Supervised Machine Learning for RSSI based Indoor Localization in IoT Applications

Maduranga, Mwp; Abeysekara, Ruvan

doi:10.5120/ijca2021921305

Cited by 16 publications

(12 citation statements)

References 12 publications

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“…Extensive research on handcrafted and deep learning models has assisted in achieving noise robustness in indoor positioning systems [ 22 ]. With the development of advanced computational devices, the employment of deep neural models and advanced machine learning frameworks has become possible [ 23 , 24 ]. Deep neural networks such as AlexNet, DagNet, GoogleNet, ResNet, InceptionV3, VGG-16, MobileNet, and ZFNet require 2D input data, which is not available in the case of fingerprint base localization [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…These photos were taken in a basic skin cancer clinic from January 1, 2008, to July 13, 2017. A total of 2072 unidentified instances were tested, and the model's results were compared to those of 95 medical professionals who acted as human ratters [3][4][5].…”

Section: Related Workmentioning

confidence: 99%

“…Such a method has employed ANN to classify gender with the help of facial characteristics identified. In contrast, the selection of features has been performed using the Viola-Jones [4] method provided by A Jaswante and others [5], from which accuracy of approximately 98% has been observed. The research team has highlighted the suggested system's efficiency, claiming that it will benefit a real-time system due to its performance.…”

Section: Related Workmentioning

confidence: 99%

“…Bayesian classification [5] The method efficiently manages distinct and constant data by disregarding unsuitable characteristics for both binary and multiclass classifications. Method resourcefully manages distinct and constant data by disregarding unsuitable characteristics for multiclass and binary classifications.…”

Section: Related Workmentioning

confidence: 99%

“…The CNN method does not understand the scale and size of the item. In addition to the question of the space invariance between pixel data, the model requires enormous training for good results Fine-tuned Neural Network [5] The new problem using pre-trained data through initiation and updates is efficient for the Fine-Tune Neural network.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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Hybrid Statistical and Deterministic-Based Pedestrian Tracking Algorithm for Location-Based IoT Applications

Manoj Sithara,

Maduranga

2022

Computer Networks, Big Data and IoT

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Supervised Machine Learning for RSSI based Indoor Localization in IoT Applications

Cited by 16 publications

References 12 publications

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

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

Hybrid Statistical and Deterministic-Based Pedestrian Tracking Algorithm for Location-Based IoT Applications

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