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Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.

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Driver fatigue and inattention have long been recognised as the main contributing factors in traffic accidents. This study presents a novel system which applies convolutional neural network (CNN) to automatically learn and predict pre‐defined driving postures. The main idea is to monitor driver hand position with discriminative information extracted to predict safe/unsafe driving posture. In comparison to previous approaches, CNNs can automatically learn discriminative features directly from raw images. In the authors' works, a CNN model was first pre‐trained by an unsupervised feature learning method called sparse filtering, and subsequently fine‐tuned with classification. The approach was verified using the Southeast University driving posture dataset, which comprised of video clips covering four driving postures, including normal driving, responding to a cell phone call, eating, and smoking. Compared with other popular approaches with different image descriptors and classification methods, the authors' scheme achieves the best performance with an overall accuracy of 99.78%. To evaluate the effectiveness and generalisation performance in more realistic conditions, the method was further tested using other two specially designed datasets which takes into account of the poor illuminations and different road conditions, achieving an overall accuracy of 99.3 and 95.77%, respectively.

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Background Paediatric fever is a common cause of emergency department (ED) attendance. A lack of prompt and definitive diagnostics makes it difficult to distinguish viral from potentially life-threatening bacterial causes, necessitating a cautious approach. This may result in extended periods of observation, additional radiography, and the precautionary use of antibiotics (ABs) prior to evidence of bacterial foci. This study examines resource use, service costs, and health outcomes. Methods We studied an all-year prospective, comprehensive, and representative cohort of 6518 febrile children (aged < 16 years), attending Alder Hey Children’s Hospital, an NHS-affiliated paediatric care provider in the North West of England, over a 1-year period. Performing a time-driven and activity-based micro-costing, we estimated the economic impact of managing paediatric febrile illness, with focus on nurse/clinician time, investigations, radiography, and inpatient stay. Using bootstrapped generalised linear modelling (GLM, gamma, log), we identified the patient and healthcare provider characteristics associated with increased resource use, applying retrospective case-note identification to determine rates of potentially avoidable AB prescribing. Results Infants aged less than 3 months incurred significantly higher resource use than any other age group, at £1000.28 [95% CI £82.39–£2993.37] per child, ( p < 0.001), while lesser experienced doctors exhibited 3.2-fold [95% CI 2.0–5.1-fold] higher resource use than consultants ( p < 0.001). Approximately 32.4% of febrile children received antibiotics, and 7.1% were diagnosed with bacterial infections. Children with viral illnesses for whom antibiotic prescription was potentially avoidable incurred 9.9-fold [95% CI 6.5–13.2-fold] cost increases compared to those not receiving antibiotics, equal to an additional £1352.10 per child, predominantly resulting from a 53.9-h increase in observation and inpatient stay (57.1 vs. 3.2 h). Bootstrapped GLM suggested that infants aged below 3 months and those prompting a respiratory rate ‘red flag’, treatment by lesser experienced doctors, and Manchester Triage System (MTS) yellow or higher were statistically significant predictors of higher resource use in 100% of bootstrap simulations. Conclusion The economic impact of diagnostic uncertainty when managing paediatric febrile illness is significant, and the precautionary use of antibiotics is strongly associated with increased costs. The use of ED resources is highest among infants (aged less than 3 months) and those infants managed by lesser experienced doctors, independent of clinical severity. Diagnostic advances which could increase confidence to withhold antibiotics may yield considerable efficiency gains in these groups, where the perceived risks of failing to identify potentially life-threatening bacterial infections are greatest. ...

Abstract:The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

Abstract:The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighed Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI.Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.