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

Subhan, Fazlı; Saleem, Sajid; Bari, Haseeb; Khan, Wazir Zada; Hakak, Saqib; Ahmad, Shafiq; El-Sherbeeny, Ahmed M.

doi:10.3390/su122410627

Cited by 15 publications

(10 citation statements)

References 23 publications

(28 reference statements)

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“…The reduced feature set may be readily observed and processed using various machine learning methods such HMM while preserving as much data as practicable. Other discriminant analysis methods that have been adopted with HMM include LDA or FDA [ 30 ] and DMD [ 31 ]. Moreover, the PCA is a multivariate method with low computing complexity for reducing the dimensionality of a huge dataset by transforming variables to smaller components while keeping the information in the larger dataset [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System

Babalola

Balyan

2022

Sensors

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Visible light communication (VLC) channel quality depends on line-of-sight (LoS) transmission, which cannot guarantee continuous transmission due to interruptions caused by blockage and user mobility. Thus, integrating VLC with radio frequency (RF) such asWireless Fidelity (WiFi), provides good quality of experience (QoE) to users. A vertical handover (VHO) scheme that optimizes both the cost of switching and dwelling time of the hybrid VLC–WiFi system is required since blockage on VLC LoS usually occurs for a short period. Hence, an automated VHO algorithm for the VLC–WiFi system based on the hidden Markov model (HMM) is developed in this article. The proposed VHO prediction scheme utilizes the channel characterization of the networks, specifically, the measured received signal strength (RSS) values at different locations. Effective RSS are extracted from the huge datasets using principal component analysis (PCA), which is adopted with HMM, and thus reducing the computational complexity of the model. In comparison with state-of-the-art VHO handover prediction methods, the proposed HMM-based VHO scheme accurately obtains the most likely next assigned access point (AP) by selecting an appropriate time window. The results show a high VHO prediction accuracy and reduced mixed absolute percentage error performance. In addition, the results indicate that the proposed algorithm improves the dwell time on a network and reduces the number of handover events as compared to the threshold-based, fuzzy-controller, and neural network VHO prediction schemes. Thus, it reduces the ping-pong effects associated with the VHO in the heterogeneous VLC–WiFi network.

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Section: Methodsmentioning

confidence: 99%

Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System

Babalola

Balyan

2022

Sensors

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“…Another common field of application of discriminant analysis is that of consumer behavior [17], where an individual's behavior can be calculated probabilistically to a particular product or service, depending on the state of the explanatory variables that define a particular attitude. This method can also be used for other economic research such as bankruptcy risk analysis [18], but also in other fields such as agriculture [17], physics [19], engineering [20], medicine [21,22], biology [22], genetics [22], ecology [17,23], etc.…”

Section: The Proposed Methodology For the Studied Problemmentioning

confidence: 99%

The multicriteria analysis, a useful tool in classifying counties from a socio-economic point of view and investments made in water management: a case study on Romania

Iordache¹,

Vasile²,

Pascu³

2022

RJEEC

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The article presents a classification methodology of regional development in Romania (41 counties and Bucharest) using a set of data registered in 2019 by the National Institute of Statistics. Several indicators cover information related to the population (the migration, permanent residence, and education), local investments, private turnover, and investments in water supply and sewerage systems, including waste management. After applying the statistical tests, the separation of the counties was done in three classes, the delimitation indicating a high percentage of 59.5% of regions that have low values of the selected indicators. These regions require major investments that will lead to rising living standards, and the emergence of new jobs, driving down the average age of the population. 21.4% of the regions present an average level of the analyzed indicators while only 19% registered the highest values of the parameters, indicating urban agglomerations and a high level of industrial development.

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“…Recently, this technique has been used for indoor positioning or localisation systems for the purpose of obtaining superior and higher accuracy [125]. The performance of LDA in the construction of data using independent variables is directly proportional to the number of data patterns [125]. However, its performance is yet to be confirmed in the context of nonlinearity [126].…”

Section: G Application Of Deep Learning Techniquesmentioning

confidence: 99%

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

2022

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The advent of modern remote sensors alongside the development of advanced parallel computing has significantly transformed both the theoretical and real implementation aspects of remote sensing. Several algorithms for detecting objects of interest in remote sensing images and subsequent classification have been devised, and these include template matching based methods, machine learning and knowledge-based methods. Knowledge-driven approaches have received much attention from the remote sensing fraternity. They do, however, face challenges in terms of sensory gap, duality of expression, vagueness and ambiguity, geographic concepts expressed in multiple modes, and semantic gap. This paper aims to review and provide an up-to-date survey on machine learning and knowledge driven approaches towards remote sensing forest image analysis. It is envisaged that this work will assist researchers in coming up with efficient models that accurately detect and classify forest images. There is a mismatch between what domain experts expect from remote sensing data and what remote sensing science produces. Such a mismatch or disparity can be reduced or alleviated by adopting an ontology paradigm methodology. Ontologies should be used to support the future of remote sensing in forest object classification. The paper is presented in five parts: (1) a review of methods used for forest image detection and classification; (2) challenges faced by object detection methods; (3) analysis of segmentation techniques employed; (4) feature extraction and classification; and (5) performance of the state-of-the-art methods employed in forest image detection and classification.

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

Cited by 15 publications

References 23 publications

Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System

Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System

The multicriteria analysis, a useful tool in classifying counties from a socio-economic point of view and investments made in water management: a case study on Romania

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

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