Massey Research Online - Browsing by Author "Liu T"

Browsing by Author "Liu T"

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A Semi-automatic Diagnosis of Hip Dysplasia on X-Ray Films
(Frontiers Media S.A., 2020-12-17) Yang G; Jiang Y; Liu T; Zhao X; Chang X; Qiu Z; Gao X
Background: Diagnosis of hip joint plays an important role in early screening of hip diseases such as coxarthritis, heterotopic ossification, osteonecrosis of the femoral head, etc. Early detection of hip dysplasia on X-ray films may probably conduce to early treatment of patients, which can help to cure patients or relieve their pain as much as possible. There has been no method or tool for automatic diagnosis of hip dysplasia till now. Results: A semi-automatic method for diagnosis of hip dysplasia is proposed. Considering the complexity of medical imaging, the contour of acetabulum, femoral head, and the upper side of thigh-bone are manually marked. Feature points are extracted according to marked contours. Traditional knowledge-driven diagnostic criteria is abandoned. Instead, a data-driven diagnostic model for hip dysplasia is presented. Angles including CE, sharp, and Tonnis angle which are commonly measured in clinical diagnosis, are automatically obtained. Samples, each of which consists of these three angle values, are used for clustering according to their densities in a descending order. A three-dimensional normal distribution derived from the cluster is built and regarded as the parametric model for diagnosis of hip dysplasia. Experiments on 143 X-ray films including 286 samples (i.e., 143 left and 143 right hip joints) demonstrate the effectiveness of our method. According to the method, a computer-aided diagnosis tool is developed for the convenience of clinicians, which can be downloaded at http://www.bio-nefu.com/HIPindex/. The data used to support the findings of this study are available from the corresponding authors upon request. Conclusions: This data-driven method provides a more objective measurement of the angles. Besides, it provides a new criterion for diagnosis of hip dysplasia other than doctors' experience deriving from knowledge-driven clinical manual, which actually corresponds to very different way for clinical diagnosis of hip dysplasia.
A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data
(MDPI (Basel, Switzerland), 2023-05-09) Dehghan-Shoar MH; Pullanagari RR; Kereszturi G; Orsi AA; Yule IJ; Hanly J; Berger K; Croft H; Liu T; Lu B; Yin D
The increasing number of satellite missions provides vast opportunities for continuous vegetation monitoring, crucial for precision agriculture and environmental sustainability. However, accurately estimating vegetation traits, such as nitrogen concentration (N%), from Landsat 7 (L7), Landsat 8 (L8), and Sentinel-2 (S2) satellite data is challenging due to the diverse sensor configurations and complex atmospheric interactions. To address these limitations, we developed a unified and physically based method that combines a soil–plant–atmosphere radiative transfer (SPART) model with the bottom-of-atmosphere (BOA) spectral bidirectional reflectance distribution function. This approach enables us to assess the effect of rugged terrain, viewing angles, and illumination geometry on the spectral reflectance of multiple sensors. Our methodology involves inverting radiative transfer model variables using numerical optimization to estimate N% and creating a hybrid model. We used Gaussian process regression (GPR) to incorporate the inverted variables into the hybrid model for N% prediction, resulting in a unified approach for N% estimation across different sensors. Our model shows a validation accuracy of 0.35 (RMSE %N), a mean prediction interval width (MPIW) of 0.35, and an R (Formula presented.) of 0.50, using independent data from multiple sensors collected between 2016 and 2019. Our unified method provides a promising solution for estimating N% in vegetation from L7, L8, and S2 satellite data, overcoming the limitations posed by diverse sensor configurations and complex atmospheric interactions.
Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.
(MDPI (Basel, Switzerland), 2021-12-22) Liu T; Sabrina F; Jang-Jaccard J; Xu W; Wei Y
A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.
