Toward Using Machine Learning-Based Motion Gesture for 3D Text Input

Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8993 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3947-3951

Author(s):

R. Vineeth ◽

R. Rithish ◽

D. V. S. N. Sai Varma ◽

B. V. Ajay Prakash

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Health Professionals ◽

Disease Diagnosis ◽

Artificial Intelligence Techniques ◽

Text Input ◽

Smart Health ◽

User Friendly ◽

Present World

In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.

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Automated Classification of Unstructured Bilingual Software Bug Reports: An Industrial Case Study Research

Applied Sciences ◽

10.3390/app12010338 ◽

2021 ◽

Vol 12 (1) ◽

pp. 338

Author(s):

Ömer Köksal ◽

Bedir Tekinerdogan

Keyword(s):

Machine Learning ◽

Industrial Case Study ◽

Software Bugs ◽

Text Input ◽

Bug Reports ◽

Bug Report ◽

Software Bug ◽

Manual Classification

Software bug report classification is a critical process to understand the nature, implications, and causes of software failures. Furthermore, classification enables a fast and appropriate reaction to software bugs. However, for large-scale projects, one must deal with a broad set of bugs from multiple types. In this context, manually classifying bugs becomes cumbersome and time-consuming. Although several studies have addressed automated bug classification using machine learning techniques, they have mainly focused on academic case studies, open-source software, and unilingual text input. This paper presents our automated bug classification approach applied and validated in an industrial case study. In contrast to earlier studies, our study is applied to a commercial software system based on unstructured bilingual bug reports written in English and Turkish. The presented approach adopts and integrates machine learning (ML), text mining, and natural language processing (NLP) techniques to support the classification of software bugs. The approach has been applied within an industrial case study. Compared to manual classification, our results show that bug classification can be automated and even performs better than manual bug classification. Our study shows that the presented approach and the corresponding tools effectively reduce the manual classification time and effort.

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Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.2379 ◽

2021 ◽

Vol 12 (2) ◽

pp. 3216-3223

Author(s):

Siji George C G, Et. al.

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Experimental Result ◽

Text Input

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

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An Approach for Generating Pattern-Based Shorthand Using Speech-to-Text Conversion and Machine Learning

Journal of Intelligent Systems ◽

10.1515/jisys-2013-0039 ◽

2013 ◽

Vol 22 (3) ◽

pp. 229-240

Author(s):

K. R. Abhinand ◽

H. K. Anasuya Devi

Keyword(s):

Machine Learning ◽

Speech Recognition ◽

Knowledge Representation ◽

Natural Language ◽

Unique Sequence ◽

Speech Input ◽

Text Input ◽

Input Signals ◽

New Knowledge ◽

Textual Form

AbstractRapid handwriting, popularly known as shorthand, involves writing symbols and abbreviations in lieu of common words or phrases. This method increases the speed of transcription and is primarily used to record oral dictation. Someone skilled in shorthand will be able to write as fast as the dictation occurs, and these patterns are later transliterated into actual, natural language words. A new kind of rapid handwriting scheme is proposed, called the Pattern-Based Shorthand. A word on a keyboard involves pressing a unique sequence of keys in a particular order. This sequence forms a pattern that defines the word. Such a pattern forms the shorthand for that word. Speech recognition involves identifying, by a machine, the words spoken by a speaker. These spoken words form speech input signals to a computer that is equipped to correctly recognize the words and do further action, such as convert it to text. From this text input, unique shorthand patterns are generated by the system. The system employs machine learning to improve its performance with experience, by creating a dictionary of mappings from word to patterns in such a way that the access to existing patterns is faster with progression. This forms a new knowledge representation schema that reduces the redundancy in the storage of words and the length of information content. In conclusion, the speech is converted into textual form and then reconstructed into Pattern-Based Shorthand.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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