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A framework for mining internet-of-things to solve complex problems / Mohammed Anas Haroun ; Supervised Amr Ahmed Badr , Sherif Essam Eldin Khattab

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohammed Anas Haroun , 2017Description: 103 Leaves : charts ; 30cmOther title:
  • إطار للتنق{u٠٦أأ}ب في انترنت الاش{u٠٦أأ}اء من اجل حل المسائل المعقدة [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science Summary: One of Internet of things (IoT) big opportunities is the huge data that can be collected. As human heuristics have been shown to improve performance of complex problems and deliver more accurate results, IoT data can be used to extract these heuristics. In this thesis we present a framework to capture human heuristics from taxi drivers using data collected from sensors deployed in the taxis. The captured heuristics are used to create initial chromosomes for a genetic algorithm to solve the Travelling Salesman Problem (TSP). A dataset collected from 10,357 taxis in Beijing for 18 months was used. The quality of the TSP solutions was improved by up to 49% as compared to the same genetic algorithm with randomly initialized chromosomes. The results show a promising potential for augmenting heuristic search algorithms by data collected from the IoT
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Mo.F (Browse shelf(Opens below)) Not for loan 01010110076614000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Mo.F (Browse shelf(Opens below)) 76614.CD Not for loan 01020110076614000

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

One of Internet of things (IoT) big opportunities is the huge data that can be collected. As human heuristics have been shown to improve performance of complex problems and deliver more accurate results, IoT data can be used to extract these heuristics. In this thesis we present a framework to capture human heuristics from taxi drivers using data collected from sensors deployed in the taxis. The captured heuristics are used to create initial chromosomes for a genetic algorithm to solve the Travelling Salesman Problem (TSP). A dataset collected from 10,357 taxis in Beijing for 18 months was used. The quality of the TSP solutions was improved by up to 49% as compared to the same genetic algorithm with randomly initialized chromosomes. The results show a promising potential for augmenting heuristic search algorithms by data collected from the IoT

Issued also as CD

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