Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Tools for successful NRM in the Lake Eyre Basin : achieving effective engagement

This chapter was developed as part of the ‘People, communities and economies of the Lake Eyre Basin’ project. It has been written for communities, government agencies and interface organisations involved in natural resource management (NRM) in the Lake Eyre Basin (LEB). Its purpose is to identify the key factors for successful community engagement processes relevant to the LEB and present tools and principles for successful engagement processes. The term ‘interface organisation’ is used here to refer to the diverse range of local and regional organisations (such as Catchment Committees or NRM Regional Bodies) that serve as linkages, or translators, between local communities and broader Australian and State Governments. The importance of fostering and harnessing effective processes of community engagement has been identified as crucial to building a prosperous future for rural and remote regions in Australia. The chapter presents an overview of the literature on successful community engagement processes for NRM, as well as an overview of the current NRM arrangements in the LEB. The main part of the chapter presents findings of the series of interviews conducted with the government liaison officers representing both state and federal organisations who are responsible for coordinating and facilitating regional NRM in the LEB, and with the members of communities of the LEB.

Measuring the mechanical properties of ground ophthalmic polymer surfaces using nanoindentation

This work is motivated by the need to efficiently machine the edges of ophthalmic polymer lenses for mounting in spectacle or instrument frames. The polymer materials used are required to have suitable optical characteristics such high refractive index and Abbe number, combined with low density and high scratch and impact resistance. Edge surface finish is an important aesthetic consideration; its quality is governed by the material removal operation and the physical properties of the material being processed. The wear behaviour of polymer materials is not as straightforward as for other materials due to their molecular and structural complexity, not to mention their time-dependent properties. Four commercial ophthalmic polymers have been studied in this work using nanoindentation techniques which are evaluated as tools for probing surface mechanical properties in order to better understand the grinding response of polymer materials.

QUT Library advances the use of open public tools to navigate genetic inventions and builds research networks

Many emerging economies are dangling the patent system to stimulate bio-technological innovations with the ultimate premise that these will improve their economic and social growth. The patent system mandates full disclosure of the patented invention in exchange of a temporary exclusive patent right. Recently, however, patent offices have fallen short of complying with such a mandate, especially for genetic inventions. Most patent offices provide only static information about disclosed patent sequences and even some do not keep track of the sequence listing data in their own database. The successful partnership of QUT Library and Cambia exemplifies advocacy in Open Access, Open Innovation and User Participation. The library extends its services to various departments within the university, builds and encourages research networks to complement skills needed to make a contribution in the real world.

Improved molecular tools for sugar cane biotechnology

Sugar cane is a major source of food and fuel worldwide. Biotechnology has the potential to improve economically-important traits in sugar cane as well as diversify sugar cane beyond traditional applications such as sucrose production. High levels of transgene expression are key to the success of improving crops through biotechnology. Here we describe new molecular tools that both expand and improve gene expression capabilities in sugar cane. We have identified promoters that can be used to drive high levels of gene expression in the leaf and stem of transgenic sugar cane. One of these promoters, derived from the Cestrum yellow leaf curling virus, drives levels of constitutive transgene expression that are significantly higher than those achieved by the historical benchmark maize polyubiquitin-1 (Zm-Ubi1) promoter. A second promoter, the maize phosphonenolpyruvate carboxylate promoter, was found to be a strong, leaf-preferred promoter that enables levels of expression comparable to Zm-Ubi1 in this organ. Transgene expression was increased approximately 50-fold by gene modification, which included optimising the codon usage of the coding sequence to better suit sugar cane. We also describe a novel dual transcriptional enhancer that increased gene expression from different promoters, boosting expression from Zm-Ubi1 over eightfold. These molecular tools will be extremely valuable for the improvement of sugar cane through biotechnology.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Perceptions of online learning in an Australian university : Malaysian students’ perspectives-usability of the online learning tools

This study set out to investigate the kinds of learning difficulties encountered by the Malaysian students and how they actually coped with online learning. The modified Online Learning Environment Survey (OLES) instrument was used to collect data from the sample of 40 Malaysian students at a university in Brisbane, Australia. A controlled group of 35 Australian students was also included for comparison purposes. Contrary to assumptions from previous researches, the findings revealed that there were only a few differences between the international Asian and Australian students with regards to their perceptions of online learning. Recommendations based on the findings of this research study were applicable for Australian universities which have Asian international students enrolled to study online.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Sharpening our tools – translating research into practice

Providing culturally appropriate health communication tools at a community level, whilst meeting funding objectives set by Government led initiatives, can be challenging. Literature states that a translational research framework fostering community communication can encourage the development of appropriate communication tools to facilitate transfer of health information between community and researchers. Reflections from initial Need for Feed cooking and nutrition education program trials in remote Indigenous communities across Cape York indicated program resources were neither meeting community nor researchers needs. In response, a translational research framework was modelled with collaborative partnerships formed between researchers and community with the aim of modifying current resources. Local working groups were established to facilitate communication and guide continual remodelling and retrial of resources for successive programs. Feedback from working groups indicated community members wanted resources with more pictures and less words. Partnership with Chronic Disease Resources Online (CDRO) led to the development of pictorial resources including 3 evaluation tools, 27 recipe sets and 10 education support materials. Between June to December 2012 resources were trialled across 4 Cape York communities with 69 school aged children and 4 community elders. Qualitative data has indicated high satisfaction with modified pictorial resources, proving pictorial resources to be an effective and culturally appropriate method to both communicate health messages to community and facilitate flow of evaluation data to researchers. A translational research framework fostering communication between community and researchers can potentially enhance the quality of health communication tools.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information.

Development of descriptor tools for the characterisation of Australian sugar mill evaporator scale

Cleaning of sugar mill evaporators is an expensive exercise. Identifying the scale components assists in determining which chemical cleaning agents would result in effective evaporator cleaning. The current methods (based on x-ray diffraction techniques, ion exchange/high performance liquid chromatography and thermogravimetry/differential thermal analysis) used for scale characterisation are difficult, time consuming and expensive, and cannot be performed in a conventional analytical laboratory or by mill staff. The present study has examined the use of simple descriptor tests for the characterisation of Australian sugar mill evaporator scales. Scale samples were obtained from seven Australian sugar mill evaporators by mechanical means. The appearance, texture and colour of the scale were noted before the samples were characterised using x-ray fluorescence and x-ray powder diffraction to determine the compounds present. A number of commercial analytical test kits were used to determine the phosphate and calcium contents of scale samples. Dissolution experiments were carried out on the scale samples with selected cleaning agents to provide relevant information about the effect the cleaning agents have on different evaporator scales. Results have shown that by simply identifying the colour and the appearance of the scale, the elemental composition and knowing from which effect the scale originates, a prediction of the scale composition can be made. These descriptors and dissolution experiments on scale samples can be used to provide factory staff with an on-site rapid process to predict the most effective chemicals for chemical cleaning of the evaporators.

Tools for Mobile Multimedia Programming and Development

Mobile devices are rapidly developing into the primary technology for users to work, socialize, and play in a variety of settings and contexts. Their pervasiveness has provided researchers with the means to investigate innovative solutions to ever more complex user demands. Tools for Mobile Multimedia Programming and Development investigates the use of mobile platforms for research projects, focusing on the development, testing, and evaluation of prototypes rather than final products, which enables researchers to better understand the needs of users through image processing, object recognition, sensor integration, and user interactions. This book benefits researchers and professionals in multiple disciplines who utilize such techniques in the creation of prototypes for mobile devices and applications. This book is part of the Advances in Wireless Technologies and Telecommunication series collection.