f) 2020 - 82 Documents

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Subject: search.filters.subject.Deep learning

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    BUILDING ACOUSTIC MODEL FOR PHONEME RECOGNITION USING PSO-DBN
    (Inderscience Enterprises Ltd, 2020-04-02) Laxmi Sree, B R; Vijaya, M S
    Deep neural networks has shown its power in generous classification problems including speech recognition. This paper proposes to enhance the power of deep belief network (DBN) further by pre-training the neural network using particle swarm optimisation (PSO). The objective of this work is to build an efficient acoustic model with deep belief networks for phoneme recognition with much better computational complexity. The result of using PSO for pre-training the network drastically reduces the training time of DBN and also decreases the phoneme error rate (PER) of the acoustic model built to classify the phonemes. Three variations of PSO namely, the basic PSO, second generation PSO (SGPSO) and the new model PSO (NMPSO) are applied in pre-training the DBN to analyse their performance on phoneme classification. It is observed that the basic PSO is performing comparably better to other PSOs considered in this work, most of the time.
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    DEEP LEARNING PREDICTIVE MODEL FOR DETECTING HUMAN INFLUENZA VIRUS THROUGH BIOLOGICAL SEQUENCES
    (Springer Link, 2020-09-08) Nandhini, M; Vijaya, M S
    Swine influenza is a contagious disease which is generated by one of the swine influenza viruses. Any modification in protein will alter the biological activity and lead to illness. Obtaining appropriate information from virus protein sequence is an interesting research problem in bioinformatics. The aim of this research work is to develop deep neural network (DNN)-based virus identification model for detecting the virus accurately with the protein sequences using deep learning. Deep learning is gaining more importance because of its governance in terms of accuracy when the network trained with large amount of data. A corpus of 404 protein sequences associated with nine types of human influenza virus is collected for training the deep neural network and building the model. Various parameters of the DNN such as input layer, hidden layer and output layer are fine-tuned to improve the efficiency of the model. Sequential model is created for developing DNN classification model using Adam optimizer with Softmax and ReLu activation functions. It is observed that experiments of proposed human influenza virus identification model with DNN classifier give 80% of accuracy and outperform with other ensemble learning algorithms.
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    DEEP NEURAL NETWORK FOR EVALUATING WEB CONTENT CREDIBILITY USING KERAS SEQUENTIAL MODEL
    (Springer Link, 2020-08-08) Manjula, R; Vijaya, M S
    Web content credibility determines the measure of acceptable and reliable of the web content that is observed. Content will prove to be unreliable if it is not updated, and it is not controlled for remarkable, and therefore, web content credibility is considerably essential for the people to assess the content. The analysis of content credibility is a vital and challenging task as the content credibility is outlined on crucial factors. This paper focuses on building deep neural network (DNN)-based predictive model using sequential model API to evaluate credibility of a webpage content. Deep neural network (DNN) is considered as an extremely promising decision-making architecture, and it performs feature extraction and transformation with the use of refined statistical modeling. A corpus of 400 webpage contents has been developed, and the factors like readability, freshness, and duplicate content are defined and captured from the webpage content. These features are redefined, and a new set of features is self-learned through the deep layers of neural network. Numeric labeling is used for defining credibility, wherein five-point Likert scale rating is used to denote the content credibility. By using sequential model, KerasRegressor with ADAM optimizer and a multilayer network is generated for building DNN-based predictive model and discovered that deep neural network outperforms other general regression algorithms in prediction scores.