Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

Study on non-parametric methods for fast pattern recognition with emphasis on neural networks and cascade classifiers

Tese de doutoramento em Engenharia Eletrotécnica e de Computadores, no ramo de especialização em Automação e Robótica, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraEsta tese concentra-se em reconhecimento de padrões, com particular ênfase para o con ito de escolha entre capacidade de generalização e custo computacional, a m de fornecer suporte para aplicações em tempo real. Neste contexto são apresentadas contribuições metodológicas e analíticas para a abordagem de dois tipos de datasets: balanceados e desbalanceados. Um dataset é denominado balanceado quando há um número aproximadamente igual de observações entre as classes, enquanto datasets que têm números desiguais de observações entre as classes são denominados desbalanceados, tal como ocorre no caso de detecção de objetos baseada em imagem. Para datasets balanceados é adoptado o perceptrão multicamada (MLP) como classi cador, uma vez que tal modelo é um aproximador universal, ou seja MLPs podem aproximar qualquer conjunto de dados. Portanto, ao invés de propor novos modelos de classi cadores, esta tese concentra-se no desenvolvimento e análise de novos métodos de treinamento para MLP, de forma a melhorar a sua capacidade de generalização através do estudo de quatro abordagens diferentes: maximização da margem de classi cação, redundância, regularização, e transdução. A idéia é explorar novos métodos de treino para MLP com vista a obter classi cadores não-lineares mais rápidos que o usual SVM com kernel não-linear, mas com capacidade de generalização similar. Devido à sua função de decisão, o SVM com kernel não-linear exige um esforço computacional elevado quando o número de vetores de suporte é grande. No contexto dos datasets desbalanceados, adotou-se classi cadores em cascata, já que tal modelo pode ser visto como uma árvore de decisão degenerativa que realiza rejeições em cascata, mantendo o tempo de processamento adequado para aplicações em tempo real. Tendo em conta que conjuntos de classi cadores são susceptíveis a ter alta dimensão VC, que pode levar ao over- tting dos dados de treino, foram deduzidos limites para a capacidade de generalização dos classi cadores em cascata, a m de suportar a aplicação do princípio da minimização do risco estrutural (SRM). Esta tese também apresenta contribuições na seleção de características e dados de treinamento, devido à forte in uência que o pre-processamento dos dados tem sobre o reconhecimento de padrões. Os métodos propostos nesta tese foram validados em vários datasets do banco de dados da UCI. Alguns resultados experimentais já podem ser consultados em três revistas da ISI, outros foram submetidos a duas revistas e ainda estão em processo de revisão. No entanto, o estudo de caso desta tese é limitado à detecção e classi cação de peões.This thesis focuses on pattern recognition, with particular emphasis on the trade o between generalization capability and computational cost, in order to provide support for on-the- y applications. Within this context, two types of datasets are analyzed: balanced and unbalanced. A dataset is categorized as balanced when there are approximately equal numbers of observations in the classes, while unbalanced datasets have unequal numbers of observations in the classes, such as occurs in case of imagebased object detection. For balanced datasets it is adopted the multilayer perceptron (MLP) as classi er, since such model is a universal approximator, i.e. MLPs can t any dataset. Therefore, rather than proposing new classi er models, this thesis focuses on developing and analysing new training methods for MLP, in order to improve its generalization capability by exploiting four di erent approaches: maximization of the classi cation margin, redundancy, regularization, and transduction. The idea is to exploit new training methods for MLP aiming at an nonlinear classi er faster than the usual SVM with nonlinear kernel, but with similar generalization capability. Note that, due to its decision function, the SVM with nonlinear kernel requires a high computational e ort when the number of support vectors is big. For unbalanced datasets it is adopted the cascade classi er scheme, since such model can be seen as a degenerate decision tree that performs sequential rejection, keeping the processing time suitable for on-the- y applications. Taking into account that classi er ensembles are likely to have high VC dimension, which may lead to over- tting the training data, it were derived generalization bounds for cascade classi ers, in order to support the application of structural risk minimization (SRM) principle. This thesis also presents contributions on feature and data selection, due to the strong in uence that data pre-processing has on pattern recognition. The methods proposed in this thesis were validated through experiments on several UCI benchmark datasets. Some experimental results can be found in three ISI journals, others has been already submitted to two ISI journals, and are under review. However, the case study of this thesis is limited to pedestrian detection and classi cation

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden