Natural language processing and deep learning chatbot using long short term memory algorithm

Kasthuri, E.; Balaji, S.

doi:10.1016/j.matpr.2021.04.154

Cited by 32 publications

(17 citation statements)

References 5 publications

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“…LSTM has been successful in a variety of biological and chemical fields, including chemical–protein relation extraction, chemical substance classification, and drug molecular design . Additionally, it has been used in fields like imaging, , speech recognition, , NLP, and more. However, when the data set is small or has few labeled samples, using LSTM may not always yield desired results.…”

Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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“…As for increasing the agent's response, a Deep Learning method has been widely used [27]- [29]. However, the study about increasing user motivation and engagement while using a chatbot as a learning medium is still limited.…”

Section: B Acceptance Of Chatbot As Learning Mediamentioning

confidence: 99%

Gamification on Chatbot-Based Learning Media: a Review and Challenges

Hidayatulloh

Pambudi

Surjono

et al. 2021

ELINVO

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The mobile learning sector has exploded, implying that the e-Learning trend is shifting to mobile platforms. As a result, chatbots have become increasingly popular alternatives for online learning and examinations on mobile platforms. However, it did not provide enough motivation for the student. On the other hand, gamification in a typical e-Learning platform is a widely used technique for increasing students' learning motivation. Therefore, combining gamification with chatbot-based learning and examinations possibly offer benefits, including increase student learning motivation. This study explored the possibilities and future challenges of the development of gamification within chatbot-based learning media. We discussed four aspects: architecture's reliability, security and privacy issue, user’s acceptance and motivation, and gamification feature challenges.

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“…Long short-term memory (LSTM) is one of the deep learning algorithms that can be used for classification, prediction, and control [8][9][10][11]. It can learn complex patterns and relationships in the input data, making it a valuable tool for a wide range of tasks in various fields such as finance, healthcare, and natural language processing [12][13][14]. It also has the capacity to discern audio signals based on their anticipated properties, which is a very dependable feature for audio classification.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and acoustic approach for mechanical failure detection in industrial machinery

Qurthobi,

Maskeliūnas

2023

J. Phys.: Conf. Ser.

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Research into acoustic signal-based failure detection has developed into a subject that has attracted the attention of many researchers in recent years. Acoustic signal data collection can be performed without having to interrupt or stop the operation of the machine to be inspected. Therefore, it is very beneficial for the development of nondestructive testing and predictive maintenance. In this study, a collection of pump sound recordings that are part of the Malfunctioning Industrial Machine Investigation and Inspection dataset, known as the MIMII dataset, is used as test material. Several deep learning algorithms such as long short-term memory (LSTM), gate recurrent unit (GRU), autoencoder, and convolutional neural network (CNN) were involved and compared to determine their ability to detect failures. Based on the training results with 300 epochs and a learning rate of 10−6 it was found that CNN produced the classification with the highest accuracy compared to the other algorithms. In addition, the CNN algorithm is also capable of performing classification amidst the problem of imbalance in the amount of data.

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Natural language processing and deep learning chatbot using long short term memory algorithm

Cited by 32 publications

References 5 publications

Machine Learning Methods for Small Data Challenges in Molecular Science

Machine Learning Methods for Small Data Challenges in Molecular Science

Gamification on Chatbot-Based Learning Media: a Review and Challenges

Deep learning and acoustic approach for mechanical failure detection in industrial machinery

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