BURA Collection:http://bura.brunel.ac.uk/handle/2438/2102024-03-29T02:15:30Z2024-03-29T02:15:30ZSAABM: A framework for combining sentiment analysis and agent-based modelling for dynamic marketplace analysisDaroge, Habibahttp://bura.brunel.ac.uk/handle/2438/282282024-02-07T03:01:02Z2023-01-01T00:00:00ZTitle: SAABM: A framework for combining sentiment analysis and agent-based modelling for dynamic marketplace analysis
Authors: Daroge, Habiba
Abstract: The evolution of social media as a digital platform to share consumers experiences of purchasing products has gained a considerable amount of attention from researchers and businesses in recent years. However, the accumulation of large volumes of textual data has presented some challenges in analyzing such unstructured data and capturing complex social phenomena. In light of companies utilizing online platforms to sell their products, the concept of dynamic pricing has been prevalent as a pricing strategy in which the price of a product is continuously adjusted in response to changing market conditions and demand patterns. This element of complexity at which the pace of price fluctuations has evolved over time has resulted in creating advanced models for dynamic marketplace analysis and can be used to understand consumer behaviour and enhance competitiveness for companies. The intersection of dynamic pricing and sentiment analysis presents a unique opportunity to investigate the influence of consumer sentiment on pricing strategies and decision-making processes.
This thesis proposes the Sentiment Analysis and Agent-Based Modelling (SAABM) framework which implements a combination approach of sentiment analysis and Agent-Based Modelling (ABM) to model behavioural complexity. Sentiment analysis and topic modelling is applied in this study based on a case study approach of 100 trainers products including Nike, Puma and Timberland, sourced from Amazon UK consumer reviews. Key insights which were extracted from the data include developing wordclouds for positive, negative and neutral sentiment and applying topic modelling to list the top 10 common topics that were being discussed among consumers about a particular trainers product. Correlation analysis was performed to determine whether there is a correlation between sentiment and price which resulted in a positive correlation for 6 Nike products, 11 Puma products and 11 Timberland products. This exploratory data analysis was used to create an agent-based model to observe interactions between consumers and an Amazon UK seller in a simulation environment.
By incorporating the Bass model (Bass, 1969), coefficient of innovation and social influence were included to investigate what-if scenarios. Visualizations were created to examine how consumers react in the following what-if scenarios: high sentiment and high price, low sentiment and low price, neutral sentiment and neutral price. In addition, to test the robustness of the model, parameter sweeping was implemented which indicated a faster rate of adoption in a smaller market size, increasing the number of innovators increases the social influence diffusion rate and the higher the coefficient of innovation and social influence values result in a higher rate of adoption.
The key contribution of this study is the SAABM framework which is evaluated through the case study approach. One of the findings of the SAABM framework is the integration of sentiment and price variables which influences the adoption threshold of adopting trainers products in a simulated dynamic marketplace environment. Moreover, it provides data-driven insights to be extracted which can aid in data-driven decision making (DDD) to better understand consumer behaviour and to model complex, heterogeneous systems to observe emergent social interactions.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZInvestigating the factors affecting smart transportation mobile applications adoption in the sultanate of OmanAl-Bulushi, Hanahttp://bura.brunel.ac.uk/handle/2438/281192024-01-30T17:38:28Z2023-01-01T00:00:00ZTitle: Investigating the factors affecting smart transportation mobile applications adoption in the sultanate of Oman
Authors: Al-Bulushi, Hana
Abstract: The transportation condition in developing countries generally and in Oman particularly is
characterized by inadequate public transportation, Inefficient transportation modes and
Limited access to clean transportation. This condition has resulted in profound challenges in
the transportation sector. One of the leading transportation problems that have existed for a
long time is traffic congestion. This further caused an increment in the travelling costs, waiting
time for passengers, and late arrivals for work, schools, and businesses. Also, most of the
central areas in cities face a high demand for parking slots in which drivers spend a reasonable
amount of time searching for parking space which can be compensated by money. Moreover,
transportation decisions directly influence land use by reducing open areas such as parks and
wildlife. The implementation of the new smart transportation technologies, including smart
mobile applications, in addressing these urban transportation challenges through offering
better traffic management, enabling automatic fee collections, ensuring safe driving, reducing
trips via private cars, and providing cost-effective and simply accessible flexible transportation
modes contributes to enhancing the cities’ ecological condition, presenting a healthier style of
living.
Smart transportation services and smart mobile applications implementation involves troves
of smart technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big
data. Therefore, the public adoption and acceptance of smart services and smart mobile
applications in smart transportation are vital to reduce congested traffic and improve quality
of life. Yet, most of these services and applications are relatively new, with limited data
exploring the extent of end user's acceptance of smart city services, especially in developing
countries. Therefore, this thesis explored factors affecting citizens’ acceptance of smart
transportation mobile applications in Oman to enhance the successful implementation of these
smart applications.
