Computing Dissertations and Theses

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    A development framework for software integration projects – case study: Web app Integration with OpenWeather API
    (2023) Thirunahari, Siddartha; Unitec, Te Pūkenga; Te Pūkenga
    RESEARCH QUESTIONS Q1. What are the key stages of the Software Development Life Cycle (SDLC) implementation in software integration projects, and how do they contribute to the successful integration of software systems? Q2. In what ways does the development process of software integrations differ from conventional software development approaches, and what specific considerations are essential for ensuring effective integration? Q3. How can the type of integration in software integration projects be determined, considering factors such as system compatibility, data interchange requirements, and integration architecture? Q4. What approaches and methodologies can be employed to ensure the overall quality of the final software integration product, with specific emphasis on functionality, efficiency, and maintainability, as well as adherence to industry standards and best practices? ABSTRACT Software development has experienced rapid growth and advancement, leading to the adoption of state-of-the-art designs, methods, techniques, and tools to deliver high-quality software solutions. However, proficiency in programming alone is insufficient for ensuring reliable, feasible, cost-effective, and high-quality software products. Developers must consider various aspects of the software development life cycle (SDLC) to enhance software solutions. This includes analytical and critical thinking skills, envisioning real-world business cases, and emphasizing quality assurance through thorough testing. Furthermore, cost-effective software solutions can be achieved through detailed project requirements and scope, outsourcing options, sound project planning, and agile methodologies for handling requirement changes. A gap identified in software development is the lack of a comprehensive and generic integration framework for software systems integration. Such a framework would provide developers with the necessary knowledge and resources to successfully undertake industrial software integration projects and deliver leading solutions. Without proper guidance, developers may face challenges in upskilling themselves in software integration. This research proposes a generic integration development framework to produce and deliver high-quality software integration solutions. The framework offers step-by-step guidance throughout each phase of the SDLC, drawing insights from real-world software integration projects. A case study is conducted to demonstrate the application of the proposed framework in a software integration project. The case study involves the development of a small-scale web application using the ReactJS front-end development framework, integrating with the OpenWeather API to retrieve weather forecasting data. The proposed framework is evaluated through a comparative analysis of selected software quality factors: functionality, efficiency, and maintainability. Functionality encompasses accuracy of data fetched through API requests and API security, efficiency focuses on optimal resource utilization for application performance, and maintainability emphasizes code maintainability for future improvements. The analysis validates the improved reliability achieved by implementing the proposed software integration framework and highlights the quality of the final product compared to the pre-framework implementation. By bridging this research gap and providing a generic integration framework, this research contributes to the advancement of software integration practices. It equips developers with the understanding and insights required to excel in software integration projects, ultimately leading to the development of reliable and high-quality software solutions.
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    A new approach for the prevention of sinkhole attack in mobile wireless sensor networks
    (2023) Alomirah, Yasser Abdulrahman; Unitec, Te Pūkenga; Te Pūkenga
    RESEARCH QUESTIONS Main research question is: Is it possible to prevent a sinkhole attack on MWSN using a combination of Blowfish and RSA algorithms? Then it was broken down into the following questions: Q1: What would be the overhead of using the Blowfish algorithm on MWSN? Q2: What would be the overhead of using the RSA algorithm on MWSN? Q3: What is the impact of the key size on the performance of the Blowfish algorithm? Q4: What is the impact of the block size on the performance of the Blowfish algorithm? Q5: How much is the combination of the RSA and Blowfish algorithms more accurate than a pure Blowfish algorithm in the prevention of Sinkhole attack in MWSN? Nowadays, the usage of wireless sensor networks (WSN) is rapidly growing all over the world. For many applications, these networks are deployed in harsh and unreachable environments. As a result, this type of network is faced with many problems. Energy consumption and security are two main challenges of WSN. There are many types of attacks on these networks, of which one of the most serious is Sinkhole attack. This attack is one of the impersonation attacks through which a malicious node advertises the best route to the base station and misguides its neighbours to encourage them to use that route more frequently. The malicious node can alter the messages or drop them or cause an unnecessary delay before forwarding them to the base station. A subclass of WSN in which the nodes can move and frequently change their locations is called mobile wireless sensor network (MWSN). This type of WSN is more vulnerable to Sinkhole attack as the nodes regularly change their positions and neighbours. In this thesis, a new lightweight encryption-based method is proposed to prevent Sinkhole attack in MWSN. The proposed method is aimed at reducing energy consumption and increasing the accuracy of the prevention of Sinkhole attack simultaneously. A hybrid encryption algorithm is used, which employs the Blowfish algorithm for encrypting messages to ensure data integrity and security of MWSN against Sinkhole attack. The Blowfish algorithm consumes low resources and is very fast, which makes it ideal for MWSN. To ensure a secure sharing of the Blowfish key between sender and receiver, at the beginning of the process, the sender uses the RSA algorithm to encrypt the Blowfish key. The receiver decrypts the message using its built-in private key. The experimental results show that while the proposed method significantly enhance the security of MWSN and effectively prevents Sinkhole attack, it does not impose significant overhead on the routing protocol.
