Construction of indoor floor plan and localization

Abadleh, Ahmad; Han, Sangyup; Hyun, Soon J.; Liu, Ben; Kim, Myungchul

doi:10.1007/s11276-015-0964-6

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Using a biometric key derived from machine learning models, it is possible to maintain a communication link between senders and receivers (Hamadaqa et al 2019;Mulhem et al 2019;Mars et al 2019). Moreover, the use of machine learning can be applied to indoor localization and distance estimation (Alabadleh et al 2018;Aljaafreh et al 2017;Abadleh et al 2016Abadleh et al , 2017.…”

Section: Related Literaturementioning

confidence: 99%

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

et al. 2022

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One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies.

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Section: Related Literaturementioning

confidence: 99%

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

et al. 2022

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“…Some of the path-loss models have been already studied for indoor environments (Seidel and Rappaport, 1992;So and Lin, 2011;Alikhani et al, 2017;Atia et al, 2012;Zhang et al, 2017), where the power of the transmitters and attenuation of walls and floors have to be known for predicting the pathloss model. However, RSS changes due to the environmental effects leads to inaccurate distance estimations (Torres-Solis et al, 2010;Abadleh et al, 2016). Therefore, authors in Li et al (2018) presented a method that fits the distance between APs and RPs calculated by path-loss formula to the actual distances between them to better estimate the distances based on RSS values.…”

Section: Related Workmentioning

confidence: 99%

Low-cost localisation considering LOS/NLOS impacts in challenging indoor environments

Moghtadaiee

Alikhani

Ghorashi

2020

IJSNET

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One popular indoor localisation method in wireless sensor networks (WSN) and wireless local area networks (WLAN) is fingerprinting technique. However, the training stage of fingerprinting and constructing the radio map is time-consuming and labour-intensive. Here, we propose a novel method to decrease the training cost by building a simulated radio map using an improved pathloss model in which the impacts of line of sight (LOS) and non-light-of-sight (NLOS) propagations are considered. Including LOS/NLOS effects also improves distance estimation. Furthermore, the simulated radio map helps assess and improve the fingerprinting area and network parameters prior to the actual positioning. For performance evaluation, three different radio maps are created using simulation and a test field experiment and then compared based on deterministic and probabilistic algorithms. The results indicate that the improved model outperforms the typical path-loss model and the localisation error gets closer to the actual error of the fingerprinting network.

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“…Biometrics [14]- [22], gene profiling [23], credit card fraud detection [24], [25], face image retrieval [24], contentbased image retrieval [26], [27], disease detection [28]- [32], internet of things [33]- [43], Natural Language Processing [44], [45], network security [46]- [52], image recognition [53]- [58], Anomaly Detection [59]- [69], etc.…”

Section: Introductionmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Review

Tarawneh¹,

Hassanat

Almuhaimeed

2022

IEEE Access

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For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and nine imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

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Construction of indoor floor plan and localization

Cited by 12 publications

References 17 publications

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

Low-cost localisation considering LOS/NLOS impacts in challenging indoor environments

Stop Oversampling for Class Imbalance Learning: A Review

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