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Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

• This paper is dedicated to vehicle-bicycle crash severity modeling. • The class-imbalanced issue in vehicle-bicycle crash severity analysis is handled with the imbalanced data resampling process. • To address the complexity of crash dataset, the learning-based feature extraction process is adopted in an iterative manner, such that the the most significant contributing factors to the severity of vehicle-bicycle crashes are determined and the trade-off between computation time and model performance is catered for. • The vehicle-bicycle crash dataset in this paper contains a large number of discrete variables. The gradient boosting algorithm is applied to handle the large number of categories and does not rely on strict statistical assumptions. • The impact of the most significant contributing factors on the severity of vehiclebicycle crashes are explained with the marginal effect analysis. • The result can provide some implications for policies and counter-measures for fatal and serious vehicle-bicycle crashes. Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Today's information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of 'oceans of data' by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia's largest insurance companies. [ABSTRACT FROM AUTHOR]

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data. [ABSTRACT FROM AUTHOR]

The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. The goal of the NBN is to provide Australians with broadband internet access by using a mix of technologies, ranging from fibre and hybrid fibre-coaxial to fixed wireless and satellite platforms. Although the NBN is a public project, one of the more vexing aspects of its evolution is its lack of data transparency. There is virtually no information on platform use or footprint distribution throughout the country. Not only does this drastically limit evidence-based telecommunications policy analysis, it also limits the ability to evaluate equity in the spatial distribution of broadband connectivity and infrastructure quality. The purpose of this paper is to uncover the geographic presence of the various NBN technologies using data mining techniques and census-based socio-spatial data (SEIFA). The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane – with a focus on how the mixed-technology platform adopted divides major Australian cities. • The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. • There is limited information on the geographic distribution of the various NBN technologies throughout the country. • This paper aims to uncover the NBN footprint using data mining techniques and census-based socio-spatial data (SEIFA). • The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]