Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

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.

Recently, satisfactory performance has been achieved on sentiment analysis tasks by using recurrent neural network (RNN). As a derived model of RNN, gated recurrent units (GRU) model has a great advantage in dealing with long text sequence problems, while the performance is limited when working with short texts. Therefore, to effectively extract the contextual information of network comments, and take advantage of convolutional neural network (CNN) that can capture the local features, we propose a GCNN (gated convolutional neural network) model based on GRU and CNN. In this paper, we compare the proposed GCNN model with CNN and RNN models on standard datasets. Experiment results show that the GCNN model achieves state-of-the-art performance on the sentiment analysis tasks, with 94.9% and 90.0% F1-score on hotel comments (ChnSentiCorp) corpus and the IMDB corpus respectively.

Recent experience in applying recurrent neural networks (RNNs) to interpreting Permannt Downhole Gauge (PDG) records has highlighted the potential utility of machine learning algorithms to learn reservoir behavior from data. The power of the RNN resides in its ability to retain information in a form of memory of previous patterns and information contained in the previous behavior of phenomena being modeled. This memory plays a role of informing the decision at the present time by using what happened in the past. This property suggests RNNs as a suitable choice to model sequences of reservoir information, even when the reservoir modeler is faced with incomplete knowledge of the underlying physical system. Convolutional Neural Networks (CNNs) are another variant of machine learning algorithms that have shown promise in sequence modeling domains, such as audio synthesis and machine translation. In this study, RNN and CNN were applied to tasks that traditionally would be modeled by a reservoir simulator. This was achieved by formulating the relationship between physical quantities of interest from subsurface reservoirs as a sequence mapping problem. In addition, the performance of a CNN layer as compared to RNN was evaluated systematically to investigate their capabilities in a variety of tasks of interest to the reservoir engineer. Preliminary results suggest that CNNs, with specific design modifications, are as capable as RNNs in modeling sequences of information and as reliable when making inferences to cases that has not been seen by the algorithm during training. Design details and reasons pertaining to the way these two seemingly different architectures process information, and handle memory are also discussed.

A hybrid model based on neural networks for biomedical relation extraction

Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

Deep learning methodologies have had a major impact on performance across a wide variety of machine learning tasks, and speech recognition is no exception. We describe a set of deep learning techniques that proved to be particularly successful in achieving performance gains in word error rate on a popular large vocabulary conversational speech recognition benchmark task (“Switchboard”). We found that the best performance is achieved by combining features from both recurrent and convolutional neural networks. We compare two recurrent architectures: partially unfolded nets with max-out activations and bidirectional long short-term memory nets. In addition, inspired by the success of convolutional networks for image classification, we designed a convolutional net with many convolutional layers and small kernels that create a receptive field with more nonlinearity and fewer parameters than standard configurations. When combined, these neural networks achieve a word error rate of 6.2% on this difficult task; this was the best reported rate at the time of this writing and is even more remarkable given that human performance itself is estimated to be 4% on this data.

The Sixth International Symposium on Neural Networks (ISNN 2009)

In recent years, convolutional neural networks have been widely used in image processing and have shown good results. Particularly useful was their ability to automatically extract image features (textures and shapes of objects). The article proposes a method that improves the accuracy and speed of recognition of an ultra-precise neural network based on image recognition of people’s faces. At first, a recurrent neural network is introduced into the convolutional neural network, thereby studying the characteristics of the image more deeply. Deep image characteristics are studied in parallel using a convolutional and recurrent neural network. In line with the idea of skipping the ResNet convolution layer, a new ShortCut3- ResNet residual module is built. A double optimization model is created to fully optimize the convolution process. A study of the influence of various parameters of a convolutional neural network on network performance is demonstrated, also analyzed using simulation experiments. As a result, the optimal parameters of the convolutional neural network are established. Ex- periments show that the method presented in this paper can study various images of people’s faces regardless of age, gender, and also improves the accuracy of feature extraction and image recognition ability.

проведено исследованию методов оценки состояния канала связи (CSI) в ортогональном частотно-разделённом мультиплексировании (OFDM), применяемом в современных стандартах беспроводной связи, таких как LTE, Wi-Fi и 5G. Рассматриваются традиционные методы оценки канала, такие как Least Squares (LS) и Minimum Mean Square Error (MMSE), отмечаются их недостатки в сложных условиях, связанных с многолучевым распространением сигнала и высокой мобильностью абонентов. Рассмотрено перспективное направление оценки канала связи — глубокое обучение (DL). Доказано, что применение нейронных сетей позволяет существенно повысить точность оценки канала. Приводятся примеры исследований, использующих свёрточные нейронные сети (CNN) и другие архитектуры нейронных сетей для улучшения качества оценки CSI. Отмечено, что ряд аспектов применения нейронных сетей требует дальнейшего изучения, включая оптимизацию моделей под изменяющиеся условия среды и снижение вычислительной сложности. Предлагаются новые подходы для повышения эффективности оценки канала, среди которых использование рекуррентных нейронных сетей (RNN) и методов сжатия моделей (квантование и прунинг). Результаты экспериментов продемонстрировали потенциал использования RNN для эффективной оценки канала связи и восстановления сигналов в OFDM-системах. Моделирование подтвердило преимущество рекуррентных нейронных сетей над CNN в плане потребления вычислительных ресурсов, при сохранении сопоставимой производительности. Описана важность внедрения глубоких нейронных сетей для совершенствования технологии OFDM и предложены направления дальнейших исследований и разработок we conducted a study of methods for assessing the state of a communication channel (CSI) in orthogonal frequency-divided multiplexing (OFDM), used in modern wireless communication standards such as LTE, Wi-Fi and 5G. We considered traditional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE), and their disadvantages are noted in difficult conditions associated with multipath signal propagation and high subscriber mobility. We considered a promising area of communication channel assessment, deep learning (DL). We proved that the use of neural networks can significantly improve the accuracy of channel estimation. We give examples of studies using convolutional neural networks (CNNs) and other neural network architectures to improve the quality of CSI assessment. We note that a number of aspects of the use of neural networks require further study, including optimizing models for changing environmental conditions and reducing computational complexity. We proposed new approaches to improve the efficiency of channel estimation, including the use of recurrent neural networks (RNNs) and model compression methods (quantization and pruning). The simulation confirmed the advantage of recurrent neural networks over CNNs in terms of computing resource consumption, while maintaining comparable performance. The experimental results demonstrated the potential of using RNN for effective assessment of communication quality and signal recovery in OFDM systems. We proposed the importance of the introduction of deep neural networks for the improvement of OFDM technology is described and the directions of further research and development

Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).

Aspect based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose main goal is to identify the sentiment polarity of an aspect in a sentence. A sentence may contain many different aspects, each of which may have different sentiment polarities. Based on the current researches in this area, ABSA can be divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). In the past, more commonly used method is to adopt the time serial algorithm such as Long Short-Term Memory (LSTM) or Recurrent Neural Network (RNN), which usually needs more training time and has complex structures. Moreover, many previous models lack the abilities to effectively learn the internal structure features of sentences. For ABSA, sometimes the sentence structure may significantly affect the final classification results. However, we found the excellent performance of self-attention algorithm and gating mechanism in some other related researches. Therefore, to solve the problems above, we build a new model based on gating mechanism, combined with convolutional neural networks (CNN) and self-attention mechanism. First, we use self-attention to extract the structural feature of the input, and integrate it with the features of the original sentence extracted by CNN. On such basis, we further combine the aspect-category or aspect-term of the input sentence to form the final sentiment feature. Experiments on SemEval datasets show the performance of our models and the effectiveness of the model is proved.

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.

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.