Fuzzy Transfer Learning

The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data

Fuzzy Transfer Learning: Methodology and application

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Producing a methodology that is able to predict output using a model is a well studied area in Computational Intelligence (CI). However, a number of real-world applications require a model but have little or no data available of the specific environment. Predominantly, standard machine learning approaches focus on a need for training data for such models to come from the same domain as the target task. Such restrictions can severely reduce the data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model such environments. It is on this particular problem that this paper is focussed. In this paper two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By applying a TL approach through the combining of labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data

Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks

Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approac

Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study

Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented

Historical Data Trend Analysis in Extended Reality Education Field

The arrival of the digital age brings Virtual Reality, Augmented Reality, and Mixed Reality technologies into our daily life. It provides a brand-new user experience to composite with real environments. Due to the development of related devices in recent years, the highly interactive connections between users and devices have gradually evolved. The paper starts from a literature review to discuss Virtual Reality, Augmented Reality, and Mixed Reality's history and social impact. The review reveals not only the traditional historical review but also contains a data research study. The research focuses on the case study paper, which proposed a bright, interactive future with technology in educational field. We compared the proposed future view and the current development. This paper collected 269 citations from 2005 to 2020 and analyzed them, assessing whether they belonged to technical or theoretical paper. The paper uses the collected data to discuss industrial developing trends and indicates the possible future view based on the data study result

Virtual Reality Research: Design Virtual Education System for Epidemic (COVID-19) Knowledge to Public

open access articleAdvances in information and communication technologies have created a range of new products and services for the well-being of society. Virtual Reality (VR) technology has shown enormous potential in educational, commercial, and medical fields. The recent COVID-19 outbreak highlights a poor global performance in communicating epidemic knowledge to the public. Considering the potential of VR, the research starts from analyzing how to use VR technology to improve public education in COVID-19. The research uses Virtual Storytelling Technology (VST) to promote enthusiasm in user participation. A Plot-based VR education system is proposed in order to provide an immersive, explorative, educational experiences. The system includes three primary modules: the Tutorial Module, the Preparation Module, and the Investigation Module. To remove any potential confusion in the user, the research aims to avoid extremely complicated medical professional content and uses interactive, entertainment methods to improve user participation. In order to evaluate the performance efficiency of the system, we conducted performance evaluations and a user study with 80 participants. Compared with traditional education, the experimental results show that the VR education system can used as an effective educational tool for epidemic (COVID-19) fundamental knowledge. The VR technology can assist government agencies and public organizations to increase public understanding of the spread the epidemic (COVID-19

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

What Do We See: An Investigation Into the Representation of Disability in Video Games

There has been a large body of research focused on the representation of gender in video games. Disproportionately, there has been very little research in respect to the representation of disability. This research was aimed at examining the representation of disabled characters through a method of content analysis of trailers combined with a survey of video gamers. The overall results showed that disabled characters were under-represented in videogames trailers, and respondents to the survey viewed disabled characters as the least represented group. Both methods of research concluded that the representation of disabled characters was low. Additionally, the characters represented were predominantly secondary, non-playable characters not primary. However, the research found that the defined character type was a mixture of protagonists and antagonists, bucking the standard view of disabled characters in video games

Towards fuzzy transfer learning for intelligent environments

By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. The use of a FuzzyTL algorithm allows for a source of labelled data to improve the learning of an alternative context task. We will demonstrate the application of an Fuzzy Inference System (FIS) to produce a model from a source Intelligent Environment (IE) which can provide the knowledge for a differing target context. We will investigate the use of FuzzyTL within differing contextual distributions through the use of temporal and spatial alternative domains