Clustering by Search in Descending Order and Automatic Find of Density Peaks
(IEEE, 2019-01-01) Liu T; Li H; Zhao X; Liang Q
Clustering by fast search and find of density peaks published on journal Science in 2014 is a density-based clustering technique, which is not only unnecessary to determine the number of clusters in advance, but also able to recognize the clusters of arbitrary shapes. Due to a manual selection of clustering centers on a decision graph, samples which belong to one cluster may be assigned to two or more clusters and vice versa. On assumption that boundary points which keep comparable densities with cluster centers should be regarded as inner points, we make a new method which not only can find all possible clusters automatically but also can combine those with similarities simultaneously to obtain the final clusters. Unlike clustering by fast search and find of density peaks, we only focus on densities with discarding the relative metric which measures the minimum distance between a cluster center and a point with a higher density. Qualitative and quantitative experimental results on sufficient datasets demonstrate the effectiveness of our method.
Completed sample correlations and feature dependency-based unsupervised feature selection
(Springer Science+Business Media, LLC, 2023-04) Liu T; Hu R; Zhu Y
Sample correlations and feature relations are two pieces of information that are needed to be considered in the unsupervised feature selection, as labels are missing to guide model construction. Thus, we design a novel unsupervised feature selection scheme, in this paper, via considering the completed sample correlations and feature dependencies in a unified framework. Specifically, self-representation dependencies and graph construction are conducted to preserve and select the important neighbors for each sample in a comprehensive way. Besides, mutual information and sparse learning are designed to consider the correlations between features and to remove the informative features, respectively. Moreover, various constraints are constructed to automatically obtain the number of important neighbors and to conduct graph partition for the clustering task. Finally, we test the proposed method and verify the effectiveness and the robustness on eight data sets, comparing with nine state-of-the-art approaches with regard to three evaluation metrics for the clustering task.
From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models
(Elsevier B.V., 2024-09-01) McIntosh TR; Susnjak T; Liu T; Watters P; Xu D; Liu D; Nowrozy R; Halgamuge MN
This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks – NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 – for the opportunities, risks, and regulatory compliance when adopting Large Language Models (LLMs), using qualitative content analysis and expert validation. Our analysis, with both LLMs and human experts in the loop, uncovered potential for LLM integration together with inadequacies in LLM risk oversight of those frameworks. Comparative gap analysis has highlighted that the new ISO 42001:2023, specifically designed for Artificial Intelligence (AI) management systems, provided most comprehensive facilitation for LLM opportunities, whereas COBIT 2019 aligned most closely with the European Union AI Act. Nonetheless, our findings suggested that all evaluated frameworks would benefit from enhancements to more effectively and more comprehensively address the multifaceted risks associated with LLMs, indicating a critical and time-sensitive need for their continuous evolution. We propose integrating human-expert-in-the-loop validation processes as crucial for enhancing cybersecurity frameworks to support secure and compliant LLM integration, and discuss implications for the continuous evolution of cybersecurity GRC frameworks to support the secure integration of LLMs.
Harnessing GPT-4 for generation of cybersecurity GRC policies: A focus on ransomware attack mitigation
(Elsevier B.V., 2023-11-01) McIntosh T; Liu T; Susnjak T; Alavizadeh H; Ng A; Nowrozy R; Watters P
This study investigated the potential of Generative Pre-trained Transformers (GPTs), a state-of-the-art large language model, in generating cybersecurity policies to deter and mitigate ransomware attacks that perform data exfiltration. We compared the effectiveness, efficiency, completeness, and ethical compliance of GPT-generated Governance, Risk and Compliance (GRC) policies, with those from established security vendors and government cybersecurity agencies, using game theory, cost-benefit analysis, coverage ratio, and multi-objective optimization. Our findings demonstrated that GPT-generated policies could outperform human-generated policies in certain contexts, particularly when provided with tailored input prompts. To address the limitations of our study, we conducted our analysis with thorough human moderation, tailored input prompts, and the inclusion of legal and ethical experts. Based on these results, we made recommendations for corporates considering the incorporation of GPT in their GRC policy making.
