Neural Networks

Patan, Krzysztof

doi:10.1007/978-3-030-11869-3_2

Cited by 3 publications

(1 citation statement)

References 80 publications

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“…A neural network (NN) is an interconnected assembly of simple processing elements, whose functionality is inspired by the human neuron. The processing ability of the network rely in the inter-unit connection strengths obtained by process of adaptation by learning from a set of training patterns, modelled after the human brain to recognise patterns in a given set of data [24,26].…”

Section: Neural Networkmentioning

confidence: 99%

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Hernández¹,

Nugent²,

McChesney³

et al. 2019

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Neural networks are a popular type of algorithm for human activity monitoring which can build intelligent systems from labelled data in an automated fashion. Obtaining accurately labelled data is costly; it requires time and effort, which can be cumbersome because it interrupts the user activity stream. In conjunction with the ubiquitous presence of embedded technology, neural networks present new research opportunities for human activity monitoring in smart home environments.OBJECTIVES: We propose a human activity classification method that requires a limited amount of labelled data, which consists of a concatenation method for classifying human activities built upon the fusion of neural network activation functions. METHODS: Our methodology builds a neural network model that receives the sensor data through the input layer to then distribute it among the different vertical hidden layers, which implement different activation functions simultaneously. Next a hidden layer combines activation functions by utilising a concatenation method. Finally, the neural network provides classes to the unlabelled sensing data. We conducted an evaluation utilising an open-access dataset. We compared the activity recognition accuracy of our approach utilising 25%, 50%, and 75% of labelled data against a conventional shallow neural network trained with the 100% of labelled data available. RESULTS: Results show an improvement in the accuracy of the activity classification regardless of the portion of labelled data available. It was observed that the highest achieved accuracy when using 25% of activation function fusion data outperformed results compared to when using 100% of labelled data in a conventional shallow network (i.e., increase in accuracy of 2.7%, 3.7%, 4.8%, and 0.9% across the activity recognition of four subjects). CONCLUSION: The approach proposed showed an improvement in the accuracy of classifying human activity when a limited amount of labelled data is available.

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Section: Neural Networkmentioning

confidence: 99%

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Hernández¹,

Nugent²,

McChesney³

et al. 2019

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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Neural networks and behaviour based control for education botanical robot navigation

Burhanuddin

Supriyadi

Malik

2020

J. Phys.: Conf. Ser.

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The development of robots began when the military needed it as war equipment, and then it was used by some industries to develop production until now widely used for education and agriculture. Educational robots are usually more universal and simpler than industrial or military robots because robots for education are made only for simulations or prototypes. In this study, the authors surveyed prototypes of Educational botany robots, namely robots used to distinguish fruit maturity. In this journal, the behaviour-based control (BBC) algorithm will continue implemented into mobile robots. The movement of the mobile robot prepared in advance, and then actually the mobile robot is wheeled compared to the desired path. Besides, both kinematic and dynamic smelling mobile robots are derived and considered. In this study, only focused on mobile robots three-wheel differential drive, which will explore in a circular and straight path. In this journal, it can be found that image processing techniques can be used to determine the maturity of watermelons which are shown in different average values in each image obtained by the camera. The best models are generated by layer 32 hidden with an accuracy value of 87% at the training dataset level 60:10:30. The Behaviour Based Control Method has directional movements. But there was an error in each test so that I couldn’t take the watermelon correctly. The error rate reaches 25% from 40 tests or about ten failures in the form of deviating from the object point.

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National Vaccination and Local Intervention Impacts on COVID-19 Cases

et al. 2021

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COVID-19, as a global pandemic, has spread across Indonesia. Jakarta, as the capital of Indonesia, is the province with the most positive cases. The government has issued various guidelines, both at the central and regional levels. Since it began in 2021, the planned new measures, called ‘Pemberlakuan Pembatasan Kegiatan Masyarakat Darurat’, or PPKM emergency public activity restrictions, began with the possibility that the number of active cases might decrease. Accordingly, global vaccinations were also carried out, as they were in Indonesia. However, the first phase prioritized frontline health workers and high-risk elderly people. This study conducted a causal impact analysis to determine the effectiveness of PPKM in Jakarta and its vaccination program against the increase in daily new cases. Based on this test, PPKM showed a significant effect on the addition of daily new cases and recovered cases. Conversely, the vaccination program only had a significant impact on recovered cases. A forecast of the COVID-19 cases was conducted and indicated that the daily new cases showed a negative trend, although it fluctuated for the next 7 days, while death and recovered cases continued to increase. Hence, it can be said that the vaccination program has still not shown its effectiveness in decreasing the number of daily new cases while PPKM is quite effective in suppressing new cases.

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Neural Networks

Cited by 3 publications

References 80 publications

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Neural networks and behaviour based control for education botanical robot navigation

National Vaccination and Local Intervention Impacts on COVID-19 Cases

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