Do Deep Networks Really Need Complex Modules for Multilingual Sentiment Polarity Detection and Domain Classification?

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

In this paper, alternative direction finding methods have been proposed using deep learning techniques. Firstly, Regeression and Classification models have created by using Convolutional Neural Networks (CNNs). In the second Convolutional Neural Networks and Recurrent Neural Networks (RNNs) have been utilized in the proposed methods. Despite having vast amount of direction finding studies, utilization of neural networks is scarce in literature and past works mostly only includes usage of CNNs. In this study, direction finding is performed by learning signals reaching multiple antenna arrays by networks. Created neural networks have been fed with different data formats and their performances against noisy and no-noise data have been shown. In addition, comparative analysis of the developed models were made in the similar Signal-to-Noise Ratio (SNR) range with the subspace based MUSIC algorithm, which is frequently used in direction finding.

Feature data processing: Making medical data fit deep neural networks

The natural language processing (NLP) area has been magically transformed by the Deep Neural Network (DNN). The two variations of Neural Networks, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) can handle the different NLP tasks effectively. CNN is based on feature extraction using N-grams at higher levels. The RNN can be effectively used to model the sequential information. Choosing a neural technique for the various NLP tasks is always a challenge. The paper focuses on the evaluating the performance of the two DNN techniques CNN and RNN for the various NLP tasks, so that appropriate technique can be selected.

This paper focuses on the design and comparison of different deep neural networks for the real-time prediction of locomotor intentions by using data from inertial measurement units. The deep neural network architectures are convolutional neural networks, recurrent neural networks, and convolutional recurrent neural networks. The input to the architectures are features in the time domain, which have been derived either from one inertial measurement unit placed on the upper right leg of ten healthy subjects, or two inertial measurement units placed on both the upper and lower right leg of ten healthy subjects. The study shows that a WaveNet, i.e., a full convolutional neural network, achieves a peak F1-score of 87.17% in the case of one IMU, and a peak of 97.88% in the case of two IMUs, with a 5-fold cross-validation.

This paper presents a distributed modeling approach of deep neural network (DNN) classifier for segmentation-free Telugu word recognition. The DNN is a hybrid neural network model. It involves the convolutional neural network (CNN) and recurrent neural network (RNN) layers. Moreover, the connectionist temporal classification (CTC) layer gives the output. The presence of CNN layers requires advanced computer systems consisting of graphics processing units (GPUs) for speedy training of a DNN model. The distributed modeling approach involves the distribution of load across a TPU. As a comparative study, we also train the same model on CPU and GPU systems. Our method aims to handle large batch sizes and reduce modeling time, which is precious to researchers. The word error rate (WER) of the test set is 5%, which is promising.KeywordsCNNCTCDistributed ModelingDNNGPULSTMRNNTelugu OCRWord Recognition

This paper studies the problem of Chinese license plate recognition (CLPR) in complex natural scenes. Aiming at the problem that the license plate is difficult to be segmented due to its tilt and distortion caused by the process of license plate photographing, an improved Convolutional Recurrent Neural Network (CRNN) deep neural network model for license plate recognition was proposed, which combined with Deep Convolutional Neural Networks (DCNN), Recurrent Neural Networks (RNN), Spatial Transformer Networks (STN) and Connectionist Temporal Classification (CTC) model. The recognition process does not need to be segmented, which avoids the error caused by plate segmentation and affects the recognition accuracy. The convolutional layers selects DenseNet, the latest CNN network architecture, to improve the feature extraction accuracy. The space conversion layers adds STN to affine the license plate, which improves the recognition accuracy. In order to evaluate the performance, Chinese City Parking Dataset (CCPD) is used to test, and the validity of the method is proved.

A hybrid model based on neural networks for biomedical relation extraction

This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

Maxout neurons for deep convolutional and LSTM neural networks in speech recognition

Target-based aspect level sentiment analysis (TBASA) seeks to discover the polarity of the text towards certain aspect terms in each text. Most of the recent studies utilize deep learning (DL) frameworks like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to predict the influences of multiple contextual aspects on sentiment polarity. Both CNN and RNN are successfully used earlier to create complicated semantic representations. However, existing approaches fail to capture the sequence information due to the high dimensionality. In this paper, a typical approach called a Deep Sentient network with a novel inter-mutual attention mechanism is proposed to tackle this issue. The proposed model adds the sequence information identified with RNN into CNN to consistently anticipate the polarity. It also learns the contextual and target terms sequentially to understand the mutual impact between the features. Furthermore, both Part-of-Speech (POS) and position information are also included in the input layer as background knowledge. Finally, a series of experiments are performed on various benchmark datasets to verify the efficacy of our proposed approach.

A floating foundation vibration reduction system with an air spring as a vibration isolation element has been widely used in the foundation of large ultra-precision instruments. With the excellent vibration isolation performance of the air spring, its complex dynamic nonlinear behavior is always a research difficulty. In this paper, a dynamic model of a floating foundation vibration reduction system based on the restoring force of air spring is derived. The structure of the recurrent convolution neural network (RCNN) is proposed based on combining the working characteristics of a convolution neural network and a long short-term memory neural network, and the dynamic model of a floating foundation vibration reduction system is established with the restoring force of the air spring calculated by the RCNN as the input. Finally, a test experiment was designed to compare the dynamic characteristics of the traditional numerical model and three deep neural network models in the floating foundation. The results show that convolution neural network, long short-term memory, and RCNN models could predict the vibration response of floating foundation vibration reduction system, and the RCNN model had better performance for a floating foundation.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

In this digital era, there is an exponential growth of text-based content in the electronic world. Data as texts exist in the form of documents, social media posts on Facebook, Twitter, etc., logs, sensor data, and emails. Twitter is a social platform where users express their views on various aspects in a day to day life. Twitter produces over 500 million tweets daily that is 6000 tweets per second. Twitter data is, by definition, very noisy and unstructured in nature. Text classifications based on the machine learning techniques have problems like poor generalization ability and sparsity dimension explosion. Classifiers based on deep learning techniques are implemented to improve accuracy to overcome shortcomings of machine learning techniques and to avoid feature extraction processes and have high prediction accuracy and strong learning ability. In this work, the classification of tweets is performed on Covid-19 dataset by implementing deep learning techniques namely Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Recurrent Convolution Neural Network (RCNN), Recurrent Neural Network with Long Short Term Memory (RNN+LSTM), and Bidirectional Long Short Term Memory with Attention (BI-LSTM + Attention). The algorithms are implemented using two-word embedding techniques namely Global Vectors for Word Representation (GloVe) and Word2Vec. RNN with Bidirectional LSTM model has performed better than all the classifiers considered. It has classified the text with an accuracy of 93% and above when used with GloVe and Word2Vec.