Hierarchical graph learning with convolutional network for brain disease prediction
(Springer Nature, 2024-10-23) Liu T; Liu F; Wan Y; Hu R; Zhu Y; Li L
In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However, the existing methods either ignore the uniqueness of different subjects across the functional connectivities or neglect the commonality of the same disease for the functional connectivity of each subject, resulting in a lack of capacity of capturing a comprehensive functional model. To solve the issues, we develop a hierarchical graph learning with convolutional network that not only considers the unique information of each subject, but also takes the common information across subjects into account. Specifically, the proposed method consists of two structures, one is the individual graph model which selects the representative brain regions by combining each subject feature and its related brain region-based graph. The other is the population graph model to directly conduct classification performance by updating the information of each subject which considers both the subject itself and the nearest neighbours. Experimental results indicate that the proposed method on four real datasets outperforms the state-of-the-art approaches.
Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier
(MDPI (Basel, Switzerland), 2022-06-01) Xu W; Jang-Jaccard J; Liu T; Sabrina F; Kwak J
Existing generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.
Initialization-similarity clustering algorithm
(Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu X
Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.
Joint Spectral Clustering based on Optimal Graph and Feature Selection
(Springer Nature Switzerland AG, 2021-02) Zhu J; Jang-Jaccard J; Liu T; Zhou J
Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.
Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.
(Elsevier Ltd, 2022-01) Hu R; Gan J; Zhu X; Liu T; Shi X
In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.
Non-negative Matrix Factorization: A Survey
(Oxford University Press on behalf of the British Computer Society, 2021-07-01) Gan J; Liu T; Li L; Zhang J
Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.
Parameter-Free Extreme Learning Machine for Imbalanced Classification Authors Li, L - China Agric
(Springer Science+Business Media, LLC, 2020-12) Li L; Zhao K; Sun R; Gan J; Yuan G; Liu T
Imbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.
The perceived benefits of apps by construction professionals in New Zealand
(MDPI AG, 1/12/2017) Liu T; Mbachu J; Mathrani A; Jones B; McDonald B
The construction sector is a key driver of economic growth in New Zealand; however, its productivity is still considered to be low. Prior research has suggested that information and communication technology (ICT) can help enhance efficiency and productivity. However, there is little research on the use of mobile technologies by New Zealand construction workforce. This paper reports findings of an exploratory study with the objective of examining the perceived benefits regarding uptake of apps in New Zealand construction sector. Using self-administered questionnaire survey, feedback was received from the major construction trade and professional organisations in New Zealand. Survey data was analyzed using descriptive, one-sample t-test, Spearman’s rank correlation coefficient and structural equation modeling. Results showed that iPhone and Android phone currently dominate the smartphone market in New Zealand construction industry. The top three application areas are site photos, health and safety reporting and timekeeping. The benefits of mobile apps were widely confirmed by the construction professionals. The benefit of “better client relationship management and satisfaction” has substantial correlation with overall productivity improvement and best predictor of the overall productivity improvement. These findings provide a starting point for further research aimed at improving the uptake and full leveraging of mobile technologies to improve the dwindling productivity trend in New Zealand construction industry.
Transnational physical activity and sport engagement of new Asian migrants in Aotearoa/New Zealand
(Victoria University of Wellington and John Wiley and Sons Australia Ltd, 2022-08) Liu T; Liu LS
Based upon a literature review, this paper first identifies and articulates the importance of studying physical activity and sport (PAS) engagement of new Asian migrants within a particular geographical location – New Zealand. A pilot study with a series of in-depth interviews highlights some challenges that New Zealand Regional Sports Organisations (RSOs) and new Asian migrants face in terms of PAS engagement. Findings from the pilot study interviews indicate that RSOs in New Zealand are well aware of these challenges, and these challenges mainly stem from a lack of understanding of the needs of new Asian migrant communities. These findings also indicate that ethnicity plays a significant role in influencing migrants' PAS engagement.
Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids.
(Hindawi Limited, 2021-03-08) Zhao Z; Liu T; Zhao X; Haber RE
Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.
Weighted adjacent matrix for K-means clustering
(Springer Science+Business Media, LLC, 2019-12) Zhou J; Liu T; Zhu J
K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.

Browsing by Author "Liu T"

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