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Crime Intelligence 2.0 : = Reinforci...

University of California, Irvine.

Crime Intelligence 2.0 : = Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Crime Intelligence 2.0 :/
其他題名:	Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing.
作者:	Khanwalkar, Sanket Subhash.
面頁冊數:	1 online resource (84 pages)
附註:	Source: Masters Abstracts International, Volume: 56-01.
Contained By:	Masters Abstracts International56-01(E).
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369174335

Crime Intelligence 2.0 : = Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing.
Khanwalkar, Sanket Subhash.

Crime Intelligence 2.0 :Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing. - 1 online resource (84 pages)

Source: Masters Abstracts International, Volume: 56-01.

Thesis (M.S.)

Includes bibliographical references

Despite heavy investments by local and national governments, crime continues to remain a serious problem in the society. Current state-of-the-art is below average and relies heavily on crime intelligence and efforts of human personnel across several law enforcement agencies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369174335Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Crime Intelligence 2.0 : = Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing.
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245 1 0 $a Crime Intelligence 2.0 : $b Reinforcing Crowdsourcing using Artificial Intelligence and Mobile Computing.
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300 $a 1 online resource (84 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 56-01.
500 $a Adviser: Gloria Mark.
502 $a Thesis (M.S.) $c University of California, Irvine $d 2016.
504 $a Includes bibliographical references
520 $a Despite heavy investments by local and national governments, crime continues to remain a serious problem in the society. Current state-of-the-art is below average and relies heavily on crime intelligence and efforts of human personnel across several law enforcement agencies.
520 $a Over the last few years, crowdsourcing has been promoted worldwide through several Suspicious Activity Reporting (SAR) campaigns. However, such systems have proven to be largely unsuccessful owing to complicated reporting processes, excessive noise, limited authorized personnel, lack of motivation and one-way communication.
520 $a To address these issues and improve counterintelligence, this thesis proposes two approaches: (i) Combination of Machine Learning and Natural Language Processing to promote generic reporting while automating the process of crime detection, summarisation and delegation (ii) Improving crowd intelligence by enabling better citizen involvement through newer information sharing techniques, sophisticated sensors and responsive feedback. In this thesis, we also investigate lone wolf terrorism as a special case of crime, and evaluate application of our proposed methods for its mitigation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Computer science. $3 573171
650 4 $a Criminology. $3 563146
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0984
690 $a 0627
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Irvine. $b Information and Computer Science. $3 1180304
773 0 $t Masters Abstracts International $g 56-01(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10162644 $z click for full text (PQDT)

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