User ticketing system with automatic resolution suggestions

In the recent years, neural networks are very popular in the field of the artificial intelligence (AI). We decided to design the project on the basis of it. Neural network is the branch of the machine learning that uses the different layers to represent the data. Data are transformed to different or multi layers and generate the output. Our project is User ticketing system with automatic resolution suggestions, in which data is text based and with the help of Natural language processing (NLP) we can solve the problem. NLP is a sub-field of artificial intelligence which creates interactions between computers and human language. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. In this project the main goal is measuring the similarity between the texts. We design different models and use different layers and hyper-parameters later we will discuss in details. Our models are based on the Siamese neural network (SNN). SNN (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. In this project, three main approaches have been used. Firstly, on the basis of the pre-trained model we get the output. Secondly, we follow the transfer learning approach. The transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. Thirdly, the model is fine tunned and designed on the basis of mutilingual (Spanish and English)

Speech Transmission Index from running speech : a neural network approach

Speech Transmission Index (STI) is an important objective parameter concerning speech intelligibility for sound transmission channels. It is normally measured with specific test signals to ensure high accuracy and good repeatability. Measurement with running speech was previously proposed, but accuracy is compromised and hence applications limited. A new approach that uses artificial neural networks to accurately extract the STI from received running speech is developed in this paper. Neural networks are trained on a large set of transmitted speech examples with prior knowledge of the transmission channels' STIs. The networks perform complicated nonlinear function mappings and spectral feature memorization to enable accurate objective parameter extraction from transmitted speech. Validations via simulations demonstrate the feasibility of this new method on a one-net-one-speech extract basis. In this case, accuracy is comparable with normal measurement methods. This provides an alternative to standard measurement techniques, and it is intended that the neural network method can facilitate occupied room acoustic measurements

Neural Networks Architecture Evaluation in a Quantum Computer

In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks. Unlike conventional algorithms for evaluating neural network architectures, QNNAE does not depend on initialization of weights. The proposed algorithm has a binary output and results in 0 with probability proportional to the performance of the network. And its computational cost is equal to the computational cost to train a neural network

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM