Comparative Analysis of Convolutional Neural Network and LSTM in Text-Based Sentiment Classification

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.

Sentiment analysis is a subfield of Natural Language Processing (NLP) which tries to process a text to extract opinions or attitudes towards topics or entities. Recently, the use of deep learning methods for sentiment analysis has received noticeable attention from researchers. Generally, different deep learning methods have shown superb performance in sentiment analysis problem. However, deep learning models are different in nature and have different strengths and limitations. For example, convolutional neural networks are useful for extracting local structures from data, while recurrent models are able to learn order dependence in sequential data. In order to combine the advantages of different deep models, in this paper we have proposed a novel approach for aspect-based sentiment analysis which utilizes deep ensemble learning. In the proposed method, we first build four deep learning models, namely CNN, LSTM, BiLSTM and GRU. Then the outputs of these models are combined using stacking ensemble approach where we have used logistic regression as meta-learner. The results of applying the proposed method on the real datasets show that our method has increased the accuracy of aspect-based prediction by 5% to 20% compared to the basic deep learning methods.

Intelligent colour analysis based on deep learning has a wide range of applications in image, video, and other colour information automatic analysis and understanding and has great potential. Deep learning models can learn the representation of color features through the process of feature abstraction and hierarchical extraction and feature learning to express colors, as well as pattern recognition that is suitable for the given task. They have stronger feature representation, learning, and generalization abilities. These characteristics allow deep learning models to better deal with the problems incurred by traditional methods in the processing, analysis, and understanding of colour and its features. The author mainly uses Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to analyse and process color data through the designed experimental system, respectively. The learning process of deep learning model is fully automatic, and the representation and abstraction ability of the model is improved through the large-scale annotated data. It can be used in the field of image/video recognition and understanding of color by deep learning method. The experimental results are shown in this paper. The application of deep learning-based color intelligent analysis in different tasks has achieved remarkable results. A deep learning model can accurately analyse the color and color texture of the image and its distribution in the image, and be used in the image recognition and classification task.

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.

Most existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in this under‐resourced language. We develop an open‐source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. The objectives of this work are bi‐fold (a) the creation of a human‐annotated corpus for the research of sentiment analysis in Urdu; and (b) measurement of up‐to‐date model performance using a corpus. For their assessment, we performed binary and ternary classification studies utilizing another model, namely long short‐term memory (LSTM), recurrent convolutional neural network (RCNN) Rule‐Based, N‐gram, support vector machine , convolutional neural network, and LSTM. The RCNN model surpasses standard models with 84.98% accuracy for binary classification and 68.56% accuracy for ternary classification. To facilitate other researchers working in the same domain, we have open‐sourced the corpus and code developed for this research.

Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs

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.

Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter

Chapter 25 - Techniques and applications of deep learning in smart agriculture systems

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

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.

Deep learning such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP). Previous research shows that RNN and CNN achieved significant results in extracting long and short-term dependencies. However, there is a lack of research on Persian/Dari due to low resources and with many difficulties in preprocessing. In this research paper, we proposed a novel sentiment analysis model using bidirectional GRU and CNN to address the challenges and improve sentiment knowledge in the Persian/Dari language. Firstly, a preprocessor is utilized to enhance data quality by eliminating noise. Secondly, a bidirectional Gated Recurrent Unit is used to capture long-term dependencies. Thirdly, our model(named PersiSentNet) employs CNN with max pooling to extract contextual features and reduce feature dimensionality. Gaussian Noise and Dropout are applied as regularization techniques to avoid the overfitting problem. Finally, the model’s performance is verified on two standard datasets, and the experimental results indicate that our model significantly outperforms the state-of-the-art models, highlighting its robustness and effectiveness in addressing the challenges of Persian/Dari language sentiment analysis.

