Pub Date : 2022-05-26DOI: 10.46610/rrmlcc.2022.v01i02.002
P. A. Grana, Vinod S Agrawal
The technique of determining whether a text is good, negative or neutral is known as sentiment analysis (SA).Sentiment Analysis can be identified by many names like Textual Analysis, Opinion Mining. Sentiment Analysis is a branch of Natural Language Processing (NLP) that focuses on the expression of subjective views and feelings about a topic gathered from multiple sources. Sentiment Analysis is a collection of methods for detecting and extracting opinions and uses them for the benefit of business operation. It is a classification algorithm aimed at finding opinions and decision-making point of view. Sentiment Analysis is performed in many ways, Automatic classification approach involves Nave Bayes (NB), Support Vector Machine (SVM), and Linear Regression is examples of supervised machine learning methods (LR). The data is explored using unsupervised machine learning. Recurrent Neural Network (RNN) derivatives are also used for classification. Rule-based approach involves various NLP process for classification.
{"title":"Evaluation of Sentiment Analysis of Text Using Rule-Based and Automatic Approach","authors":"P. A. Grana, Vinod S Agrawal","doi":"10.46610/rrmlcc.2022.v01i02.002","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i02.002","url":null,"abstract":"The technique of determining whether a text is good, negative or neutral is known as sentiment analysis (SA).Sentiment Analysis can be identified by many names like Textual Analysis, Opinion Mining. Sentiment Analysis is a branch of Natural Language Processing (NLP) that focuses on the expression of subjective views and feelings about a topic gathered from multiple sources. Sentiment Analysis is a collection of methods for detecting and extracting opinions and uses them for the benefit of business operation. It is a classification algorithm aimed at finding opinions and decision-making point of view. Sentiment Analysis is performed in many ways, Automatic classification approach involves Nave Bayes (NB), Support Vector Machine (SVM), and Linear Regression is examples of supervised machine learning methods (LR). The data is explored using unsupervised machine learning. Recurrent Neural Network (RNN) derivatives are also used for classification. Rule-based approach involves various NLP process for classification.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121685480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-26DOI: 10.46610/rrmlcc.2022.v01i02.003
Sanchit Shahi, Rishabh Gautam Shahi, M.Anil Kumar
Today's corporate world focuses not only on the set of skills of the potential employees, but also on their respective personality. Personality helps you succeed in both your professional and personal life. Therefore, recruiters need to be aware of an individual's personality trait. While the number of job seekers is increasing exponentially, the number of positions is declining, making it difficult to manually add the best candidate for the right position to the candidate list by looking at your resume. This article explores a variety of machine learning approaches to efficiently predict the personality by the usage of Natural language processing (NLP) technology. The results showcase that Random-forest achieves higher accuracy than several other algorithms i.e. KNN, SVM and Naive Bayes. This system can be used in many business areas / areas that may require professional candidates. This system reduces the workload of the department (general workers, employment, and training and dismissal department).
{"title":"Machine Learning Empowered Personality Predication System Encompassing","authors":"Sanchit Shahi, Rishabh Gautam Shahi, M.Anil Kumar","doi":"10.46610/rrmlcc.2022.v01i02.003","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i02.003","url":null,"abstract":"Today's corporate world focuses not only on the set of skills of the potential employees, but also on their respective personality. Personality helps you succeed in both your professional and personal life. Therefore, recruiters need to be aware of an individual's personality trait. While the number of job seekers is increasing exponentially, the number of positions is declining, making it difficult to manually add the best candidate for the right position to the candidate list by looking at your resume. This article explores a variety of machine learning approaches to efficiently predict the personality by the usage of Natural language processing (NLP) technology. The results showcase that Random-forest achieves higher accuracy than several other algorithms i.e. KNN, SVM and Naive Bayes. This system can be used in many business areas / areas that may require professional candidates. This system reduces the workload of the department (general workers, employment, and training and dismissal department).","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115746353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-18DOI: 10.46610/rrmlcc.2022.v01i02.001
Swathi Bhat D, S. M, Poojita Reddy Yatakunta, Prathiksha S Naik, Prathima Bhat
Long-term renal damage is a critical issue that has to be addressed using healthcare analytics. It is a kind of kidney disease where the kidney's functionality will be degraded over months or years. Hence, accurate prediction needs to be done so that patients can undergo proper treatment at the right time. The machine learning techniques help to accomplish this. The proposed research will examine the effectiveness of supervised or guided classification algorithms such as Naive Bayesian and K-Nearest Neighbor in predicting the disorders on the basis of accuracy. A web application will be implemented that helps doctors and patients identify the disease and undergo medication with a proper diet plan.
{"title":"Chronic Kidney Disease Prediction Using Naïve Bayesian Classifier and K-NN Machine-Learning Algorithms","authors":"Swathi Bhat D, S. M, Poojita Reddy Yatakunta, Prathiksha S Naik, Prathima Bhat","doi":"10.46610/rrmlcc.2022.v01i02.001","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i02.001","url":null,"abstract":"Long-term renal damage is a critical issue that has to be addressed using healthcare analytics. It is a kind of kidney disease where the kidney's functionality will be degraded over months or years. Hence, accurate prediction needs to be done so that patients can undergo proper treatment at the right time. The machine learning techniques help to accomplish this. The proposed research will examine the effectiveness of supervised or guided classification algorithms such as Naive Bayesian and K-Nearest Neighbor in predicting the disorders on the basis of accuracy. A web application will be implemented that helps doctors and patients identify the disease and undergo medication with a proper diet plan.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126776402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.46610/rrmlcc.2022.v01i01.002
S. Sree, M. S
In today's environment, people are treated as equals to machines. So book lovers didn't have time to read their favorite novels, and even if they did, they couldn't read them manually, couldn't keep track of what they'd read, or remember what they'd read. In addition, not all books are available in the market at the time of need. People are unable to convey their thoughts and ideas on the book. So, using our app, we can keep track of the books we've read and also share our opinions on them. Users can provide comments or feedback on the book, making it easier for other users to choose a book based on the feedback and remarks. All the books published will be available so no need to search in markets for hours. A text reader option will be a feature in it.
{"title":"Online Automated Library for Reading Books","authors":"S. Sree, M. S","doi":"10.46610/rrmlcc.2022.v01i01.002","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i01.002","url":null,"abstract":"In today's environment, people are treated as equals to machines. So book lovers didn't have time to read their favorite novels, and even if they did, they couldn't read them manually, couldn't keep track of what they'd read, or remember what they'd read. In addition, not all books are available in the market at the time of need. People are unable to convey their thoughts and ideas on the book. So, using our app, we can keep track of the books we've read and also share our opinions on them. Users can provide comments or feedback on the book, making it easier for other users to choose a book based on the feedback and remarks. All the books published will be available so no need to search in markets for hours. A text reader option will be a feature in it.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116588548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.46610/rrmlcc.2022.v01i01.001
M. S, Sharmikha Sree R, K. Valarmathi
AI use information mining and computational knowledge calculations to further develop dynamic models. Market Basket Analysis is one of the main affiliation rule learning is an information mining strategy, Consists of examining the much of the time bought thing in the market container of clients. In the existing system is use a Apriority algorithm is used for finding frequent item sets. However, it takes longer to locate frequently used item sets because it must repeatedly scan the database, which is a time-consuming procedure. The proposed method was created to address the shortcomings of the existing approach. The ECLAT algorithm is utilized to separate successive item sets from the data set, and afterward the affiliation rules are made.
{"title":"Market Basket Analysis for Designing a Product Placement Layout in Retail Shop","authors":"M. S, Sharmikha Sree R, K. Valarmathi","doi":"10.46610/rrmlcc.2022.v01i01.001","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i01.001","url":null,"abstract":"AI use information mining and computational knowledge calculations to further develop dynamic models. Market Basket Analysis is one of the main affiliation rule learning is an information mining strategy, Consists of examining the much of the time bought thing in the market container of clients. In the existing system is use a Apriority algorithm is used for finding frequent item sets. However, it takes longer to locate frequently used item sets because it must repeatedly scan the database, which is a time-consuming procedure. The proposed method was created to address the shortcomings of the existing approach. The ECLAT algorithm is utilized to separate successive item sets from the data set, and afterward the affiliation rules are made.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-22DOI: 10.46610/rrmlcc.2023.v02i01.002
Barkha Bhardwaj, Shivam Tiwari
This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper starts with a brief introduction to machine learning and its various algorithms. It then presents various applications of machine learning in agriculture, including crop yield prediction, precision agriculture, plant disease detection, and soil moisture prediction. The paper highlights the advantages of using machine learning in agriculture, including increased efficiency, reduced costs, and improved decision-making. It also discusses the challenges faced in the application of machine learning in agriculture, including the need for large amounts of data and the difficulty in collecting high-quality data in remote and rural areas. Finally, the paper concludes with future directions for research and the potential impact of machine learning on the agriculture industry. The review shows that machine learning has the potential to revolutionize the way we approach agriculture and food production, leading to a more sustainable and efficient future for the industry.
{"title":"Exploring the Potential of Machine Learning in Agriculture: A Review of its Applications and Results","authors":"Barkha Bhardwaj, Shivam Tiwari","doi":"10.46610/rrmlcc.2023.v02i01.002","DOIUrl":"https://doi.org/10.46610/rrmlcc.2023.v02i01.002","url":null,"abstract":"This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper starts with a brief introduction to machine learning and its various algorithms. It then presents various applications of machine learning in agriculture, including crop yield prediction, precision agriculture, plant disease detection, and soil moisture prediction. The paper highlights the advantages of using machine learning in agriculture, including increased efficiency, reduced costs, and improved decision-making. It also discusses the challenges faced in the application of machine learning in agriculture, including the need for large amounts of data and the difficulty in collecting high-quality data in remote and rural areas. Finally, the paper concludes with future directions for research and the potential impact of machine learning on the agriculture industry. The review shows that machine learning has the potential to revolutionize the way we approach agriculture and food production, leading to a more sustainable and efficient future for the industry.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121656972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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