scholarly journals Multimodal Emotion Recognition Using the Symmetric S-ELM-LUPI Paradigm

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 487
Author(s):  
Lingzhi Yang ◽  
Xiaojuan Ban ◽  
Michele Mukeshimana ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Information Source ◽  
Privileged Information ◽  
Learning Methods ◽  
Multimodal Emotion Recognition ◽  
Learning Machine ◽  
Learning Using Privileged Information

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable.

Improvement in bug localization based on kernel extreme learning machine

2016 ◽  
Vol 5 ◽  
pp. 1
Author(s):  
Marzie Rahmati ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Extreme Learning Machine ◽  
Bug Localization ◽  
Learning Methods ◽  
Bug Reports ◽  
Bug Report ◽  
Mozilla Firefox ◽  
Learning Machine ◽  
Information Retrieval Methods

Bug localization uses bug reports received from users, developers and testers to locate buggy files. Since finding a buggy file among thousands of files is time consuming and tedious for developers, various methods based on information retrieval is suggested to automate this process. In addition to information retrieval methods for error localization, machine learning methods are used too. Machine learning-based approach, improves methods of describing bug report and program code by representing them in feature vectors. Learning hypothesis on Extreme Learning Machine (ELM) has been recently effective in many areas. This paper shows effectiveness of none-linear kernel of ELM in bug localization. Furthermore the effectiveness of Different kernels in ELM compare to other kernel-based learning methods is analyzed. The experimental results for hypothesis evaluation on Mozilla Firefox dataset show effectiveness of Kernel ELM for bug localization in software projects.

scholarly journals Recognition of Imbalanced Epileptic EEG Signals by a Graph-Based Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Zhou ◽  
Xiongtao Zhang ◽  
Zhibin Jiang
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Learning Methods ◽  
Imbalanced Classification ◽  
Machine Learning Methods ◽  
Learning Machine

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

scholarly journals FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 6551-6557
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Bench Mark ◽  
Hardware Platform ◽  
Learning Speed ◽  
Field Programmable ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Elm Classifier ◽  
Short Event

To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.

scholarly journals An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

2020 ◽  
Vol 14 ◽  
Author(s):  
Yaqing Zhang ◽  
Jinling Chen ◽  
Jen Hong Tan ◽  
Yuxuan Chen ◽  
Yunyi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Real Life ◽  
Learning Rate ◽  
Learning Models ◽  
Learning Methods ◽  
Time Frequency ◽  
Comparison Results

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

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