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An improved co-training approach for document Sentiment classification
Jawad Khan(자와드 칸),Aftab Alam(아프타 발람),Muhammad Numan Khan(무함마드 누만 칸),Irfan Ullah(이르판 울라),Muhammad Umair(무하마드 우매르),Umair Qudus(구두스 우매르),Tariq Habib Afridi(타리크 하비브 아프리디),Sung Soo Park(박성수),Young-Koo Lee( 한국정보과학회 2020 한국정보과학회 학술발표논문집 Vol.2020 No.7
Hybrid Indoor Position Estimation using K-NN and MinMax
( Fazli Subhan ),( Shakeel Ahmed ),( Sajjad Haider ),( Sajid Saleem ),( Asfandyar Khan ),( Salman Ahmed ),( Muhammad Numan ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9
Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.