A Semantic-Aware Strategy for Automatic Speech Recognition Incorporating Deep Learning Models

Santhanavijayan, A.; Kumar, D. Naresh; Deepak, Gerard

doi:10.1007/978-981-15-5400-1_25

Cited by 36 publications

(7 citation statements)

References 12 publications

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“…The results are divided into four parts; the first two parts present and explain results for both the top and lowest contributing EU countries, without using Brexit variables, and the second and last section gives the findings of the sentiment analysis, incorporating the Brexit event. The evaluation metrics used for both of these experiments are the mean absolute error (MAE) and root mean squared error (RMSE) [54,55,75,76].…”

Section: Resultsmentioning

confidence: 99%

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Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

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Section: Resultsmentioning

confidence: 99%

“…Deep learning models are used to achieve better results in different application areas such as medical imaging [73,74], biometric systems [75], as well as natural language-based tasks [76]. In addition, as compared to linear regression or support vector regression models, they perform better.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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“…Recent advances in machine learning (ML) and data science have resulted in its extensive application in various fields. Artificial intelligence (AI) and other advanced ML approaches have significantly improved state-of-the-art outcomes in computer vision, speech recognition [ 22 ], drug discovery, genomics, and, most recently, physical layer communication [ 23 ]. MC algorithms [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] focused on various ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

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“…In recent years, deep learning has its own strong learning ability and has been widely applied in image recognition [10,11], speech recognition [12,13], natural language processing [14,15], and other fields [16]. Deep learning is good at mining and learning the deep features of multisource heterogeneous data.…”

Section: Introductionmentioning

confidence: 99%

Network Course Recommendation System Based on Double-Layer Attention Mechanism

Zhu

2021

Scientific Programming

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In view of the lack of accurate recommendation and selection of courses on the network teaching platform in the new form of higher education, a network course recommendation system based on the double-layer attention mechanism is proposed. First of all, the collected data are preprocessed, while the data of students and course information are normalized and classified. Then, the dual attention mechanism is introduced into the parallel neural network recommendation model so as to improve the model’s ability to mine important features. TF-IDF (term frequency-inverse document frequency) based on the student score and course category is improved. The recommendation results are classified according to the weight of course categories, so as to construct different types of course groups and complete the recommendation. The experimental results show that the proposed algorithm can effectively improve the model recommendation accuracy compared with other algorithms.

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A Semantic-Aware Strategy for Automatic Speech Recognition Incorporating Deep Learning Models

Cited by 36 publications

References 12 publications

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Network Course Recommendation System Based on Double-Layer Attention Mechanism

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