Bluetooth Low Energy (BLE) and Feed Forward Neural Network (FFNN) Based Indoor Positioning for Location-based IoT Applications

Maduranga, Mwp; Abeysekera, Ruvan

doi:10.5815/ijwmt.2022.02.03

Cited by 12 publications

(10 citation statements)

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“…In the FFNN Model, the neuron serves as the eventual constituent of a neural network that takes the inputs, and the input is used in uniting with changeable parameters, covered in more detail in a successive subtopic. Feed-forward neural network is a sub-type of ANN, also a powerful machine learning technique used for regression and classification problems (Maduranga & Abeysekera, 2022). Feed-forward neural network (FFNN) MODEL is a versatile technique for making a good prediction.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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“…In order to increase the accuracy of RSSI-based indoor-location systems, several articles research the use of machine learning and neural networks for this purpose [16,17]. In [18], the authors conducted a study on using BLE beacons and Feed Forward Neural Networks (FFNN) for indoor localization in IoT applications. They trained a FFNN using signal strength values from thirteen BLE iBeacon nodes in an indoor environment.…”

Section: Related Workmentioning

confidence: 99%

Indoor Content Delivery Solution for a Museum Based on BLE Beacons

Verde,

Romero,

Faria

et al. 2023

Sensors

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The digital transformation advancement enables multiple areas to provide modern services to their users. Culture is one of the areas that can benefit from these advances, more specifically museums, by presenting many benefits and the most emergent technologies to the visitors. This paper presents an indoor location system and content delivery solution, based on Bluetooth Low Energy Beacons, that enable visitors to walk freely inside the museum and receive augmented reality content based on the acquired position, which is done using the Received Signal Strength Indicator (RSSI). The solution presented in this paper was created for the Foz Côa Museum in Portugal and was tested in the real environment. A detailed study was carried out to analyze the RSSI under four different scenarios, and detection tests were carried out that allowed us to measure the accuracy of the room identification, which is needed for proper content delivery. Of the 89 positions tested in the four scenarios, 70% of the received signals were correctly received throughout the entire duration of the tests, 20% were received in an intermittent way, 4% were never detected and 6% of unwanted beacons were detected. The signal detection is fundamental for the correct room identification, which was performed with 96% accuracy. Thus, we verified that this technology is suitable for the proposed solution.

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“…It's simpler to use than CNTK because it allows you to focus entirely on the model's logic. It also allows the developer to observe the neural network that has been created [17][18].…”

Section: Tensorflow Librarymentioning

confidence: 99%

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Maduranga¹,

Nandasena²

2022

IJIGSP

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This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

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Bluetooth Low Energy (BLE) and Feed Forward Neural Network (FFNN) Based Indoor Positioning for Location-based IoT Applications

Cited by 12 publications

References 0 publications

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Indoor Content Delivery Solution for a Museum Based on BLE Beacons

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

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