. 9:t, -i-. i- -I‘-1'1‘-II'l_Il‘-IIWIIILI! .'I3. pm i‘l; I‘ iIk‘I, .II(‘lI, 'I ?1:l: - i'iI: I1.: I;‘ DI-‘ll i‘I-1:‘ ‘LL11 . 'I! IIII, I'-1Ii'i. I: li‘Ii‘I_1l~ i'iII I: (|IIi‘IIi‘| ! §i'i| §I1|I. |l§! I(9l; IfllII'I~‘1 i'tI, l'I: l£Ii‘| I ', -.11 i'iI,1.': I:I: (i= Ii‘II’f~‘. 'i1lI: I1I; l~"I-‘~i‘I1"i'lII: k, Ii. -1i‘I11 IIi'ili‘I11 swai '. i,lIIII= _IIlIII‘-I-IL‘ : k4-'«-iltamic I'III', 'iI‘l: ‘ | :Ii‘I: uII; (f1:1 _IllII'II-I= I~‘ I; I,1,= 1': -I. -I. -III-ram-iii. pit-Ila: -I-In-I-1. I~15.I'. ._1Z3.; l13.i'il; l72:L. ’2IVllL. i'il? ':. .i'i. ..ll'2:L. i'i. .:': lI! IL. .i'il:7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED Our project modules are given below: > Identifying leading sessions > Ranking based evidences > Rating based evidences > Review based evidences > Evidence aggregation IDENTIFYING LEADING SESSIONS: Ranking fraud usually happens in leading sessions Therefore, detecting ranking fraud of mobile Apps is actually to detect ranking fraud within leading sessions of mobile Apps. Specifically, we first propose a simple yet effective algorithm to identify the leading sessions of each App based on its historical ranking records. Then, with the analysis of Apps’ ranking ‘behaviors, we find that the fraudulent Apps often have different ranking patterns in each leading session compared with normal Apps. miccmsinfotech, N0: 8 . 100 FEET ROAD. PONDlCHERRY. WININ. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAIL. COM +91 90036 28940; +91 94435 11725
  6. 6. miccansinfotech +91 90036 28940 +91 94435 11725 ' ‘I-—r= irL~ -i‘ §Z(II'-III‘-lIIII'—'~'II1 I11; "i0II= «III iI: I'l= IIII, IIuI'-«III I-i 'lIII'l'-lI~‘IIII'~I~‘——= I-Ills}-1? li‘l! II: . IL71, II: -, . ‘l| l-"lliléllll, -. iri-‘I-1'i'I; I;I. |l; Ii‘. 9:t, -i-. i- -I-—i'i-—iI-i, -i-aimlri! :.'l: . pm i‘l; I‘ iIk‘I, .II(‘lI, 'I ?1:l: - i'iI: I1.: I;‘ DI-‘ll i‘I-1:‘ ‘LL11 . '2.loiII, I'-'1Ii'i. I: Ii‘Ii‘I; I:~ i'iIl 2:(IIIi'IIi‘I! .1i'i| ,=I,1,Il, Il.1,| :(sI; IsIII'2~‘i i'iL1’I:7:Ii‘| i ', -.11, i'iI,1.': r:I: (:-ii‘Il'f: . ViiII: I=IIl; l~‘i: ~Mat-'. 'I: i:I: , -i. -Iiilai Iii'iIi‘I,11 swai '. i,lIIII= _IIlIII‘-I-IL‘ : k4-'«-iltamic I'III', 'iI‘l: ‘ | :Ii‘| :uII; (f1:1 _IllII'II-I= I~‘ I; I,1,= 1': -I. -I. -III-ram-iii. pit-Ila: -I-In-I-1. I~15.I'. .,1Z3.; l13.i'il; l72:L. ’2IVllL. i'il?7:. .i'i. ..ll'2:L. i'i. .:': lI! IL. .i'il:7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED Mining Leading Sessions: There are two main steps for mining leading sessions. First, we need to discover leading events from the App’s historical, ranking records. Second, we need to merge adjacent leading events for constructing leading sessions. RANKING BASED EVIDENCES: A leading session is composed of severalfleading events. Therefore, we should first analyze the basic characteristics of leading events for extracting fraud evidences. By analyzing the Apps’ historical ranking records, we observe that Apps’ ranking behaviors in a