scholarly journals CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar ◽  
Ki-Hyung Kim
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Signal Propagation ◽  
Location Based Services ◽  
Radio Propagation ◽  
Propagation Models ◽  
Related Information ◽  
Real Time Applications

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

scholarly journals Wi-Fi Fingerprinting Based Room Level Indoor Localization Framework Using Ensemble Classifiers

2019 ◽  
Vol 38 (1) ◽  
pp. 151-174 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar
Keyword(s):  
Neural Network ◽  
Real World ◽  
Indoor Localization ◽  
Propagation Model ◽  
Radio Propagation ◽  
Ensemble Classifiers ◽  
Propagation Models ◽  
Nearest Neighbours ◽  
Set Up ◽  
Realistic Information

Over the past decennium, Wi-Fi fingerprinting based indoor localization has seized substantial attention. Room level indoor localization can enable numerous applications to increase their diversity by incorporating user location. Real-world commercial scale deployments have not been realized because of difficulty in capturing radio propagation models. In case of fingerprinting based approaches, radio propagation model is implicitly integrated in the gathered fingerprints providing more realistic information as compared to empirical propagation models. We propose ensemble classifiers based indoor localization using Wi-Fi fingerprints for room level prediction. The major advantages of the proposed framework are, ease of training, ease to set up framework providing high room-level accuracy with trifling response time making it viable and appropriate for real time applications. It performs well in comparison with recurrently used ANN (Artificial Neural Network) and kNN (k-Nearest Neighbours) based solutions. Experiments performed showed that on our real-world Wi-Fi fingerprint dataset, our proposed approach achieved 89% accuracy whereas neural network and kNN based best found configurations achieved 85 and 82% accuracy respectively.

scholarly journals Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146395-146407
Author(s):  
Allan Dos S. Braga ◽  
Hugo A. O. Da Cruz ◽  
Leslye E. C. Eras ◽  
Jasmine P. L. Araujo ◽  
Miercio C. A. Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geometric Parameters ◽  
Radio Propagation ◽  
Propagation Models

scholarly journals Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1678 ◽  
Author(s):  
Ahmed H. Salamah ◽  
Mohamed Tamazin ◽  
Maha A. Sharkas ◽  
Mohamed Khedr ◽  
Mohamed Mahmoud
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Indoor Localization ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Indoor Environments ◽  
Learning Approaches ◽  
Positioning Systems ◽  
Learning Techniques ◽  
And Performance

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.

scholarly journals Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

2020 ◽  
Vol 12 (24) ◽  
pp. 10627
Author(s):  
Fazli Subhan ◽  
Sajid Saleem ◽  
Haseeb Bari ◽  
Wazir Zada Khan ◽  
Saqib Hakak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real Time ◽  
Indoor Localization ◽  
Location Based Services ◽  
Object Localization ◽  
Support Vector ◽  
Linear Discriminant ◽  
Time Dynamic

Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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