Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for speech disabled individuals, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems were found to hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

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Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Sensors ◽

10.3390/s19081911 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1911 ◽

Cited By ~ 7

Author(s):

Yasmin Elsahar ◽

Sijung Hu ◽

Kaddour Bouazza-Marouf ◽

David Kerr ◽

Annysa Mansor

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Deep Learning ◽

Natural Language Processing ◽

Language Processing ◽

Augmentative And Alternative Communication ◽

High Tech ◽

Alternative Communication ◽

Computer Interfaces ◽

Ease Of Access

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

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Leveraging Natural Language Processing Applications Using Machine Learning

Handbook of Research on Emerging Trends and Applications of Machine Learning - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9643-1.ch016 ◽

2020 ◽

pp. 338-360

Author(s):

Janjanam Prabhudas ◽

C. H. Pradeep Reddy

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Text Summarization ◽

Feature Representation ◽

Learning Models ◽

Primary Focus ◽

And Performance

The enormous increase of information along with the computational abilities of machines created innovative applications in natural language processing by invoking machine learning models. This chapter will project the trends of natural language processing by employing machine learning and its models in the context of text summarization. This chapter is organized to make the researcher understand technical perspectives regarding feature representation and their models to consider before applying on language-oriented tasks. Further, the present chapter revises the details of primary models of deep learning, its applications, and performance in the context of language processing. The primary focus of this chapter is to illustrate the technical research findings and gaps of text summarization based on deep learning along with state-of-the-art deep learning models for TS.

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Application of Natural Language Processing on Character Computing : A Short Review

10.31224/osf.io/sprxj ◽

2021 ◽

Author(s):

KOUSHIK DEB

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Personality Trait ◽

Prediction Models ◽

Short Review ◽

Learning Techniques ◽

Trait Prediction

Character Computing consists of not only personality trait recognition, but also correlation among these traits. Tons of research has been conducted in this area. Various factors like demographics, sentiment, gender, LIWC, and others have been taken into account in order to understand human personality. In this paper, we have concentrated on the factors that could be obtained from available data using Natural Language Processing. It has been observed that the most successful personality trait prediction models are highly dependent on NLP techniques. Researchers across the globe have used different kinds of machine learning and deep learning techniques to automate this process. Different combinations of factors lead the research in different directions. We have presented a comparative study among those experiments and tried to derive a direction for future development.

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Deep Learning Approaches for Textual Sentiment Analysis

Handbook of Research on Emerging Trends and Applications of Machine Learning - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9643-1.ch009 ◽

2020 ◽

pp. 171-182 ◽

Cited By ~ 1

Author(s):

Tamanna Sharma ◽

Anu Bajaj ◽

Om Prakash Sangwan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Machine Learning Techniques ◽

Computational Technique ◽

Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

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Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

Computational Economics ◽

10.1007/s10614-021-10145-2 ◽

2021 ◽

Author(s):

Faisal Khalil ◽

Gordon Pipa

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Short Term Memory ◽

High Tech ◽

Long Term Memory ◽

Term Memory ◽

Hourly Data ◽

Analytic Models

AbstractThis study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.

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Neural Language Modeling for Molecule Generation

10.26434/chemrxiv.14700831 ◽

2021 ◽

Author(s):

Sanjar Adilov

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Natural Language Processing ◽

Drug Design ◽

Natural Language ◽

Language Processing ◽

De Novo ◽

Language Modeling ◽

Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

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Neural Language Modeling for Molecule Generation

10.26434/chemrxiv.14700831.v1 ◽

2021 ◽

Author(s):

Sanjar Adilov

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Natural Language Processing ◽

Drug Design ◽

Natural Language ◽

Language Processing ◽

De Novo ◽

Language Modeling ◽

Machine Learning Methods

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Malicious Encryption Traffic Detection Based on NLP

Security and Communication Networks ◽

10.1155/2021/9960822 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hao Yang ◽

Qin He ◽

Zhenyan Liu ◽

Qian Zhang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Detection Method ◽

Data Preprocessing ◽

Encrypted Traffic ◽

Traffic Detection

The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.

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