Debt detection and debt recovery with advanced classification techniques

University of Technology Sydney. Faculty of Engineering and Information Technology.My study is part of an ARC linkage project between University of Technology, Sydney and Centrelink Australia, which aims to applying data mining techniques to optimise the debt detection and debt recovery. A debt indicates an overpayment made by the government to a customer who is not entitled to that payment. In social security, an interaction between a customer and the government department is recorded as an activity. Each customer’s activities happen sequentially along the time, which can be regarded as a sequence. Based on the experience of debt detection experts, there are usually some patterns in the sequence of activities of customers who commit debts. The patterns indicating the customers’ intention to be overpaid can thus be used to discover or predict debt occurrence. The development of debt detection and recovery over sequential transaction data, however, is a challenging problem due to following reasons. (1) The size of transaction data is vast, and the transaction data are being generated continuously as the business goes on. (2) Transaction data are always time stamped by the business system, and the temporal order of the transaction data is highly related to the business logic. (3) The patterns and relationships hidden behind the transaction data may be affected by a lot of factors. They are not only dependent on business domain knowledge, but also subject to seasonal and social factors outside the business. Based on a survey of existing methods on debt detection and recovery, data mining techniques are studied in this thesis to detect and recovery debt in an adaptive and efficient fashion. Firstly, sequence data is used to model the evolvement of customer activities, and the sequential patterns generalize the trends of sequences. For long running sequence classification issues, even if the sequences come from the same source, the sequential patterns may vary from time to time. An adaptive sequential classification model is to be built to make the sequence classification adapt to the sequential pattern variation. The model is applied to 15,931 activity sequences from Centrelink which includes 849,831 activity records. The experimental results show that the proposed adaptive sequence classification framework performs effectively on the continuously arriving data. Secondly, a new technique of sequence classification using both positive and negative patterns is to be studied, which is able to find the relationship between activity sequences and debt occurrences and also the impact of oncoming activities on the debt occurrence. The same dataset is used for the evaluation. The outcome shows if built with the same number of rules, in terms of recall, the classifier built with both positive and negative rules outperforms traditional classifiers with only positive rules under most conditions. Finally, decision trees are to be built in the thesis to model debt recovery and predict the response of customers if contacted by phone. The customer contact strategy driven by the model aims to improve the efficiency of debt recovery process. The model is utilized in a real life pilot project for debt recovery in Centrelink. The pilot result outperforms the traditional random customer selection. In summary, this thesis studies debt detection and debt recovery in social security using data mining techniques. The proposed models are novel and effective, showing potentials in real business

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments

Influence of ferromagnetic spin waves on persistent currents in one-dimensional mesoscopic rings

The influence of the electron-magnon and the electron-phonon interactions on the persistent current in a one-dimensional mesoscopic ring is studied. We show that, due to the electron-magnon interaction, the amplitude of the persistent current is exponentially reduced compared to the free case. Two features occur in the presence of an electron-phonon interaction. For the normal state of electrons, the persistent current is weakened by the Debye-Waller factor. Considering the so-called Peierls distortions, we show that the effect of the Peierls instability on the amplitude of the persistent current (i.e., the oscillation with respect to the flux) is suppressed significantly and the persistent current will be practically undetectable in the case of a wide-gap Peierls material. © 1996 The American Physical Society.published_or_final_versio

State-independent error-disturbance trade-off for measurement operators

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Computation Energy Efficiency Maximization for Intelligent Reflective Surface-Aided Wireless Powered Mobile Edge Computing

A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This paper studies the Wireless Power Transfer-based Mobile Edge Computing (WPT-MEC) network system assisted by Intelligent Reflective Surface (IRS) to enhance communication performance while improving the battery life of MD. In order to maximize the Computation Energy Efficiency (CEE) of the system and reduce the carbon footprint of the MEC server, we jointly optimize the CPU frequencies of MDs and MEC server, the transmit power of Power Beacon (PB), the processing time of MEC server, the offloading time and the energy harvesting time of MDs, the local processing time and the offloading power of MD and the phase shift coefficient matrix of Intelligent Reflecting Surface (IRS). Moreover, we transform this joint optimization problem into a fractional programming problem. We then propose the Dinkelbach Iterative Algorithm with Gradient Updates (DIA-GU) to solve this problem effectively. With the help of convex optimization theory, we can obtain closed-form solutions, revealing the correlation between different variables. Compared to other algorithms, the DIA-GU algorithm not only exhibits superior performance in enhancing the system's CEE but also demonstrates significant reductions in carbon emissions

Magnetoresistance in La- and Ca-doped YBa2Cu3O7–δ

We studied the microstructures, electronic, and magnetic properties on La-doped and La- and Ca-codoped YBa2Cu3O7−δ (YBCO). The superconducting transition temperature remains unchanged up to 10% for La-doped YBCO. The competition between electrons and holons was assumed according to the variation of Tc0 in La and Ca codopings in YBCO. The magnetoresistance (MR) effect is about 8%, which is observed obviously near the critical temperature and is independent of the content of La in La-doped YBCO. MR increases up to about 40% with the incorporation of Ca in La-doped YBCO. We present here possible explanations for the magnetoresistance effect in polycrystalline samples based on the microstructure and the increase of oxygen vacancies at grain-boundary interface. © 2006 American Institute of Physicspublished_or_final_versio

Breath-hold FSE for accurate imaging of myocardial and hepatic R2

Session 21: Hepatic Storage Disease - Oral presentationMRI provides a means to non-invasively assess tissue iron concentration by exploiting the paramagnetic effects of iron on T2 or T2*. The most widely used method is T2* imaging is sensitive to non-iron related magnetic field (B0) inhomogeneities, which can confound T2* measurements within the whole heart and liver. An alternative method is T2 imaging, but they are generally performed during free breathing with respiratory gating due to their low data acquisition efficiency. The purpose of this study was to develop a breath-hold fast spin echo (FSE) sequence for fast and accurate imaging of myocardial and hepatic T2.published_or_final_versionThe 17th Scientific Meeting & Exhibition of the International Society of Magnetic Resonance in Medicine (ISMRM), Honolulu, HI., 18-24 April 2009. In Proceedings of ISMRM 17th Scientific Meeting & Exhibition, 2009, p. 20

Blockchain and Reinforcement Neural Network for Trusted Cloud-Enabled IoT Network

The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAWMOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency

Holographic non-relativistic fermionic fixed point by the charged dilatonic black hole

Driven by the landscape of garden-variety condensed matter systems, we have investigated how the dual spectral function behaves at the non-relativistic as well as relativistic fermionic fixed point by considering the probe Dirac fermion in an extremal charged dilatonic black hole with zero entropy. Although the pattern for both of the appearance of flat band and emergence of Fermi surface is qualitatively similar to that given by the probe fermion in the extremal Reissner-Nordstrom AdS black hole, we find a distinctly different low energy behavior around the Fermi surface, which can be traced back to the different near horizon geometry. In particular, with the peculiar near horizon geometry of our extremal charged dilatonic black hole, the low energy behavior exhibits the universal linear dispersion relation and scaling property, where the former indicates that the dual liquid is a Fermi one while the latter implies that the dual liquid is not exactly of Landau Fermi type

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules