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.

scholarly journals Competitor Identification by Use The Sentiment Classification Based on the User Research

2019 ◽  
Vol 8 (9S2) ◽  
pp. 871-874
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
The Other ◽  
Machine Learning Techniques ◽  
Single Product ◽  
User Research ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Wide Range

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

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 BnVec: Towards the Development of Word Embedding for Bangla Language Processing

2021 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
Md. Kowsher ◽  
Md. Jashim Uddin ◽  
Anik Tahabilder ◽  
Nusrat Jahan Prottasha ◽  
Mahid Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Parameter Tuning ◽  
Research Community ◽  
Word Embedding ◽  
The Other

Progression in machine learning and statistical inference are facilitating the advancement of domains like computer vision, natural language processing (NLP), automation & robotics, and so on. Among the different persuasive improvements in NLP, word embedding is one of the most used and revolutionary techniques. In this paper, we manifest an open-source library for Bangla word extraction systems named BnVec which expects to furnish the Bangla NLP research community by the utilization of some incredible word embedding techniques. The BnVec is splitted up into two parts, the first one is the Bangla suitable defined class to embed words with access to the six most popular word embedding schemes (CountVectorizer, TF-IDF, Hash Vectorizer, Word2vec, fastText, and Glove). The other one is based on the pre-trained distributed word embedding system of Word2vec, fastText, and GloVe. The pre-trained models have been built by collecting content from the newspaper, social media, and Bangla wiki articles. The total number of tokens used to build the models exceeds 395,289,960. The paper additionally depicts the performance of these models by various hyper-parameter tuning and then analyzes the results.

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 Sentiment analysis of tweets through Altmetrics: A machine learning approach

2020 ◽  
pp. 016555152093091
Author(s):  
Saeed-Ul Hassan ◽  
Aneela Saleem ◽  
Saira Hanif Soroya ◽  
Iqra Safder ◽  
Sehrish Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Naive Bayes ◽  
The Other ◽  
Support Vector

The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.

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).

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

Sign in / Sign up

Export Citation Format

Share Document