Wearable biosensors and artificial intelligence (AI) are radically improving healthcare by facilitating continuous, non-intrusive, real-time monitoring of physiological and biochemical signals. Wearable biosensors are small, personal devices that can be worn on the body and gather metrics including heart rate, body temperature, blood glucose, and respiratory rate. Wearable biosensors lay the foundation for proactive, personalized healthcare. Chronic disease rates are on the rise, populations are aging across the globe, and there is need for smart systems that support remote care. Smart monitoring systems that utilize biosensors are being adopted for these reasons, especially AI-based monitoring. AI allows for data generated by biosensors to be processed, and used reliably. Machine learning and deep learning models are particularly useful in processing biosensor data. Example machine learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Deep learning methods are able to remove background noise, feature extraction, data interpretation, and anomaly detection, along with retrospective analysis to predict health events including arrhythmia, stress episodes, hypoglycemia. AI and ML algorithms can use biosensor data for on-time feedback and decision support to clinicians with high amounts of specificity and sensitivity and low connectivity latency. Using Internet of Medical Things (IoMT) based infrastructure, wearable biosensors can securely transmit data privately to discrete clinical or edge cloud providers, to facilitate either clinician oversight of patients in real-time as they do their monitoring, or clinicians being forewarned to support early intervention when monitoring for acute patient events. While these systems can be considered game-changing advancements which include many advantages, there still exists hurdles to adopting systems to improve healthcare. There must be work done to address issues with data privacy, algorithmic biases, device interoperability, and sensor accuracy. Compliance with some privacy laws, such as HIPAA and GDPR is needed and the use of ethical and transparent AI models is necessary. Technical issues that must be addressed more generally include motion-induced signal noise, battery lifespan, and hardware-software integration. In conclusion, wearable biosensors empowered by AI may represent a new frontier in personalized, real-time health monitoring that enables early recognition of health issues, remote supervision, and chronic disease monitoring while alleviating the need for traditional healthcare capacity. To realize the full potential of this functionality, subsequent research should strive to improve data security, model transparency, and system scalability, and to keep patients as the focus of innovation.

In the past decades, sentiment analysis has become one of the most active research areas in natural language processing, data mining, web mining, and information retrieval. The great demand in everyday life and the factor of novelty coupled with the availability of data from social networks have served as strong motivation for research on sentiment-analysis. A number of technical problems, most of which had not been attempted before, either in the NLP or linguistics communities have also generated strong research interests in academia. Sentiment analysis, also called opin-ion mining, is the field of study that analyzes people’s opinions, sentiments, apprais-als, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. It improves not only the field of natural language processing but also management, political science, economics, and sociology because all these areas are related to the thoughts of consumers and public. User-generated content is full of opinions, because the main reason why people post messages on social media platforms is to express their views and opinions, and therefore sentiment analysis is at the centre of social media analysis. It turned out that user messages often contain plenty of sarcastic expressions and ambiguous words. Within one opinion both positive and negative sentiments can be present. This also applies to negative particles, which do not always indicate a negative tone. This article investigates four challenges faced by researchers while conducting sentiment analysis, namely: sarcasm, negation, word ambiguity, and multipolarity. These aspects significantly affect the accuracy of the results when we determine a sentiment. Modern approaches to solving the problem are also covered. These are mainly machine learning methods, such as convolutional neural networks (CNN), deep neural networks (DNN), long short-term memory (LTSM), recurrent neural network (RNN), support vector machines (SVM), etc.

Sentiment analysis is a sub-field of natural language processing (NLP). In sentiment analysis the sentiment behind the piece of data is tried to know, this data can be a review of a product by a customer or a comment on some social media platform. Analysing large amounts of data is still an easy task for small retail websites and business owners. Deep learning (DL) has made a great revolution in the field of speech and image recognition. Mature deep learning neural network i.e. convolution neural network (CNN) has completely changed the field of NLP. This paper proposed a high accuracy, efficient, scalable, reliable and secure solution to cater all the needs of business owners and institutes for sentiment analysis with DL model, a browser based GUI interface for easy accessibility to all the non-technical folks and a dashboard having graphical representations of their results. The proposed sentiment analysis based model has achieved 93.55% accuracy which has outperformed other models.