scholarly journals SpliceVec: distributed feature representations for splice junction prediction

2017 ◽  
Author(s):  
Aparajita Dutta ◽  
Tushar Dubey ◽  
Kusum Kumari Singh ◽  
Ashish Anand
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
Random Sequence ◽  
Splice Junction ◽  
Feature Representation ◽  
Computationally Efficient ◽  
Feature Representations ◽  
Proposed Model ◽  
Feature Extraction And Selection ◽  
Splice Junctions

AbstractIdentification of intron boundaries, called splice junctions, is an important part of delineating gene structure and functions. This also provides valuable in-sights into the role of alternative splicing in increasing functional diversity of genes. Identification of splice junctions through RNA-seq is by mapping short reads to the reference genome which is prone to errors due to random sequence matches. This encourages identification of splicing junctions through computa-tional methods based on machine learning. Existing models are dependent on feature extraction and selection for capturing splicing signals lying in vicinity of splice junctions. But such manually extracted features are not exhaustive. We introduce distributed feature representation, SpliceVec, to avoid explicit and biased feature extraction generally adopted for such tasks. SpliceVec is based on two widely used distributed representation models in natural language processing. Learned feature representation in form of SpliceVec is fed to multi-layer perceptron for splice junction classification task. An intrinsic evaluation of SpliceVec indicates that it is able to group true and false sites distinctly. Our study on optimal context to be considered for feature extraction indicates inclusion of entire intronic sequence to be better than flanking upstream and downstream region around splice junctions. Further, SpliceVec is invariant to canonical and non-canonical splice junction detection. The proposed model is consistent in its performance even with reduced dataset and class-imbalanced dataset. SpliceVec is computationally efficient and can be trained with user defined data as well.

scholarly journals General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091100 ◽  
Author(s):  
Ahmad al-Qerem ◽  
Faten Kharbat ◽  
Shadi Nashwan ◽  
Staish Ashraf ◽  
khairi blaou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Differential Evolution ◽  
Discrete Wavelet Transform ◽  
Research Work ◽  
Discrete Wavelet ◽  
Proposed Model ◽  
Feature Extraction And Selection ◽  
Selection Step ◽  
Wavelet Family

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

scholarly journals A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021 ◽  
Author(s):  
Huiyun Zhang ◽  
Heming Huang ◽  
Henry Han
Keyword(s):  
Feature Extraction ◽  
Natural Language Processing ◽  
Emotion Recognition ◽  
Language Processing ◽  
Speech Emotion Recognition ◽  
Dense Layer ◽  
Acoustic Model ◽  
Spatiotemporal Information ◽  
Proposed Model ◽  
The Right

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

scholarly journals An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets

2021 ◽  
Vol 11 (22) ◽  
pp. 10801
Author(s):  
Gabriel Ichcanziho Pérez-Landa ◽  
Octavio Loyola-González ◽  
Miguel Angel Medina-Pérez
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Language Processing ◽  
Physical Violence ◽  
Violent Behavior ◽  
Feature Representation ◽  
Feature Representations ◽  
Explainable Artificial Intelligence ◽  
Contrast Pattern ◽  
New Feature

Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours. With the rise of social networks like Twitter, hate speeches were swift because of the pseudo feeling of anonymity that these platforms provide. Sometimes this violent behavior on social networks that begins as threats or insults to third parties breaks the Internet barriers to become an act of real physical violence. Hence, this proposal aims to correctly classify xenophobic posts on social networks, specifically on Twitter. In addition, we collected a xenophobic tweets database from which we also extracted new features by using a Natural Language Processing (NLP) approach. Then, we provide an Explainable Artificial Intelligence (XAI) model, allowing us to understand better why a post is considered xenophobic. Consequently, we provide a set of contrast patterns describing xenophobic tweets, which could help decision-makers prevent acts of violence caused by xenophobic posts on Twitter. Finally, our interpretable results based on our new feature representation approach jointly with a contrast pattern-based classifier obtain similar classification results than other feature representations jointly with prominent machine learning classifiers, which are not easy to understand by an expert in the application area.

scholarly journals A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021 ◽  
Vol 11 (21) ◽  
pp. 9897
Author(s):  
Huiyun Zhang ◽  
Heming Huang ◽  
Henry Han
Keyword(s):  
Feature Extraction ◽  
Natural Language Processing ◽  
Emotion Recognition ◽  
Language Processing ◽  
Speech Emotion Recognition ◽  
Dense Layer ◽  
Acoustic Model ◽  
Spatiotemporal Information ◽  
Proposed Model ◽  
The Right

Speech emotion recognition is a substantial component of natural language processing (NLP). It has strict requirements for the effectiveness of feature extraction and that of the acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address the challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recalls on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

scholarly journals Bidirectional Nonnegative Deep Model and Its Optimization in Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xianhua Zeng ◽  
Zhengyi He ◽  
Hong Yu ◽  
Shengwei Qu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Nonnegative Matrix ◽  
Feature Representation ◽  
Nonlinear Fitting ◽  
Feature Representations ◽  
Deep Model ◽  
Proposed Model ◽  
Out Of Sample ◽  
Narrow Signal

Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

HIERARCHICAL SPARSE METHOD WITH APPLICATIONS IN VISION AND SPEECH RECOGNITION

2013 ◽  
Vol 11 (02) ◽  
pp. 1350016
Author(s):  
RAMADHAN ABDO MUSLEH ALSAIDI ◽  
HONG LI ◽  
YANTAO WEI ◽  
ROKAN KHAJI ◽  
YUAN YAN TANG
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Image Recognition ◽  
Sparse Coding ◽  
Experimental Results ◽  
New Approach ◽  
Feature Representations ◽  
Proposed Model ◽  
Hierarchical Architectures ◽  
Isolated Word

A new approach for feature extraction using neural response has been developed in this paper through combining the hierarchical architectures with the sparse coding technique. As far as proposed layered model, at each layer of hierarchy, it concerned two components that were used are sparse coding and pooling operation. While the sparse coding was used to solve increasingly complex sparse feature representations, the pooling operation by comparing sparse outputs was used to measure the match between a stored prototype and the input sub-image. It is recommended that value of the best matching should be kept and discarding the others. The proposed model is implemented and tested taking into account two ranges of recognition tasks i.e. image recognition and speech recognition (on isolated word vocabulary). Experimental results with various parameters demonstrate that proposed scheme leads to extract more efficient features than other methods.

scholarly journals A BERT-based Hybrid Short Text Classification Model Incorporating CNN and Attention-based BiGRU

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Feature Fusion ◽  
Feature Representation ◽  
Classification Model ◽  
Short Text ◽  
Learning Techniques ◽  
Proposed Model ◽  
Fusion Framework

Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. In recent years, deep learning techniques are applied to text classification and has made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper propose a feature fusion framework based on the Bidirectional Encoder Representations from Transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

Sign in / Sign up

Export Citation Format

Share Document