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https://hdl.handle.net/11421/19428
2024-03-29T01:07:56ZA comparative study on machine learning algorithms for indoor positioning
https://hdl.handle.net/11421/20017
A comparative study on machine learning algorithms for indoor positioning
Bozkurt, Sinem; Elibol, G.; Günal, Serkan; Yayan, Uğur
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature, machine learning algorithms have widespread usage in estimating positions. One of the main problems in indoor positioning systems is to find out appropriate machine learning algorithm. In this paper, selected machine learning algorithms are compared in terms of positioning accuracy and computation time. In the experiments, UJIIndoorLoc indoor positioning database is used. Experimental results reveal that k-Nearest Neighbor (k-NN) algorithm is the most suitable one during the positioning. Additionally, ensemble algorithms such as AdaBoost and Bagging are applied to improve the decision tree classifier performance nearly same as k-NN that is resulted as the best classifier for indoor positioning
International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2015 -- 2 August 2015 through 4 August 2015 -- -- 118180
2015-01-01T00:00:00ZArtificial intelligence aided recommendation based mobile trip planner for Eskisehir city
https://hdl.handle.net/11421/20016
Artificial intelligence aided recommendation based mobile trip planner for Eskisehir city
Aydın, Ahmet; Telçeken, Sedat
Recent years have seen a proliferation of applications aimed for the mobile users. Although there are some mobile trip planning applications available for big cities such Istanbul, they lack some important features that would be necessary for the best trip quality. This study proposes a new mobile trip planner for real-time navigation developed for Eskisehir city, Turkey (30 points of interests and more than 150 sub places). Given the current GPS location of the traveler and their preferences, the mobile trip planner allows finding a route which minimizes the total travelling time while optimizing the visiting time for each point of interest. The proposed application also gives some recommendations to the travelers which helps them to re-plan their route interactively. The details of the route computation algorithms and their test results are discussed and evaluated
10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 -- 15 June 2015 through 17 June 2015 -- -- 118313
2015-01-01T00:00:00ZThe comparison of A* algorithm and Ant Colonial Optimization for mobile traveler application [Mobil Gezgin Uygulamasi için A* Algoritmasi ve Karinca Kolonisi Optimizasyonu Karşilaştirilmasi]
https://hdl.handle.net/11421/20020
The comparison of A* algorithm and Ant Colonial Optimization for mobile traveler application [Mobil Gezgin Uygulamasi için A* Algoritmasi ve Karinca Kolonisi Optimizasyonu Karşilaştirilmasi]
Aydın, Ahmet; Telçeken, Sedat
In this paper, a route planning application has been developed for mobile devices. The goal was to select the algorithm that computes the route in the shortest amount of time. To this end, a database containing information and high-resolution pictures of the touristic locations in Eskisehir province have been created. For each interest point in the database, optimal routes have been computed using such complete search algorithms as A* and Ant Colonial Optimization found in the literature. The experimental work has shown that A* algorithm runs up to 80% faster than the Ant Colonial Optimization as the number of interest points increases
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052
2015-01-01T00:00:00ZClassifier selection for RF based indoor positioning [RF Temelli Iç Ortam Konumlama için Siniflandirici Seçimi]
https://hdl.handle.net/11421/20022
Classifier selection for RF based indoor positioning [RF Temelli Iç Ortam Konumlama için Siniflandirici Seçimi]
Bozkurt, Sinem; Günal, Serkan; Yayan, Uğur; Bayar, V.
The selection of appropriate classifier is of great importance in improving the positioning accuracy and processing time for indoor positioning. In this work, an extensive analysis is carried out to determine the most appropriate classification algorithm to solve the indoor positioning problem. KIOS Research Center dataset is used in the experimental work. Principal Component Analysis method is employed together with Ranker method to determine the best features. In the next stage, the performances of Naïve Bayes, Bayesian Network, Multilayer Perceptron, K-Nearest Neighbor and J48 Decision Tree, which are widely preferred classification algorithms for indoor positioning studies, are analyzed on four distinct mobile phones. The results of the analysis reveal that J48 Decision Tree is superior to the other classification algorithms in terms of both processing time and accuracy
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052
2015-01-01T00:00:00Z