VOICE BASED PRESCRIPTION USING MACHINE LEARNING

2021 ◽  
Vol 1 (10) ◽  
pp. 283-287
Author(s):  
Harsh Goyal ◽  
Piyush Piyush ◽  
Ravinder Ravinder ◽  
Pooja Gupta
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Natural Language Processing ◽  
Side Effects ◽  
Language Processing ◽  
Text Processing ◽  
Health Record ◽  
Electronic Prescription ◽  
Voice Input ◽  
The World

Medicine side effects are the major problem in the world, due to wrong prescriptions thousands of people die every year. Most of these mistakes are due to illegible handwriting which leads to taking the wrong medicine or dosage. To solve this issue, a voice-based prescription came into the picture where the prescription is taken as voice input, and a pdf file is generated which is then emailed to the patient. This method can save wealth and life throughout the world, particularly in developing countries where the prescriptions are generally paper-based. The system proposed in this paper is for those doctors and hospitals that are still using a paper-based handwritten prescription. Keywords: Healthcare, Voice-based, Python, Natural Language Processing (NLP), Electronic Prescription, Text Processing, Electronic Health Record (EHR).

Machine Learning in Natural Language Processing

2010 ◽  
pp. 302-324
Author(s):  
Marina Sokolova ◽  
Stan Szpakowicz
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Part Of Speech ◽  
Applications Of Machine Learning

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.

scholarly journals Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

2019 ◽  
Vol 48 (3) ◽  
pp. 432-445 ◽  
Author(s):  
Laszlo Toth ◽  
Laszlo Vidacs
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Software Systems ◽  
Functional Requirements ◽  
Natural Languages ◽  
System Analyst

Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

Emotional Tweets Analysis on Social Media with Short Text Classification Using Various Machine Learning Techniques

2020 ◽  
Vol 17 (12) ◽  
pp. 5477-5482
Author(s):  
Shaik Rahamat Basha ◽  
M. Surya Bhupal Rao ◽  
P. Kiran Kumar Reddy ◽  
G. Ravi Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Text Processing ◽  
Human Life ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Short Text ◽  
Online Social Media ◽  
The World

Online Social media are a huge source of regular communication since most people in the world today use these services to stay communicating with each other in their modern lives. Today’s research has been implemented on emotion recognition by message. The majority of the research uses a method of machine learning. In order to extract information from the textual text written by human beings, natural language processing (NLP) techniques were used. The emotion of humans may be expressed when reading or writing a message. Human beings are willing, since human life is filled with a variety of emotions, to feel various emotions. This analysis helps us to realize the use of text processing and text mining methods by social media researchers in order to classify key data themes. Our experiments presented that the two main social networks in the world are conducting text-based mining on Facebook and Twitter. In this proposed study, we categorized the human feelings such as joy, fear, love, anger, surprise, sadness and thankfulness and compared our results using various methods of machine learning.

scholarly journals Food Recommendation using Classifier and Modified Apriori Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 3967-3970
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Food Analysis ◽  
Apriori Algorithm ◽  
Computer Technologies ◽  
The World

In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.

scholarly journals Latest Advances of Natural Language Processing and their Applications in Everyday life

2020 ◽  
Vol 6 (10) ◽  
pp. 31-35
Author(s):  
Sai Sri Nandan Challapalli Shalini Jaiswal and Preeti Singh Bahadur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Constraint Satisfaction ◽  
Large Scale ◽  
The World ◽  
Dramatic Shift

Natural language processing (NLP) area of Artificial Intelligence (AI) has offered the scope to apply and integrate various other traditional AI fields. While the world was working on comparatively simpler aspects like constraint satisfaction and logical reasoning, the last decade saw a dramatic shift in the research. Now large-scale applications of statistical methods, such as machine learning and data mining are in the limelight. At the same time, the integration of this understanding with Computer Vision, a tech that deals with obtaining information from visual data through cameras will pave way to bring the AI enabled devices closer to a layman also. This paper gives an overview of implementation and trend analysis of such technology in Sales and ServiceSectors.

scholarly journals Analyzing The Strength Between Mission And Vision Statements And Industry Via Machine Learning

2020 ◽  
Vol 36 (3) ◽  
pp. 121-128
Author(s):  
Faleh Alshameri ◽  
Nathan Green Green
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Predictive Power ◽  
Text Processing ◽  
Machine Learning Techniques ◽  
Vision Statements ◽  
Learning Techniques ◽  
Potential Customers

Mission and vision statements are critical to a company’s success both from a company’s long-term goals and appearance to potential customers. We analyze a collection of 772 mission and vision statements from companies via natural language processing. This data is hand annotated into 15 industry types. We show the distinctiveness and connectiveness of each industry via text processing and machine learning techniques. The extracted features of each industry are a telling and guiding indicator of what that industry embraces. We show high predictive power via machine learning to determine an industry by looking only at the mission and vision statements

scholarly journals Review on Robotic Machine to Solve the Matrix with Natural Language Processing and Image Processing

2021 ◽  
pp. 395-402
Author(s):  
Prof. Sonali Zunke ◽  
Sandesh Ukey ◽  
Dipak Mendhe
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Natural Language Processing ◽  
Language Processing ◽  
Human Life ◽  
The Other ◽  
Entertainment Education ◽  
Artificial Intelligent ◽  
The World ◽  
The Matrix

As the world is moving faster, humans are not taking a step back to make the world more better place, may it be by enhanced technology or extreme creativity. We know that a human life now is full of technology and every little thing is just a click away that is the favour of automation for everything. If we explore Automation more, the major concepts which will still the spotlight are Artificial Intelligent (AI), Machine Learning (ML) and the list will continue. The other concept which steals the limelight and make every automation possible is the programming language we use to make the features we are now using possible. The language which is exponentially gaining fame is Python. If all the above mentioned technology is combined together, we get most of the automation possible today. At every corner of the world, this technology is being used to make things lively and possible. Not only companies but every industry (food, entertainment, education, manufacturers and many more) are relying on this technology. The calculation which plays a significant role in almost every bit is also being automated to make the things very simpler and quicker. Even though, we have calculator but it still cannot solve the problems which are needed to be solve in some platforms. In this paper, we shall be discussing about a similar concept.

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