Location recognition system using random forest

Lee, Sunmin; Moon, Nammee

doi:10.1007/s12652-018-0679-5

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…In the method input vector is inserted in each of the trees in the forest for prophesying a distinct object from an input vector with the result of prediction from each tree. Simultaneously, the method avoids errors of bias and variance by random choosing of input-predictor variables with the application of various subsets of the related training dataset (Ren et al 2019;Lee and Moon 2018). Classification problem involves the forest, ascertaining the classification with the highest votes above total trees in the forest (Zhao et al 2019).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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“…In this paper, the similarity between users is obtained for the feature, location, and MUR of the place. The similarity for user u is calculated by using Pearson's correlation coefficients as shown in equation (4). The similarity value ranges from -1 to 1.…”

Section: Predictormentioning

confidence: 99%

“…Studies using a variety of data mining techniques-such as Classification, Association Rule, and Clustering-are currently being conducted [1,2]. Although they primarily used purchase data, more diverse types of data such as location data (GPS, Wifi, and Bluetooth) and Social Network Service (SNS) data (likable information and data relating to social networks), sensor data (eye tracking and temperature data) are also available [3,4,5]. Moreover, they are widely adopted in diverse field such as business, genetics, transportation, and distribution.…”

Section: Introductionmentioning

confidence: 99%

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Kim¹,

Moon²

2019

KSII TIIS

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Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

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“…Lee et al created an IPS where the basic service set identity (BSSID) has also been learned along with the RSS record to classify indoor locations. The classification was performed using an ensemble random forest (ERF) method [ 29 ]. A similar type RSS classification has been performed in [ 30 ] where several popular classifiers are evaluated and the best five classifiers are taken and integrated to implement an IPS application.…”

Section: Associated Work On Ipsmentioning

confidence: 99%

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Rana

Prieto

Dey

et al. 2018

Sensors

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Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

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Location recognition system using random forest

Cited by 19 publications

References 3 publications

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

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