List of Titles

Evidence for the likely extinction of an endemic south Australian springtail, 'Temeritas regalis' (Symphypleona: Sminthuridae)

- Greenslade, Penelope

Authors: Greenslade, Penelope
Date: 2019
Type: Text , Journal article
Relation: South Australian naturalist Vol. 93, no. 2 (2019), p. 103-110
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Description: 'Temeritas regalis' (Womersley, 1939) was formerly common in the Mt Lofty Ranges extending from as far north as Alligator Gorge/Mt Remarkable/Wirrabara in the Southern Flinders to Myponga, 60 km south of Adelaide. The species is a South Australian endemic and belongs to a pantropical wide genus. It was collected several times in the 1930's and fourteen times in the 1970's but it has not been seen since 1978. In spite of collecting efforts from its typical habitat in the Mt Lofty Ranges and Southern Flinders Ranges and at Mt Bold on 21 occasions in the last 20 years, no further specimens have been found. It appears that populations have shrunk in size or may have disappeared altogether since the 1980s due to a number of factors mainly: climate change, drought, habitat alteration and/or expansion of invasive plants, invertebrates and 'Phytophthora'. Consequently, it is suggested that the apparent population decrease and possible local extinctions are real and, as a result, this species complies with the IUCN criteria A1 and B, and should be listed as endangered under the South Australian legislation.

New gene selection algorithm using hypeboxes to improve performance of classifiers

- Bagirov, Adil, Mardaneh, Karim

Authors: Bagirov, Adil , Mardaneh, Karim
Date: 2020
Type: Text , Journal article
Relation: International Journal of Bioinformatics Research and Applications Vol. 16, no. 3 (2020), p. 269-289
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Description: The use of DNA microarray technology allows to measure the expression levels of thousands of genes in one single experiment which makes possible to apply classification techniques to classify tumours. However, the large number of genes and relatively small number of tumours in gene expression datasets may (and in some cases significantly) diminish the accuracy of many classifiers. Therefore, efficient gene selection algorithms are required to identify most informative genes or groups of genes to improve the performance of classifiers. In this paper, a new gene selection algorithm is developed using marginal hyberboxes of genes or groups of genes for each tumour type. Informative genes are defined using overlaps between hyberboxes. The results on six gene expression datasets demonstrate that the proposed algorithm is able to considerably reduce the number of genes and significantly improve the performance of classifiers. © 2020 Inderscience Enterprises Ltd.

Supervised anomaly detection in uncertain pseudoperiodic data streams

- Ma, Jiangang, Sun, Le, Wang, Hua, Zhang, Yanchun, Aickelin, Uwe

Authors: Ma, Jiangang , Sun, Le , Wang, Hua , Zhang, Yanchun , Aickelin, Uwe
Date: 2016
Type: Text , Journal article
Relation: ACM transactions on Internet technology Vol. 16, no. 1 (2016), p. 1-20
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Description: Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on several real datasets.

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Mineral texture identification using local binary patterns equipped with a Classification and Recognition Updating System (CARUS)

- Aligholi, Saeed, Khajavi, Reza, Khandelwal, Manoj, Armaghani, Danial

Authors: Aligholi, Saeed , Khajavi, Reza , Khandelwal, Manoj , Armaghani, Danial
Date: 2022
Type: Text , Journal article
Relation: Sustainability (Switzerland) Vol. 14, no. 18 (2022), p.
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Description: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. © 2022 by the authors.

