Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.Comment: 12pages, 6 figure

Estrategias Bioinspiradas para la optimización del aprendizaje de Redes Neuronales Artificiales (RNA)

A lo largo de las últimas décadas las Redes Neuronales Artificiales (RNAs) han sido un campo de estudio muy popular en el campo de la computación e inteligencia artificial. Esto es debido a potencia que tiene como herramientas en gran variedad de campos y disciplinas. Junto con las RNAs, otro campo en auge es el de los algoritmos evolutivos, algoritmos extremadamente versátiles y eficaces a la hora de atacar un problema de optimización de forma automatizada. En un intento de optimizar estas redes y avanzar en el desarrollo de estas tecnologías, hemos creado nuestra versión de RNA en la que las neuronas son independientes y pueden actuar de forma distinta a las demás. Esto nos es útil a la hora de analizar el comportamiento de la red al quitarle neuronas después del entrenamiento o inducir anomalías. Utilizando esta red como base, hemos implementado dos algoritmos de aprendizaje: El clásico Backpropagation y un algoritmo genético. Usando las características de nuestra red neuronal artificial hemos realizado un estudio comparativo donde analizamos la tolerancia a la perdida de neuronas en relación al algoritmo de aprendizaje utilizado. Hemos usado la experiencia en el estudio comparativo para aplicar las características de nuestra red a un caso real en el que clasificamos curvas simuladas de microscopia de sonda de barrido. A través de una serie de experimentos con la función de fit del algoritmo genético hemos aumentado la tolerancia a fallos o datos perdidos en la red con lo que demostramos que el trabajo que hemos realizado tiene aplicaciones directas en la vida real.On the last decades, Artificial Neural Networks (ANNs) have been a widely studied field of computer science and artificial intelligence. This is due to their power as a tool in a large number of disciplines. ANNs can classify data, approximate complex functions, make predictions, recognize patterns and more. There are many types of ANN. In our project we use a modified version of a Feedforward Neural Network (FFNN) for our experiments. Along with ANNs, Evolutionary Algorithms (EAs) are a popular study subject. The reason behind this is that EAs are extremely versatile and effective when it comes to automatic problem optimization. In an attempt to optimise this kind of networks and contribute on their further development we have added to our feedforward neural network the ability to treat neurons independently. This is useful when it comes to disconnect specific neurons after the training or to induce any kind of malfunctioning to observe the effects. Using this network as our baseline we implemented two training techniques: Backpropagation (BP) and the Genetic Algorithm (GA). Thanks to our network’s characteristics we have conducted a comparative study where we analyse the network’s tolerance to neuronal failure depending on the training algorithm. We use the experience gained in the study to apply the network’s characteristics to a real problem. We classify simulated Scanning Probe Microscopy (SPM) curves. Through a series of experiments with GA’s fit function we increased failure and missing data tolerance in the network. With this we demonstre that the work we have done has real life applications

PREDICTION OF RECURRENCE AND MORTALITY OF ORAL TONGUE CANCER USING ARTIFICIAL NEURAL NETWORK (A case study of 5 hospitals in Finland and 1 hospital from Sao Paulo, Brazil)

Cancer is a dreadful disease that had caused the death of millions of people. It is characterized by an uncontrollable growth of cell to form lumps or masses of tissue that are known as tumour. Therefore, it is a concern to all and sundry as these tumours mostly release hormones which have negative impact on the body system. Data mining approaches, statistical methods and machine learning algorithms have been proposed for effective cancer data classification. Artificial Neural Networks (ANN) have been used in this thesis for the prediction of recurrence and mortality of oral tongue cancer in patients. Similarly, ANN was also used to examine the diagnostic and prognostic factors. This was aimed at determining which of these diagnostic and prognostics factors had influence on the prediction of recurrence and mortality of oral tongue cancer in patients. Three different ANN have been applied for the learning and testing phases. The aim was to find the most effective technique. They are Elman, Feedforward, and Layer Recurrent neural networks techniques. Elman neural network was not able to make acceptable prediction of the recurrence or the mortality of tongue cancer based on the data. In contrast, Feedforward neural network captured the relationship between the prognostic factors and correctly predicted recurrence. However, it failed to predict the mortality based on the patient's data. Layer Recurrence neural network has been very effective and successfully predicted the recurrence and the mortality of oral tongue cancer in patients. The constructed layered recurrence neural network has been used to investigate the correlation between the prognostic factors. It was found that out of 11 prognostic factors in the data sheet, it was only 5 of them that had considerable impact on the recurrence and mortality. These are grade, depth, budding, modified stage, and gender. Time in months and disease free months were also used to train the network.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results