leading event always satisfy a specific ranking pattern, which consists of three different ranking phases, namely, rising phase, maintaining phase and recession phase. Specifically, in each leading event, an App’s ranking first increases to a peak position in the leaderboard (i. e., rising phase), then keeps such peak position for a period (i. e., maintaining phase), and finally decreases till the end of the event (i. e., recession phase). miccmsinfotech, NO: 8 . 100 FEET ROAD. PONDlCHERRY. WININ. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAIL. COM +91 90036 28940; +91 94435 11725
  7. 7. miccansinfotech +91 90036 28940 +91 94435 11725 ' 'l'—t‘-Ii: -i‘ 3-3:1-I-ail! -—iri-I-—-«Ira I11; 7‘i0II= «III OI; -I'l= «IIII, IIIII'-«III I-i 'IIII'l'-II~‘IIII'-J~‘——= I-Ills}-1-T li‘l! II: . IL11, II: -, All-"II: Ié1!l£, -. iri-‘I-1‘i'L= I;I, II: -Ii‘. 9:t, -i-. i- -I‘-1'1‘-II'l_Il‘-IIWIIILI! .'I3. pm i‘l; I‘ iII‘I, .II(‘lI, 'I ?1:l: - i'iI: I1.: I;‘ DI-‘ll i‘I-1:‘ ‘LL11 . 'I! IIII, I'-1Ii‘i. I: li‘Ii‘I_1l~ i'iII I: (|I‘i‘IIi‘| ! §i'i| §I1|I. |l§! I(9l; IfllII'I~‘1 i'tI, l'I: l£Ii‘| I ', -.11 i'iI,1I: I:I: (i= Ii‘Il’f~‘. ‘i‘lI: I1I; I~"I-‘~i‘I1"i‘lII: k, Ii. -1i‘I11 IIi'ili‘I11 swai '. i,lIIII= _IIlIII‘-I-IL‘ : k4-'«-iltamic I'III', 'iI‘l: ‘ | :Ii‘I: uII; (f1:1 _IllII'IIII= I~‘ I; I,1,= 1': -I. -I. -Ill-ram-iii. pit-Ilai-I-In-I-1. I~13.I'. ._1Z3.; l13.i'il; l72:L. ’2I‘llL. i'il? ':. .i'i. ..l; (’2:L. i'i. .:': lI! IL. .i'il:7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED RATING BASED EVIDENCES: The ranking based evidences are useful for ranking fraud detection. However, sometimes, it is not sufficient to only use ranking based evidences. Specifically, after an App has been published, it can be rated by any user who downloaded it. Indeed, user rating is one of the most important features of App advertisement. An App which has higher rating may attract more users to download and can also be ranked higher in the leaderboard. Thus, rating manipulation is also an important perspective of ranking fraud. Intuitively, if an App has ranking fraud in a leading session s, the ratings during the time period of s may have anomaly patterns compared with its historical ratings, which can be used for constructing rating based evidences. REVIEW BASED EVIDENCES: Besides ratings, most of the App stores also allow users to write some textual comments as App reviews. Such reviews can reflect the personal perceptions and usage experiences of existing users for particular mobile Apps. Indeed, review manipulation is miccmsinfotech, N0: 8 . 100 FEET ROAD. PONDlCHERRY. WININ. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAIL. COM +91 90036 28940; +91 94435 11725
  8. 8. miccansinfotech +91 90036 28940 +91 94435 11725 ' 'l'—t‘-Ii: cl‘ 3-3:1-I-4|! -—m-I-—-«in | =l= l; t'mn=4-i -I-. w:ll-i, -iui-. m 1-) | lIIl'l'-lwIWI'~J~‘——= l-lilo)-1* lt‘l! ll: . ILYI, III-, All-"lI: I‘1!7:, -. |il-‘J-'i'l_: I_= l_Ilj-It‘. _3.'1_-l‘-ll -I-—w-—lI-i, -nimlut! r.'l: . pm M! ‘ i‘t‘| ,'l: (‘"_" ”: l?- i'l7:II. :l, =‘ N-‘ll Bl-'l= ' 'Ll‘ll . 'I! IlIl, I'-'1It'i. I: lt‘Ii‘| !l~ l'lII I: (MIi‘lIt‘| ! §t'l| §I1|I, |I§! I(9l; lfllIl'I~‘l t'tl, l'I:7£lt‘| l ', -/II mI, :.':7:I: (:=1rIl'f: . WI: I=lI_=1~"l-‘~t‘l= t". 'lIl: k, I1.-will Ili'llt‘| ,=Ii * Wm :10 m: _Illofl‘-I-I4: 1H-'~-iIY= til1c mIi: ':m: t | :1.‘I= uI; (:; :I _IllIl'll-I= l~‘ I, =1:, = 1'' -I. -l. -Ill-r-. m-inpit-Ilzi-I-In-I-1, I~‘l3.I'. ._1Z3.iI43.i'ilil72:L. ’2WlL. t'il? ':. .i'i. ..ll’2:L. i'l. .r': |l! lL, .i'lI;7:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED one of the most important perspective of App ranking fraud. Specifically, before downloading or purchasing a new mobile App, users often firstly 5, read its historical reviews to ease their decision making, and a mobile App contains more positive reviews may attract more users to download. Therefore, imposters often post fake reviews in the leading sessions of a specific App in order to inflate the App downloads, and thus propel the App’s ranking position in the leaderboard. Although some previous works on review spam detection have been reported in recent years, the problem of detecting the local anomaly of reviews in the leading sessions and capturing them as evidences for ranking fraud detection are still under-explored. EVIDENCE AGGREGATION: After extracting three types of fraud evidences, the next challenge is how to combine them for ranking fraud detection. Indeed, there are many ranking and evidence aggregation methods in the literature, such as permutation based models, score based models and Dempster-Shafer rules. However, some of these methods focus on learning a miccmsinfotech, NO:8.100 FEET ROAD. PONDlCHERRY. WlNlN. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAlL. COM +91 90036 28940; +91 94435 11725
  9. 9. miccansinfotech +91 90036 28940 +91 94435 11725 ' 'l'—t‘-Ii: cl‘ ,3Z(II'-«llI'-JrlIl'—-‘III | =l= l; ? ‘i0II= «III -I-. w-. II-i, -iui-. m 1-) II-n'I= iwIIII= +u= i-iuo;1- lt‘l! lI: . ILYI, III-, All-"lI: I‘1!I: , -. Iil-‘J-Ii'l_: I_= l_Ilj-It‘. _3.'1_-I‘-ll -I-—w-—II-I, -I-—ium]ut! r.'l: . pm ill! ‘ i‘“. 'l: (‘lI_" ”: I’- i'iMII. :l, =‘ DI-‘ll Bl-'l= ' 'Ll'll . 'I! IIIl, |'-'1It'i. I: li‘li‘| _=Il~ i'iIl I: (I1Ii‘lIt‘| ! §t'l| §l1|l. |l§! I(9l; lfllIl'I~‘l t'tl. l'I:7£lt‘| l ', -JI i'lI,1.': I:I: (i= lt‘ll’f~‘. WI: I=lI, =1~"I-'~t‘l= t". 'lel: k, I1.-will Ili'llt‘| ,=II M'l= I '. i,lHlI= _-IIIII‘-I-IL‘ : k4-'«-tltzmic I'llI’, 'ilt‘I-" | :h‘I: uII; (f1:I gum-n-I= i- I, =l,1,= 1* -I. -I, -In-tam-n. .sir-Itzi-I-In-I-1. 1~‘l3.I'. ._1Z3.il13.i'iliI?2:L. 