The majority of technology acceptance models have been developed and evaluated in
developed countries. It would be imprudent to assume that these frameworks can be
universally applicable among all nations, particularly in developing countries. Therefore, the
latest UTAUT2 model was used to develop a model which was further extended and expanded
in two ways to better address the developing nation context such as Oman. Firstly, an in-depth
literature review on the smart city, smart transportation and technology acceptance studies
revealed two new constructs: trust and satisfaction. Later, interviews with the smart
transportation services providers also introduced two unique variables: awareness and former
practice. A quantitative study was conducted on 383 Omani citizens for model validation.
The finding indicates that only social influence, habit, and former practice directly influence
Omani citizens’ behavioural intentions to adopt smart transportation mobile applications in the
Omani context. The results represent a valuable contribution and a sign of progress for the
literature on Information Technology acceptance, smart cities, and smart transportation. Also,
provide recommendations for smart city services providers to improve the acceptance of smart
transportation mobile applications.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZExploring the topology of progressive disease dataSajjadi, Seyed Erfanhttp://bura.brunel.ac.uk/handle/2438/277042023-11-23T19:13:19Z2023-01-01T00:00:00ZTitle: Exploring the topology of progressive disease data
Authors: Sajjadi, Seyed Erfan
Abstract: This thesis aims to investigate the crucial objective of improving the comprehension of clinical data structure, acknowledging its increasing importance. We initiate our investigation by exploring the historical context of topological data analysis, a fundamental approach that facilitates the extraction of the inherent topological structure of data. This methodology reveals discrete segments within the dataset, wherein specific segments may indicate the presence of diseases in their initial stages, while other segments may correspond to different subtypes of advanced diseases. The identification of areas has significant significance for clinicians as it enables a deeper understanding of patients' symptoms within the disease topology and facilitates the implementation of personalised treatments.
In the following section, we will go into the domain of Pseudo Time techniques, which enable the creation of temporal models from non-temporal cross-sectional data. These approaches provide useful insights by deducing temporal aspects of diseases. Nevertheless, the effectiveness of these methods relies heavily on the selection of suitable distance measures and labelling schemes that may effectively direct the process of trajectory modelling. The utilisation of clinical staging data, namely the categorisation of patients into "early stage" and "advanced stage," plays a crucial role in limiting the potential biases of pseudo-time models, hence guaranteeing the accurate representation of disease progression patterns.
The advancement of our inquiry involves the use of two separate methodologies in constructing temporal phenotypes using data topology analysis: topological data analysis and pseudo time-series. Using data on type 2 diabetes, we give evidence that topological data analysis can effectively identify trajectories that reflect various temporal phenotypes. Additionally, we show that pseudo-time series analysis can be used to infer a state space model that exhibits transitions between hidden states, each representing discrete temporal abnormalities. Significantly, both approaches emphasise the importance of lipid profiles in identifying these symptoms.
Our research presents the innovative TDA-PTS algorithm, which combines pseudo temporal and topological data analysis. The efficacy of the combined method is assessed on three different datasets, namely simulated data, diabetic data, and genomic data. This evaluation demonstrates how the system effectively identifies unique temporal phenotypes in each disease by considering various trajectories throughout the progression of the disease.
Moreover, we explore the use of clinical staging data in order to construct robust and realistic trajectories. In this study, we utilise simulated data to showcase the accuracy attained in estimating the fundamental transition parameters using limited pseudo time approaches, which effectively mitigate the occurrence of unrealistic transitions. In the context of breast cancer pseudo time models, the trajectories are constrained by using the uniformity of cell size as a proxy of disease staging. This constraint leads to the development of models that more accurately depict the progressive increase in symptoms over time.
Finally, we employ these techniques to actual glaucoma data, therefore confirming the efficacy of the algorithm in accurately representing the advancement and categorisation of the condition. This study provides a thorough examination of illness dynamics within clinical datasets, presenting information in a chronological order that spans from background information to methods and outcomes. The findings of this research make a substantial contribution to the field, enhancing our comprehension and modelling of disease dynamics.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZAnomaly detection for IoT networks using machine learningAbdulla, Husainhttp://bura.brunel.ac.uk/handle/2438/272722023-09-30T07:16:03Z2023-01-01T00:00:00ZTitle: Anomaly detection for IoT networks using machine learning
Authors: Abdulla, Husain
Abstract: The Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning.
This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets.
This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%.
This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00Z