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    New Zealand Stock Market prediction using sentiment analysis
    (2022) Bangar, Varinder Jot; Unitec Institute of Technology
    In this research, I addressed this gap and tried to focus on the New Zealand Stock Exchange (NZX). For the proposed model, I used the financial news only, for the SA and for the stock data I took five of the biggest New Zealand organisations. The reason for choosing these organisations was that the textual data contains more mentions about these companies than others and hence, the sentiment scores can reflect the changes that take place. This research was conducted to provide a picture of how accurately NZX trends can be predicted using DL algorithms with and without taking into considerations the SA. Also, the purpose of this research was to draw a picture of how different NZX is from the rest of the bigger stock markets and if the same tools and techniques apply for the prediction of trends. The findings of this research adds to the knowledge that the similar tools and techniques that are used for bigger stock markets, can also be applied to predict the trends of NZX. It further adds that the length and quality of the textual data available for the sentiment analysis also plays an important role in the accuracy of the prediction model. This research also examines the impact of Covid-19 on the accuracy of the prediction model by including and excluding the timelines of data related to this global pandemic.
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    Critical comparison of statistical and deep learning models applied to the New Zealand Stock Market Index
    (2022) Dassanayake, Wajira; Unitec Institute of Technology
    RESEARCH QUESTIONS 1. What are the critical fundamental determinants of the NZX 50 Index movements? 2. How can effective forecasting models based on HWES and ARIMA methodologies be devised and applied with high precision to the NZX 50 Index prediction? 3. How can an efficient univariate LSTM forecasting model and a multivariate LSTM forecasting model be formulated and applied to forecast the NZX 50 Index movement with a high degree of predictive efficacy? 4. Considering all the models tested in different sample periods and scrutiny processes, is it possible to identify a superior forecasting model? Is the recognised model consistently outperforming other tested models in all the testing procedures? Can the redeveloped models efficiently handle the impact of the COVID-19 pandemic? ABSTRACT Financial markets enable buyers and sellers to trade financial instruments (stocks, bonds, foreign currencies, and derivatives) and improve capital allocation. These markets play a pivotal role in facilitating the interactions between those who seek capital and those who are prepared for capital investments, allowing market participants to transfer risks and stimulate economic growth. Financial time series are inherently dynamic, interdependent, and highly sensitive to many factors. These time series contain deterministic and stochastic characteristics, and many interrelated factors influence them. Accurate predictions of financial time series benefit various market participants to generate wealth through the right trading strategies and other stakeholders to enhance funds. However, due to their inherent complexities, financial time series prediction is considered one of the most challenging problems in data mining. This thesis employs popular and efficient time series prediction models, reformulates them and implements them to analyse stock market index movements. This scientific exploration uses two widely used classical forecasting techniques [Auto-Regressive Integrated Moving Average (ARIMA) and Holt Winter's Exponential Smoothing (HWES)] and efficient deep learning (DL) [Long Short-Term Memory (LSTM)] network. The predictive precision of the reformulated models will be empirically tested using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Four research questions are meticulously examined to close the identified empirical research gaps in the time series prediction models applied to the New Zealand stock market. Once the redesigned models are sufficiently trained, they are implemented as prediction models on selected stock market indices. Several statistical and econometric tests are executed to substantiate my research findings.
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    Identification of queen-less beehives using signal enhancement techniques and neural networks
    (2021) Peng, Rui; Unitec Institute of Technology
    Beekeeping has a long history of about 180 years in New Zealand. The first group of honey bees introduced has expanded to approximately one million registered beehives by now. An increasing number of residents have been associated with beekeeping to process and produce world-famous New Zealand honey products. The honey market competition has become more severe than before due to the slow growth of pollen resources, weather change, and the limited number of experienced apiarists. New Zealand beekeepers have lost approximately 10% of bee colonies every winter since 2015, caused mainly by the queen bee problems. In this situation, apiculture needs the support of new technologies urgently. Since the 1980s, various bee monitoring methods based on processing a wide range of physical signals, including audio, temperature, video, weight, vibration, number of bees, humidity, and O2/CO2 content, have been developed to prove the feasibility of precision beekeeping. Unfortunately, few of these systems have been applied in practice because it can be very complicated to analyse the honey bee signals indicating significant statuses, like queen-less beehives. According to previous studies, monitoring the audio signals generated by a beehive can help us predict nearly all of the crucial statuses of the beehive. Hence, this research proposes to identify queen-less beehives by monitoring the bee audio signals. However, the bee audio signals are always acquired with natural environmental noise, which means the noisy bee signals captured from the beehive must be appropriately filtered to obtain the enhanced signals close to the original signals generated by bees. This thesis proposes an approach to identify queen-less beehives by using audio signal enhancement techniques and neural networks. More particularly, the audio signals corrupted by rain noise are enhanced by Multi-band Spectral Subtraction and Wiener filtering techniques, respectively. The improved Mel-frequency Cepstrum Coefficient (IMFCC) of the enhanced signals are extracted as input features of a Multilayer Perceptron (MLP) model to classify queen-less beehives. The result shows that this approach improves the classification accuracy by around 10% to 29%, depending on the quality of the input signals. It is also reported that the proposed system has a higher classification accuracy and better learning ability than other systems using KNN and SVM algorithms.