Action research to implement an Indigenous health curriculum framework

- Wilson, Cath, Heinrich, Liesl, Heidari, Parvaneh, Adams, Karen

Authors: Wilson, Cath , Heinrich, Liesl , Heidari, Parvaneh , Adams, Karen
Date: 2020
Type: Text , Journal article
Relation: Nurse education today Vol. 91, no. (2020), p. 104464-104464
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Description: In recent decades Indigenous health curriculum frameworks have been developed, however, few studies about their implementation exist. This study aimed to employ critical theory and action research to understand how an Indigenous health curriculum framework could be applied and associated learning and teaching iteratively improved. Three action research cycles where conducted from 2017 to 2019. Student reaction (satisfaction and engagement) was collected via survey 2017–2019. Student learning was collated 2018–2019 via self-perception survey (knowledge, attitude, confidence, commitment) multi-choice questions (knowledge) and content analysis of apply and analyse activities (skill). The teaching team met annually to reflect on findings and plan enhancements to learning and teaching. Over 2017–2019 there was a pattern of improved student reaction and learning. Connecting this research to Faculty level committees led to widening success and improved sustainability of the practice. The online unit and workshop delivery were scalable, overcame a barrier of educator skill and confidence to teach this area, allowed for quality content control and provided data for analysis. Interestingly, learning gained from this unit matched that described as occurring from student placements in health settings with high numbers of Indigenous people. Student learning occurred across the Framework three levels (novice, intermediate and entry to practice) suggesting that the taxonomy of the Framework does not necessarily align with the reality of learning and teaching. Vertical implementation of the five learning domains would benefit from alignment with training evaluation models and validated assessment to understand learning that has occurred rather than the teaching that has been taught. In this study health profession accreditation bodies had driven the imperative for an Indigenous health program and curriculum. Research on Indigenous health learning and teaching relating to behaviour and results in workplaces is needed.

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Attribute weighted Naive Bayes classifier using a local optimization

- Taheri, Sona, Yearwood, John, Mammadov, Musa, Seifollahi, Sattar

Authors: Taheri, Sona , Yearwood, John , Mammadov, Musa , Seifollahi, Sattar
Date: 2013
Type: Text , Journal article
Relation: Neural Computing & Applications Vol.24, no.5 (2013), p.995-1002
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Description: The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.

A new solar power prediction method based on feature clustering and hybrid-classification-regression forecasting

- Nejati, Maryam, Amjady, Nima

Authors: Nejati, Maryam , Amjady, Nima
Date: 2022
Type: Text , Journal article
Relation: IEEE transactions on sustainable energy Vol. 13, no. 2 (2022), p. 1188-1198
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Description: Solar generation systems are globally extending in terms of scale and number, which highlights the increasing importance of solar power forecast. In this paper, a day-ahead solar power prediction method is proposed including 1) a novel feature selecting/clustering approach based on relevancy and redundancy criteria and 2) an innovative hybrid-classification-regression forecasting engine. The proposed feature selecting/clustering approach filters out irrelevant features and partitions relevant features to two separate subsets to decrease the redundancy of features. Each of these two subsets is separately trained by one forecasting engine and the final solar power prediction of the proposed method is obtained by a relevancy-based combination of these two forecasts. The proposed forecasting engine classifies the historical data based on the learnability of its constituent regression models and assigns each class of training samples to one regression model. Each regression model predicts the outputs of the test samples that belong to its class. The effectiveness of the proposed solar power prediction method is illustrated by testing on two real-world solar farms.

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Subgraph adaptive structure-aware graph contrastive learning

- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng

Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
Date: 2022
Type: Text , Journal article
Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.

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An optimized hybrid deep intrusion detection model (HD-IDM) for enhancing network security

- Ahmad, Iftikhar, Imran, Muhammad, Qayyum, Abdul, Ramzan, Muhammad, Alassafi, Madini

Authors: Ahmad, Iftikhar , Imran, Muhammad , Qayyum, Abdul , Ramzan, Muhammad , Alassafi, Madini
Date: 2023
Type: Text , Journal article
Relation: Mathematics Vol. 11, no. 21 (2023), p.
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Description: Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. © 2023 by the authors.

Fog computing: Survey of trends, architectures, requirements, and research directions

- Naha, Ranesh, Garg, Saurabh, Georgakopoulos, Dimitrios, Jayaraman, Prem, Gao, Longxiang, Xiang, Yong, Ranjan, Rajiv

Authors: Naha, Ranesh , Garg, Saurabh , Georgakopoulos, Dimitrios , Jayaraman, Prem , Gao, Longxiang , Xiang, Yong , Ranjan, Rajiv
Date: 2018
Type: Text , Journal article
Relation: IEEE access Vol. 6, no. (2018), p. 47980-48009
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Description: Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.

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A classification algorithm that derives weighted sum scores for insight into disease

- Quinn, Anthony, Stranieri, Andrew, Yearwood, John, Hafen, Gaudenz

Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
Date: 2009
Type: Text , Conference paper
Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
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Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.

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