'iWIL. i'il? ':. .i'l. ..I; '2:L. i'l. .r': Il! IL. .i'lI;1:, IEEE Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — BEST PRICE LOW PRICE GUARANTEED global ranking for all candidates. This is not proper for detecting ranking fraud for new Apps. Other methods are based on supervised learning techniques, which depend on the labeled training data and are hard to be exploited. Instead, we propose an unsupervised approach based on fraud similarity to combine these evidences. SYSTEM REQUIREMENTS: The goal of system requirement specification is to completely specify the technical requirements for the product in a concise and unambiguous manner. HARDWARE REQUIREMENTS: - System : Pentium IV 2.4 GHZ. - Hard Disk : 40 GB. - Floppy Drive 2 1.44 Mb. 0 Monitor : 15 VGA Colour. 0 Mouse : Sony. miccmsinfotech, NO:8.100 FEET ROAD. PONDlCHERRY. WlNIN. M|CAN S| NFOTECH. COM ; M| CANSINFOTECH@GMAIL. COM +91 90036 28940; +91 94435 11725
  10. 10. micansinfotech +91 90036 28940 +91 94435 11725 ‘r 7 Years of Excellence in IEEE Project development for universities across INDIA, USA, UK, AUSTRALIA, and SWEEDEN. Expert developers in . IAVA , DOT NET , ANDROID , PHP, MATLAB , NS2 , NS3 , VLSI , CLOUD SIM, TANNER , MICROWIND , EMBEDDED , ROBOTICS , MECHANICAL , MECHATRONICS , WIRELESS NETWORS, OPNET , OMNET Over 11000+ projects , 425 clients - MICANS IFNFOTECH provides IEEE & application proiects for CSE, |T, ECE, EEE, MECH, ClV| L,MCA, M.TECH, M.PHlLL, MBA, N Projects 100% WORK| N:CODE + DOCUMENTATlON+ EXPLAINATION — LOW PRICE GUARANTEED SOFTWARE REQUIREMENTS: - Operating system : Windows 7. - Coding Language : JAVA - Data Base : MySQL. REFERENCES: [1] Y. Ge, H. Xiong, C. Liu, and A taxi driving fraud detection system. In Proceedings of the 2011 IE onal Conference on Data Mining, ICDM ’1l, pages 181-190, 2011. [2] H. Zhu, E. Chen, . . ao, H. Xiong, and J . Tian. Mining personal context- mlcunsinfotech, NO: 3 , 100 FEET ROAD, PONDlCHERRY. WWWMICANSIN FOTECH. COM ; MICANSINFOTECHQGMAILCOM +91 90036 28940; +91 94435 11725
  11. 11. micclminfotech +91 90036 28940 +91 94435 11725 ‘r 7 Years of Excellence in IEEE Project development for universities across INDIA, USA, UK, AUSTRALIA, and SWEEDEN. Expert developers in JAVA , DOT NET , ANDROID , PHP, MATLAB , NS2 , NS3 , VLSI , CLOUD SIM, TANNER , MICROWIND , EMBEDDED , ROBOTICS , MECHANICAL , MECHATRONICS , WIRELESS NETWORS, OPNET , OMNET Over 11000+ projects , 425 clients - MICANS IFNFOTECH provides IEEE & application proiects for CSE, |T, ECE, EEE, MECH, ClV| L,MCA, M.TECH, M.PHlLL, MBA, I N Projects 100% WORKING CODE + DOCUMENTAT| ON+ EXPLAINATION — LOW PRICE GUARANTEED holistic view. In Proceedings of the 22nd ACM intemation Information and knowledge management, CIKM ’13, 2013. mlccnsinfotech, N0: 3 , 1oo FEET ROAD, PONDlCHERRY. WWWMICANSIN FO'I'ECH. COM ; MICANSINFOTECHQGMAILCOM +91 90036 28940; +91 94435 11725

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    May. 7, 2016

MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHILL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 MICANSINFOTECH@GMAIL.COM WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEEPROJECTSINPONDICHERRY, PROJECTDEVELOPMENTCENTRE

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